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cswilkin/example-model

cswilkin

--- license: mit ---

CodeHima/Tos-Roberta

CodeHima

transformers

text-classification

--- license: mit language: - en widget: - text: "You have the right to use CommunityConnect for its intended purpose of connecting with others, sharing content responsibly, and engaging in constructive dialogue. You are responsible for the content you post and must respect the rights and privacy of others." example_title: "Fair Clause" - text: " We reserve the right to suspend, terminate, or restrict your access to the platform at any time and for any reason, without prior notice or explanation. This includes but is not limited to violations of our community guidelines or terms of service, as determined solely by ConnectWorld." example_title: "Unfair Clause" metrics: - accuracy - precision - f1 - recall library_name: transformers pipeline_tag: text-classification --- # Tos-Roberta: Terms of Service Fairness Classifier ## Model Description Tos-Roberta is a fine-tuned RoBERTa-large model designed to classify clauses in Terms of Service (ToS) documents based on their fairness level. The model categorizes clauses into three classes: clearly fair, potentially unfair, and clearly unfair. ### Task The model performs multi-class classification on individual sentences or clauses, categorizing them into three levels of unfairness: 0. Clearly Fair 1. Potentially Unfair 2. Clearly Unfair ## Key Features - Based on the RoBERTa-large architecture - Fine-tuned on a specialized dataset of ToS clauses - Achieves high accuracy in distinguishing between fair and unfair clauses - Suitable for legal text analysis and consumer rights applications ## Performance The model demonstrates strong performance on the task of ToS clause classification: - Validation Accuracy: 89.64% - Test Accuracy: 85.84% Detailed performance metrics per epoch: | Epoch | Training Loss | Validation Loss | Accuracy | F1 Score | Precision | Recall | |-------|---------------|-----------------|----------|----------|-----------|--------| | 1 | 0.443500 | 0.398950 | 0.874699 | 0.858838 | 0.862516 | 0.874699 | | 2 | 0.416400 | 0.438409 | 0.853012 | 0.847317 | 0.849916 | 0.853012 | | 3 | 0.227700 | 0.505879 | 0.896386 | 0.893325 | 0.891521 | 0.896386 | | 4 | 0.052600 | 0.667532 | 0.891566 | 0.893167 | 0.895115 | 0.891566 | | 5 | 0.124200 | 0.747090 | 0.884337 | 0.887412 | 0.891807 | 0.884337 | ## Training Details - **Base Model**: RoBERTa-large - **Dataset**: CodeHima/TOS_DatasetV2 - **Training Time**: 3310.09 seconds - **Epochs**: 5 - **Batch Size**: 8 - **Learning Rate**: Started at 2e-5 with a warmup period and decay - **Optimizer**: AdamW - **Loss Function**: Cross-Entropy - **Training Strategy**: Mixed precision training (FP16) ## Usage To use this model for inference: ```python from transformers import RobertaTokenizer, RobertaForSequenceClassification import torch # Load model and tokenizer model = RobertaForSequenceClassification.from_pretrained("YourHuggingFaceUsername/Tos-Roberta") tokenizer = RobertaTokenizer.from_pretrained("YourHuggingFaceUsername/Tos-Roberta") # Prepare input text text = "Your Terms of Service clause here" inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) # Make prediction with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=-1) predicted_class = torch.argmax(probabilities, dim=-1).item() # Map prediction to label label_map = {0: "clearly_fair", 1: "potentially_unfair", 2: "clearly_unfair"} predicted_label = label_map[predicted_class] print(f"Predicted class: {predicted_label}") print(f"Probabilities: {probabilities[0].tolist()}") ``` ## Limitations and Bias - The model's performance may vary depending on the legal jurisdiction and specific domain of the ToS. - It may not capture nuanced legal interpretations that require human expertise. - The training data may contain biases present in existing ToS documents. ## Ethical Considerations While this model can assist in identifying potentially unfair clauses in ToS documents, it should not be used as a substitute for professional legal advice. The model's predictions should be reviewed by qualified legal professionals before making any decisions based on its output. ## Citation If you use this model in your research or application, please cite it as: ``` @misc{Tos-Roberta, author = {Himanshu Mohanty}, title = {Tos-Roberta: RoBERTa-large model for Terms of Service Fairness Classification}, year = {2024}, publisher = {HuggingFace}, journal = {CodeHima/Tos-Roberta}, howpublished = {\url{https://huggingface.co/CodeHima/Tos-Roberta}} } ``` ## Contact For questions, feedback, or collaborations, please open an issue on the model's Hugging Face repository or contact [Your Contact Information].

abbasmahmudiai/text_classification_bert

abbasmahmudiai

transformers

text-classification

--- library_name: transformers license: apache-2.0 datasets: - SeyedAli/Persian-Text-Emotion language: - fa --- # Model Card for Model ID bertmodel=HooshvareLab/bert-base-parsbert-uncased dataset=SeyedAli/Persian-Text-Emotion epoch=6 Training Loss =0.000300

ferrazzipietro/Llama-2-7b-chat-hfspecialTkn_en.layer1_NoQuant_64_32_0.02_8

ferrazzipietro

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

movenb3at/PJS

movenb3at

Entry not found

Lam-Hung/controlnet_depth_interior

Lam-Hung

Entry not found

HachiML/Mists-7B-v01-single-turn

HachiML

transformers

feature-extraction

--- base_model: HachiML/Mists-7B-v01-projector-trained license: apache-2.0 tags: - trl - sft - generated_from_trainer model-index: - name: Mists-7B-v01-single-turn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/siseikatu8/huggingface/runs/aun0jon1) # Mists-7B-v01-single-turn This model is a fine-tuned version of [HachiML/Mists-7B-v01-projector-trained](https://huggingface.co/HachiML/Mists-7B-v01-projector-trained) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4228 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.6859 | 0.0420 | 400 | 1.1048 | | 0.7572 | 0.0841 | 800 | 0.8318 | | 0.664 | 0.1261 | 1200 | 0.7295 | | 0.6135 | 0.1682 | 1600 | 0.6526 | | 0.5707 | 0.2102 | 2000 | 0.6007 | | 0.5506 | 0.2523 | 2400 | 0.5653 | | 0.5255 | 0.2943 | 2800 | 0.5434 | | 0.5106 | 0.3363 | 3200 | 0.5219 | | 0.4909 | 0.3784 | 3600 | 0.5045 | | 0.4773 | 0.4204 | 4000 | 0.4874 | | 0.4664 | 0.4625 | 4400 | 0.4762 | | 0.4555 | 0.5045 | 4800 | 0.4663 | | 0.4516 | 0.5466 | 5200 | 0.4560 | | 0.4466 | 0.5886 | 5600 | 0.4490 | | 0.4403 | 0.6306 | 6000 | 0.4433 | | 0.4323 | 0.6727 | 6400 | 0.4383 | | 0.4337 | 0.7147 | 6800 | 0.4324 | | 0.4214 | 0.7568 | 7200 | 0.4297 | | 0.4153 | 0.7988 | 7600 | 0.4269 | | 0.414 | 0.8409 | 8000 | 0.4250 | | 0.4187 | 0.8829 | 8400 | 0.4238 | | 0.418 | 0.9250 | 8800 | 0.4230 | | 0.4126 | 0.9670 | 9200 | 0.4228 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.1 - Datasets 2.20.0 - Tokenizers 0.19.1

styalai/XT-unknowM-v0.1

styalai

transformers

--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q4_0-GGUF

NikolayKozloff

transformers

text-generation

--- base_model: Iker/Llama-3-Instruct-Neurona-8b-v2 datasets: - Danielbrdz/Barcenas-Economia - HiTZ/casimedicos-exp - somosnlp/coser_resumenes - csebuetnlp/CrossSum - Iker/Document-Translation-en-es - somosnlp/es-inclusive-language-it - glaiveai/glaive-code-assistant-v3 - glaiveai/glaive-function-calling-v2 - Iker/InstructTranslation-EN-ES - somosnlp/lenguaje-claro-dataset - somosnlp/LingComp_QA - Iker/NoticIA - teknium/OpenHermes-2.5 - Iker/OpenHermes-2.5-Spanish - Helsinki-NLP/opus-100 - projecte-aina/RAG_Multilingual - HiTZ/This-is-not-a-dataset - Iker/Reddit-Post-Translation - wikipedia language: - es - en library_name: transformers license: llama3 pipeline_tag: text-generation tags: - synthetic - llama-cpp - gguf-my-repo --- # NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q4_0-GGUF This model was converted to GGUF format from [`Iker/Llama-3-Instruct-Neurona-8b-v2`](https://huggingface.co/Iker/Llama-3-Instruct-Neurona-8b-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Iker/Llama-3-Instruct-Neurona-8b-v2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q4_0-GGUF --hf-file llama-3-instruct-neurona-8b-v2-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q4_0-GGUF --hf-file llama-3-instruct-neurona-8b-v2-q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q4_0-GGUF --hf-file llama-3-instruct-neurona-8b-v2-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q4_0-GGUF --hf-file llama-3-instruct-neurona-8b-v2-q4_0.gguf -c 2048 ```

Kimata/FinanceLlama

Kimata

transformers

text-generation

Entry not found

DanielTB/priestmodelv1

DanielTB

transformers

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** DanielTB - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

sharathprasaath/PY007-TinyLlama-1.1B-Chat-v0.3

sharathprasaath

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Bonnie422/mistral-7b-mj-finetune

Bonnie422

transformers

text-generation

--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: filipealmeida/Mistral-7B-Instruct-v0.1-sharded widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

NikolayKozloff/Carrot-Ko-2.1B-Instruct-Q8_0-GGUF

NikolayKozloff

text-generation

--- base_model: CarrotAI/Carrot-Ko-2.1B-Instruct datasets: - CarrotAI/ko-instruction-dataset language: - ko license: mit pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/Carrot-Ko-2.1B-Instruct-Q8_0-GGUF This model was converted to GGUF format from [`CarrotAI/Carrot-Ko-2.1B-Instruct`](https://huggingface.co/CarrotAI/Carrot-Ko-2.1B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/CarrotAI/Carrot-Ko-2.1B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo NikolayKozloff/Carrot-Ko-2.1B-Instruct-Q8_0-GGUF --hf-file carrot-ko-2.1b-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/Carrot-Ko-2.1B-Instruct-Q8_0-GGUF --hf-file carrot-ko-2.1b-instruct-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo NikolayKozloff/Carrot-Ko-2.1B-Instruct-Q8_0-GGUF --hf-file carrot-ko-2.1b-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo NikolayKozloff/Carrot-Ko-2.1B-Instruct-Q8_0-GGUF --hf-file carrot-ko-2.1b-instruct-q8_0.gguf -c 2048 ```

debiao29/Qwen-Qwen1.5-0.5B-1719935143

debiao29

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

alixnaveed/WADU_v1

alixnaveed

sentence-transformers

sentence-similarity

--- base_model: intfloat/multilingual-e5-base datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:514 - loss:MultipleNegativesRankingLoss widget: - source_sentence: ' لوگوں نے ان سے پوچھا اور میں اس وقت سہل کے اتنا قریب تھا کہ میرے اور ان کے درمیان کوئی دوسرا حائل نہ تھا کہ رسول اللہ کے احد کے زخم کا علاج کس دوا سے کیا گیا تھا انہوں نے کہا کہ اس بات کا جاننے والا اب مجھ سے زیادہ کوئی نہیں رہا علی رضی اللہ عنہ اپنی ڈھال میں پانی لاتے اور حضرت فاطمہ رضی اللہ عنہا آپ کے منہ سے خون دھوتیں پھر ایک بوریا کا ٹکڑا جلایا گیا اور آپ کے زخم میں بھر دیا گیا ' sentences: - کیا غسل کے دوران وضو کے ارکان کو ترجیح دینی چاہیے - رسول اللہ ﷺ کے احد کے زخم کا علاج کس طرح کیا گیا تھا - جب سپاہی نے غسل کی ضرورت کے بارے میں پوچھا تو رسول اللہ ﷺ کا رد عمل کیا تھا - source_sentence: ' رسول کریم نے حجۃ الوداع میں جب سر کے بال منڈوائے تو سب سے پہلے ابوطلحہ رضی اللہ عنہ نے آپ کے بال لیے تھے ' sentences: - حجۃ الوداع میں رسول کریم ﷺ کے بال کون سب سے پہلے لیے تھے - تیمم میں کن اعضا کو مسح کرنا کافی ہے - طبیعی حالات میں مسجد کے بجائے دوسرے مقامات پر نماز پڑھنے کا کیا حکم ہے - source_sentence: 'رسول اللہ نے فرمایا كہ جب تم میں سے كوئی وضو كرے تو اسے چاہیے كہ اپنی ناک میں پانی دے پھر اسے صاف كرے اور جو شخص پتھروں سے استنجاء كرے اسے چاہیے كہ بے جوڑ عدد یعنی ایک یا تین سے استنجاء كرے اور جب تم میں سے كوئی سو كر اٹھے تو وضو كے پانی میں ہاتھ ڈالنے سے پہلے اسے دھو لے كیونكہ تم میں سے كوئی نہیں جانتا كہ رات كو اس كا ہاتھ كہاں رہا ہے ' sentences: - نبی اکرم ﷺ غسل فتح مکہ کے دن کیوں فرما رہے تھے - استنجاء کے لیے پتھر استعمال کرتے وقت کس تعداد میں استعمال کرنے کا حکم ہے - رسول اللہ ﷺ کی بیویاں رات میں کہاں قضاء حاجت کے لیے جاتی تھیں - source_sentence: ' گویا کہ میں آنحضرت کی مانگ میں خوشبو کی چمک دیکھ رہی ہوں اس حال میں کہ آپ احرام باندھے ہوئے ہیں ' sentences: - کیا جنابت کی حالت میں وضو کر کے سو سکتے ہیں - کیا احرام باندھتے وقت خوشبو لگانے کی ممانعت ہے - کیا کپڑوں میں دھبے ہونے کے باوجود نماز ادا کرنا جائز ہے - source_sentence: ' نبی کریم جب جنابت کی حالت میں ہوتے اور سونے کا ارادہ کرتے تو شرمگاہ کو دھو لیتے اور نماز کی طرح وضو کرتے ' sentences: - کیا احرام کی حالت میں خوشبو لگانا جائز ہے - کیا رفع حاجت کے بعد صفائی کے لیے پتھروں کا استعمال جائز ہے - کیا جنابت کی حالت میں شرمگاہ کو دھونا ضروری ہے --- # SentenceTransformer based on intfloat/multilingual-e5-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("alixnaveed/WADU_v1") # Run inference sentences = [ ' نبی کریم جب جنابت کی حالت میں ہوتے اور سونے کا ارادہ کرتے تو شرمگاہ کو دھو لیتے اور نماز کی طرح وضو کرتے ', 'کیا جنابت کی حالت میں شرمگاہ کو دھونا ضروری ہے', 'کیا رفع حاجت کے بعد صفائی کے لیے پتھروں کا استعمال جائز ہے', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 514 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 14 tokens</li><li>mean: 94.86 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.88 tokens</li><li>max: 31 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | <code> ایک دن میں اپنے گھر کی چھت پر چڑھا تو مجھے رسول اللہ دو اینٹوں پر قضاء حاجت کے وقت بیت المقدس کی طرف منہ کیے ہوئے نظر آئے </code> | <code>کیا قضاء حاجت کے وقت بیت المقدس کی طرف منہ کرنا جائز ہے</code> | | <code> رسول کریم میری مزاج پرسی کے لیے تشریف لائے میں بیمار تھا ایسا کہ مجھے ہوش تک نہیں تھا آپ نے وضو کیا اور اپنے وضو کا پانی مجھ پر چھڑکا تو مجھے ہوش آ گیا میں نے عرض کیا یا رسول اللہ میرا وارث کون ہو گا میرا تو صرف ایک کلالہ وارث ہے اس پر آیت میراث نازل ہوئی </code> | <code>رسول اللہ ﷺ نے بیمار صحابی پر کیا چھڑکا</code> | | <code> نبی کریم اور آپ کی کوئی زوجہ مطہرہ ایک برتن میں غسل کرتے تھے اس حدیث میں مسلم بن ابراہیم اور وہب بن جریر کی روایت میں شعبہ سے من الجنابة کا لفظ زیادہ ہے یعنی یہ جنابت کا غسل ہوتا تھا </code> | <code>کیا نبی کریم ﷺ اور آپ کی زوجہ مطہرہ ایک برتن میں جنابت کا غسل کر سکتے تھے</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 5.8140 | 500 | 0.0563 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.3 - PyTorch: 2.3.0+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

mutisya/whisper-large-v3-luo-drL-24_5-v24_23_3

mutisya

Entry not found

ANDER0312/test

ANDER0312

Entry not found

NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q5_0-GGUF

NikolayKozloff

transformers

text-generation

--- base_model: Iker/Llama-3-Instruct-Neurona-8b-v2 datasets: - Danielbrdz/Barcenas-Economia - HiTZ/casimedicos-exp - somosnlp/coser_resumenes - csebuetnlp/CrossSum - Iker/Document-Translation-en-es - somosnlp/es-inclusive-language-it - glaiveai/glaive-code-assistant-v3 - glaiveai/glaive-function-calling-v2 - Iker/InstructTranslation-EN-ES - somosnlp/lenguaje-claro-dataset - somosnlp/LingComp_QA - Iker/NoticIA - teknium/OpenHermes-2.5 - Iker/OpenHermes-2.5-Spanish - Helsinki-NLP/opus-100 - projecte-aina/RAG_Multilingual - HiTZ/This-is-not-a-dataset - Iker/Reddit-Post-Translation - wikipedia language: - es - en library_name: transformers license: llama3 pipeline_tag: text-generation tags: - synthetic - llama-cpp - gguf-my-repo --- # NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q5_0-GGUF This model was converted to GGUF format from [`Iker/Llama-3-Instruct-Neurona-8b-v2`](https://huggingface.co/Iker/Llama-3-Instruct-Neurona-8b-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Iker/Llama-3-Instruct-Neurona-8b-v2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q5_0-GGUF --hf-file llama-3-instruct-neurona-8b-v2-q5_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q5_0-GGUF --hf-file llama-3-instruct-neurona-8b-v2-q5_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q5_0-GGUF --hf-file llama-3-instruct-neurona-8b-v2-q5_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-Q5_0-GGUF --hf-file llama-3-instruct-neurona-8b-v2-q5_0.gguf -c 2048 ```

AmberYifan/sft-safe-spin-v

AmberYifan

transformers

text-generation

--- license: apache-2.0 base_model: AmberYifan/zephyr-7b-sft-safe tags: - generated_from_trainer model-index: - name: sft-safe-spin-v results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sft-safe-spin-v This model is a fine-tuned version of [AmberYifan/zephyr-7b-sft-safe](https://huggingface.co/AmberYifan/zephyr-7b-sft-safe) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1338 - Rewards/real: -3.4589 - Rewards/generated: -13.0205 - Rewards/accuracies: 0.9522 - Rewards/margins: 9.5616 - Logps/generated: -186.5033 - Logps/real: -173.6707 - Logits/generated: -3.1233 - Logits/real: -3.5104 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real | |:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:| | 0.4603 | 0.06 | 100 | 0.2849 | 0.3170 | -4.8525 | 0.9697 | 5.1696 | -104.8239 | -135.9118 | -4.1358 | -4.3181 | | 0.2425 | 0.12 | 200 | 0.2396 | -1.7168 | -15.7686 | 0.8997 | 14.0518 | -213.9848 | -156.2504 | -3.7662 | -3.9307 | | 0.1942 | 0.17 | 300 | 0.1536 | -2.2725 | -23.7294 | 0.9395 | 21.4568 | -293.5923 | -161.8076 | -3.6024 | -3.7832 | | 0.1872 | 0.23 | 400 | 0.1591 | -1.9811 | -26.8335 | 0.9443 | 24.8523 | -324.6334 | -158.8936 | -3.7387 | -3.9242 | | 0.5386 | 0.29 | 500 | 0.2124 | -2.1951 | -12.1230 | 0.9260 | 9.9280 | -177.5292 | -161.0329 | -3.5142 | -3.8361 | | 0.1318 | 0.35 | 600 | 0.1397 | -2.3554 | -19.3040 | 0.9570 | 16.9486 | -249.3387 | -162.6357 | -3.3916 | -3.8319 | | 0.1311 | 0.41 | 700 | 0.1589 | -2.6398 | -20.7908 | 0.9363 | 18.1510 | -264.2064 | -165.4799 | -3.2845 | -3.7517 | | 0.121 | 0.47 | 800 | 0.1282 | -2.8500 | -22.0061 | 0.9546 | 19.1561 | -276.3602 | -167.5825 | -3.1277 | -3.6228 | | 0.1115 | 0.52 | 900 | 0.1392 | -3.3374 | -22.9391 | 0.9419 | 19.6017 | -285.6900 | -172.4560 | -3.0962 | -3.6427 | | 2.648 | 0.58 | 1000 | 0.1711 | -3.2299 | -10.5445 | 0.9411 | 7.3146 | -161.7435 | -171.3813 | -3.4082 | -3.6724 | | 0.1078 | 0.64 | 1100 | 0.1340 | -2.8961 | -11.3713 | 0.9498 | 8.4752 | -170.0120 | -168.0435 | -3.3383 | -3.6687 | | 0.0751 | 0.7 | 1200 | 0.1293 | -2.8024 | -11.6866 | 0.9522 | 8.8842 | -173.1649 | -167.1059 | -3.2816 | -3.6419 | | 0.0927 | 0.76 | 1300 | 0.1276 | -3.0019 | -12.3015 | 0.9514 | 9.2996 | -179.3133 | -169.1012 | -3.2296 | -3.6103 | | 0.0963 | 0.81 | 1400 | 0.1256 | -2.9332 | -12.3140 | 0.9546 | 9.3809 | -179.4392 | -168.4139 | -3.2433 | -3.6265 | | 0.1122 | 0.87 | 1500 | 0.1280 | -3.3660 | -12.7449 | 0.9546 | 9.3789 | -183.7474 | -172.7419 | -3.1923 | -3.5761 | | 0.092 | 0.93 | 1600 | 0.1407 | -3.5290 | -13.0250 | 0.9459 | 9.4960 | -186.5488 | -174.3717 | -3.1057 | -3.4905 | | 0.0876 | 0.99 | 1700 | 0.1338 | -3.4589 | -13.0205 | 0.9522 | 9.5616 | -186.5033 | -173.6707 | -3.1233 | -3.5104 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

Blackmoonbear/DRL-HuggingFace-Unit1

Blackmoonbear

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 269.44 +/- 15.76 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Jayantez/q-FrozenLake-v1-4x4-noSlippery

Jayantez

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Jayantez/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-IQ4_NL-GGUF

NikolayKozloff

transformers

text-generation

--- base_model: Iker/Llama-3-Instruct-Neurona-8b-v2 datasets: - Danielbrdz/Barcenas-Economia - HiTZ/casimedicos-exp - somosnlp/coser_resumenes - csebuetnlp/CrossSum - Iker/Document-Translation-en-es - somosnlp/es-inclusive-language-it - glaiveai/glaive-code-assistant-v3 - glaiveai/glaive-function-calling-v2 - Iker/InstructTranslation-EN-ES - somosnlp/lenguaje-claro-dataset - somosnlp/LingComp_QA - Iker/NoticIA - teknium/OpenHermes-2.5 - Iker/OpenHermes-2.5-Spanish - Helsinki-NLP/opus-100 - projecte-aina/RAG_Multilingual - HiTZ/This-is-not-a-dataset - Iker/Reddit-Post-Translation - wikipedia language: - es - en library_name: transformers license: llama3 pipeline_tag: text-generation tags: - synthetic - llama-cpp - gguf-my-repo --- # NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-IQ4_NL-GGUF This model was converted to GGUF format from [`Iker/Llama-3-Instruct-Neurona-8b-v2`](https://huggingface.co/Iker/Llama-3-Instruct-Neurona-8b-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Iker/Llama-3-Instruct-Neurona-8b-v2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-IQ4_NL-GGUF --hf-file llama-3-instruct-neurona-8b-v2-iq4_nl-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-IQ4_NL-GGUF --hf-file llama-3-instruct-neurona-8b-v2-iq4_nl-imat.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-IQ4_NL-GGUF --hf-file llama-3-instruct-neurona-8b-v2-iq4_nl-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo NikolayKozloff/Llama-3-Instruct-Neurona-8b-v2-IQ4_NL-GGUF --hf-file llama-3-instruct-neurona-8b-v2-iq4_nl-imat.gguf -c 2048 ```

debiao29/Qwen-Qwen1.5-1.8B-1719935843

debiao29

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

tanoManzo/DNABERT-2-117M_ft_Hepg2_1kbpHG19_DHSs_H3K27AC_10xControl

tanoManzo

transformers

text-classification

Entry not found

Lam-Hung/controlnet_lora_interior

Lam-Hung

Entry not found

kayynaik/fine-tuned-medical-model

kayynaik

--- license: llama2 ---

mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF

mradermacher

transformers

--- base_model: wiweka24/llama3-psychiatrist-v1.3B-fp16 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/wiweka24/llama3-psychiatrist-v1.3B-fp16 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.IQ3_XS.gguf) | IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.IQ3_M.gguf) | IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/llama3-psychiatrist-v1.3B-fp16-GGUF/resolve/main/llama3-psychiatrist-v1.3B-fp16.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

nalf3in/Gemma-2-9B-It-SPPO-Iter3-Q4_K_M-GGUF

nalf3in

text-generation

--- base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3 datasets: - openbmb/UltraFeedback language: - en license: gemma pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # nalf3in/Gemma-2-9B-It-SPPO-Iter3-Q4_K_M-GGUF This model was converted to GGUF format from [`UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3`](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo nalf3in/Gemma-2-9B-It-SPPO-Iter3-Q4_K_M-GGUF --hf-file gemma-2-9b-it-sppo-iter3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo nalf3in/Gemma-2-9B-It-SPPO-Iter3-Q4_K_M-GGUF --hf-file gemma-2-9b-it-sppo-iter3-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo nalf3in/Gemma-2-9B-It-SPPO-Iter3-Q4_K_M-GGUF --hf-file gemma-2-9b-it-sppo-iter3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo nalf3in/Gemma-2-9B-It-SPPO-Iter3-Q4_K_M-GGUF --hf-file gemma-2-9b-it-sppo-iter3-q4_k_m.gguf -c 2048 ```

ZarahShibli/tmp_trainer

ZarahShibli

transformers

--- tags: - generated_from_trainer model-index: - name: tmp_trainer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tmp_trainer This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0

NikolayKozloff/Qwen2-1.5B-ITA-Instruct-Q8_0-GGUF

NikolayKozloff

--- base_model: e-palmisano/Qwen2-1.5B-ITA-Instruct datasets: - gsarti/clean_mc4_it - FreedomIntelligence/alpaca-gpt4-italian language: - it - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/Qwen2-1.5B-ITA-Instruct-Q8_0-GGUF This model was converted to GGUF format from [`e-palmisano/Qwen2-1.5B-ITA-Instruct`](https://huggingface.co/e-palmisano/Qwen2-1.5B-ITA-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/e-palmisano/Qwen2-1.5B-ITA-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo NikolayKozloff/Qwen2-1.5B-ITA-Instruct-Q8_0-GGUF --hf-file qwen2-1.5b-ita-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/Qwen2-1.5B-ITA-Instruct-Q8_0-GGUF --hf-file qwen2-1.5b-ita-instruct-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo NikolayKozloff/Qwen2-1.5B-ITA-Instruct-Q8_0-GGUF --hf-file qwen2-1.5b-ita-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo NikolayKozloff/Qwen2-1.5B-ITA-Instruct-Q8_0-GGUF --hf-file qwen2-1.5b-ita-instruct-q8_0.gguf -c 2048 ```

GeorgeImmanuel/a2c_PickAndPlaceRobot-v2

GeorgeImmanuel

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - PandaPickAndPlace-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaPickAndPlace-v3 type: PandaPickAndPlace-v3 metrics: - type: mean_reward value: -50.00 +/- 0.00 name: mean_reward verified: false --- # **A2C** Agent playing **PandaPickAndPlace-v3** This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

juanpablomesa/bge-small-bioasq

juanpablomesa

sentence-transformers

sentence-similarity

--- base_model: BAAI/bge-small-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:4012 - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'Extensive messenger RNA editing generates transcript and protein diversity in genes involved in neural excitability, as previously described, as well as in genes participating in a broad range of other cellular functions. ' sentences: - Do cephalopods use RNA editing less frequently than other species? - GV1001 vaccine targets which enzyme? - Which event results in the acetylation of S6K1? - source_sentence: Yes, exposure to household furry pets influences the gut microbiota of infants. sentences: - Can pets affect infant microbiomed? - What is the mode of action of Thiazovivin? - What are the effects of CAMK4 inhibition? - source_sentence: "In children with heart failure evidence of the effect of enalapril\ \ is empirical. Enalapril was clinically safe and effective in 50% to 80% of for\ \ children with cardiac failure secondary to congenital heart malformations before\ \ and after cardiac surgery, impaired ventricular function , valvar regurgitation,\ \ congestive cardiomyopathy, , arterial hypertension, life-threatening arrhythmias\ \ coexisting with circulatory insufficiency. \nACE inhibitors have shown a transient\ \ beneficial effect on heart failure due to anticancer drugs and possibly a beneficial\ \ effect in muscular dystrophy-associated cardiomyopathy, which deserves further\ \ studies." sentences: - Which receptors can be evaluated with the [18F]altanserin? - In what proportion of children with heart failure has Enalapril been shown to be safe and effective? - Which major signaling pathways are regulated by RIP1? - source_sentence: Cellular senescence-associated heterochromatic foci (SAHFS) are a novel type of chromatin condensation involving alterations of linker histone H1 and linker DNA-binding proteins. SAHFS can be formed by a variety of cell types, but their mechanism of action remains unclear. sentences: - What is the relationship between the X chromosome and a neutrophil drumstick? - Which microRNAs are involved in exercise adaptation? - How are SAHFS created? - source_sentence: Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss of all three clusters (tricluster deletion) led to a severe axonal arborization defect and loss of self-avoidance. sentences: - What are the effects of the deletion of all three Pcdh clusters (tricluster deletion) in mice? - what is the role of MEF-2 in cardiomyocyte differentiation? - How many periods of regulatory innovation led to the evolution of vertebrates? --- # BGE small finetuned BIOASQ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("juanpablomesa/bge-small-bioasq") # Run inference sentences = [ 'Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss of all three clusters (tricluster deletion) led to a severe axonal arborization defect and loss of self-avoidance.', 'What are the effects of the deletion of all three Pcdh clusters (tricluster deletion) in mice?', 'How many periods of regulatory innovation led to the evolution of vertebrates?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 4,012 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 63.38 tokens</li><li>max: 485 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.13 tokens</li><li>max: 49 tokens</li></ul> | * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | <code>Aberrant patterns of H3K4, H3K9, and H3K27 histone lysine methylation were shown to result in histone code alterations, which induce changes in gene expression, and affect the proliferation rate of cells in medulloblastoma.</code> | <code>What is the implication of histone lysine methylation in medulloblastoma?</code> | | <code>STAG1/STAG2 proteins are tumour suppressor proteins that suppress cell proliferation and are essential for differentiation.</code> | <code>What is the role of STAG1/STAG2 proteins in differentiation?</code> | | <code>The association between cell phone use and incident glioblastoma remains unclear. Some studies have reported that cell phone use was associated with incident glioblastoma, and with reduced survival of patients diagnosed with glioblastoma. However, other studies have repeatedly replicated to find an association between cell phone use and glioblastoma.</code> | <code>What is the association between cell phone use and glioblastoma?</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Framework Versions - Python: 3.11.5 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

styalai/tokenizer-XTmath-8_000

styalai

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Shani123/nllb-200-distilled-600M_heb_eng_v3_sci_articles

Shani123

transformers

text2text-generation

Entry not found

DeepDream2045/Daredevil-7B-Quant

DeepDream2045

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

gisang-lee/mistral-7b-qlora-arc-wandb-test-arc-challenge-all

gisang-lee

transformers

text-generation

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

stephen-osullivan/my_awesome_wnut_model

stephen-osullivan

transformers

token-classification

--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: my_awesome_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5680628272251309 - name: Recall type: recall value: 0.40222428174235403 - name: F1 type: f1 value: 0.4709712425393381 - name: Accuracy type: accuracy value: 0.9480141934932239 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_wnut_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2966 - Precision: 0.5681 - Recall: 0.4022 - F1: 0.4710 - Accuracy: 0.9480 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 107 | 0.2496 | 0.5131 | 0.3624 | 0.4248 | 0.9450 | | No log | 2.0 | 214 | 0.2794 | 0.5829 | 0.3485 | 0.4362 | 0.9456 | | No log | 3.0 | 321 | 0.2808 | 0.5755 | 0.3781 | 0.4564 | 0.9465 | | No log | 4.0 | 428 | 0.2935 | 0.5569 | 0.3902 | 0.4589 | 0.9476 | | 0.059 | 5.0 | 535 | 0.2966 | 0.5681 | 0.4022 | 0.4710 | 0.9480 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

crocutacrocuto/convnext-base-224-ECCV_backEqSCheck-15

crocutacrocuto

transformers

image-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

brandonyfeng/echo_SVD

brandonyfeng

diffusers

Entry not found

not1010011010/model-AIvaras

not1010011010

transformers

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** not1010011010 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

CassioBN/xlm-roberta-base_LeNER-Br

CassioBN

transformers

token-classification

--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer datasets: - lener_br metrics: - precision - recall - f1 - accuracy model-index: - name: xlm-roberta-base_LeNER-Br results: - task: name: Token Classification type: token-classification dataset: name: lener_br type: lener_br config: lener_br split: validation args: lener_br metrics: - name: Precision type: precision value: 0.8295165394402035 - name: Recall type: recall value: 0.8965896589658966 - name: F1 type: f1 value: 0.8617499339148824 - name: Accuracy type: accuracy value: 0.9714166181062949 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_LeNER-Br This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the lener_br dataset. It achieves the following results on the evaluation set: - Loss: nan - Precision: 0.8295 - Recall: 0.8966 - F1: 0.8617 - Accuracy: 0.9714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2394 | 1.0 | 979 | nan | 0.7134 | 0.8614 | 0.7805 | 0.9638 | | 0.0375 | 2.0 | 1958 | nan | 0.8035 | 0.9043 | 0.8509 | 0.9670 | | 0.0256 | 3.0 | 2937 | nan | 0.8026 | 0.8878 | 0.8430 | 0.9761 | | 0.0194 | 4.0 | 3916 | nan | 0.7836 | 0.8861 | 0.8317 | 0.9670 | | 0.015 | 5.0 | 4895 | nan | 0.8061 | 0.8988 | 0.8499 | 0.9691 | | 0.0098 | 6.0 | 5874 | nan | 0.8279 | 0.9076 | 0.8659 | 0.9715 | | 0.0082 | 7.0 | 6853 | nan | 0.8067 | 0.8905 | 0.8465 | 0.9681 | | 0.0042 | 8.0 | 7832 | nan | 0.8233 | 0.9021 | 0.8609 | 0.9737 | | 0.0037 | 9.0 | 8811 | nan | 0.8281 | 0.9010 | 0.8630 | 0.9712 | | 0.0031 | 10.0 | 9790 | nan | 0.8295 | 0.8966 | 0.8617 | 0.9714 | ### Testing Results metrics={'test_loss': 0.07461995631456375, 'test_precision': 0.8852040816326531, 'test_recall': 0.9137590520079, 'test_f1': 0.8992549400712667, 'test_accuracy': 0.9883402014967543, 'test_runtime': 13.0766, 'test_samples_per_second': 106.297, 'test_steps_per_second': 13.306}) ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

debiao29/google-gemma-2b-1719936475

debiao29

peft

--- base_model: google/gemma-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

NikolayKozloff/dictalm2-it-qa-fine-tune-Q8_0-GGUF

NikolayKozloff

transformers

text-generation

--- base_model: 618AI/dictalm2-it-qa-fine-tune language: - he library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/dictalm2-it-qa-fine-tune-Q8_0-GGUF This model was converted to GGUF format from [`618AI/dictalm2-it-qa-fine-tune`](https://huggingface.co/618AI/dictalm2-it-qa-fine-tune) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/618AI/dictalm2-it-qa-fine-tune) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo NikolayKozloff/dictalm2-it-qa-fine-tune-Q8_0-GGUF --hf-file dictalm2-it-qa-fine-tune-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/dictalm2-it-qa-fine-tune-Q8_0-GGUF --hf-file dictalm2-it-qa-fine-tune-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo NikolayKozloff/dictalm2-it-qa-fine-tune-Q8_0-GGUF --hf-file dictalm2-it-qa-fine-tune-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo NikolayKozloff/dictalm2-it-qa-fine-tune-Q8_0-GGUF --hf-file dictalm2-it-qa-fine-tune-q8_0.gguf -c 2048 ```

anhphuong/STT_medium

anhphuong

transformers

automatic-speech-recognition

--- language: - pa license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Medium Japanese - Anh Phuong results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: ja split: None args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 68.60652436568667 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Medium Japanese - Anh Phuong This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4725 - Wer: 68.6065 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:-------:| | 0.0776 | 2.6596 | 1000 | 0.3001 | 72.3520 | | 0.0076 | 5.3191 | 2000 | 0.3476 | 71.0632 | | 0.0013 | 7.9787 | 3000 | 0.4063 | 68.8079 | | 0.0001 | 10.6383 | 4000 | 0.4725 | 68.6065 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

debiao29/Qwen-Qwen1.5-0.5B-1719936629

debiao29

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

Icchan/Aikatsu

Icchan

--- license: openrail ---

1231czx/7b_dpo_iter3_4e7_step50_nll

1231czx

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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stablediffusionapi/mymix-g-jem

stablediffusionapi

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # MyMIX-G Jem API Inference  ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "mymix-g-jem" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/mymix-g-jem) Model link: [View model](https://modelslab.com/models/mymix-g-jem) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "mymix-g-jem", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

srirama/whisper-small-hi

srirama

transformers

automatic-speech-recognition

Entry not found

lfnothing/opt-125m-gptq

lfnothing

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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KUD-genai/TAIDE_healthedu_v6

KUD-genai

peft

--- base_model: taide/Llama3-TAIDE-LX-8B-Chat-Alpha1 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

efeno/RAFT_biomedical_100_PEFT

efeno

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Narumashi-RT-11B - GGUF - Model creator: https://huggingface.co/Alsebay/ - Original model: https://huggingface.co/Alsebay/Narumashi-RT-11B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Narumashi-RT-11B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q2_K.gguf) | Q2_K | 3.73GB | | [Narumashi-RT-11B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.IQ3_XS.gguf) | IQ3_XS | 4.14GB | | [Narumashi-RT-11B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.IQ3_S.gguf) | IQ3_S | 4.37GB | | [Narumashi-RT-11B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q3_K_S.gguf) | Q3_K_S | 4.34GB | | [Narumashi-RT-11B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.IQ3_M.gguf) | IQ3_M | 4.51GB | | [Narumashi-RT-11B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q3_K.gguf) | Q3_K | 4.84GB | | [Narumashi-RT-11B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q3_K_M.gguf) | Q3_K_M | 4.84GB | | [Narumashi-RT-11B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q3_K_L.gguf) | Q3_K_L | 5.26GB | | [Narumashi-RT-11B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.IQ4_XS.gguf) | IQ4_XS | 5.43GB | | [Narumashi-RT-11B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q4_0.gguf) | Q4_0 | 5.66GB | | [Narumashi-RT-11B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.IQ4_NL.gguf) | IQ4_NL | 5.72GB | | [Narumashi-RT-11B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q4_K_S.gguf) | Q4_K_S | 5.7GB | | [Narumashi-RT-11B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q4_K.gguf) | Q4_K | 6.02GB | | [Narumashi-RT-11B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q4_K_M.gguf) | Q4_K_M | 6.02GB | | [Narumashi-RT-11B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q4_1.gguf) | Q4_1 | 6.27GB | | [Narumashi-RT-11B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q5_0.gguf) | Q5_0 | 6.89GB | | [Narumashi-RT-11B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q5_K_S.gguf) | Q5_K_S | 6.89GB | | [Narumashi-RT-11B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q5_K.gguf) | Q5_K | 7.08GB | | [Narumashi-RT-11B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q5_K_M.gguf) | Q5_K_M | 7.08GB | | [Narumashi-RT-11B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q5_1.gguf) | Q5_1 | 7.51GB | | [Narumashi-RT-11B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q6_K.gguf) | Q6_K | 8.2GB | | [Narumashi-RT-11B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narumashi-RT-11B-gguf/blob/main/Narumashi-RT-11B.Q8_0.gguf) | Q8_0 | 10.62GB | Original model description: --- language: - en license: cc-by-nc-4.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft - Roleplay - roleplay base_model: Sao10K/Fimbulvetr-11B-v2 --- > [!Important] > Still in experiment # About this model This model now can handle (limited) TSF content. If you Character Card have complex plot, maybe you should try other model (maybe bigger parameter?). **Update: I think it worse than original model: Sao10K/Fimbulvetr-11B-v2. This model was trained with rough translated dataset, so the responses is short, the IQ logic go down, also it will response wrong name, nonsense sentences sometimes...** Anyways, if you find this is good, please let me know. Will have another update later. Do you know TSF, TS, TG? A lot of model don't really know about that, so I do some experiment to finetune TSF dataset. - **Finetuned with rough translate dataset, to increase the accuracy in TSF theme, which is not quite popular. (lewd dataset)** - **Finetuned from model :** Sao10K/Fimbulvetr-11B-v2 . Thank Sao10K a lot :) ## Still testing, but seem it good enough for handle information. But the logic go down a bit because the rough translate dataset. ## GGUF version? [here is it](https://huggingface.co/Alsebay/Narumashi-RT-11B-GGUF). ## Dataset Rough translated dataset, you could say that this is bad quality dataset. ``` Dataset(all are novels): 30% skinsuit 30% possession 35% transform(shapeshift) 5% other ``` # Thank Unsloth for good finetuning tool. This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Jiban4/lora_model

Jiban4

transformers

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** Jiban4 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

debiao29/Qwen-Qwen1.5-1.8B-1719937023

debiao29

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

LevonHakobyan/adapter_base_const_lr_overfitcheck

LevonHakobyan

transformers

automatic-speech-recognition

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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

NightBitch24-1/modeloBitch

NightBitch24-1

Entry not found

debiao29/google-gemma-2b-1719937221

debiao29

peft

--- base_model: google/gemma-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

vgangal101/distilbert-base-uncased-finetuned-imdb-accelerate

vgangal101

Entry not found

styalai/XTmath-unknowM

styalai

transformers

--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

Trelis/multi-qa-MiniLM-L6-dot-v1-ft-triplets-2-cst-epoch-overlap

Trelis

sentence-transformers

sentence-similarity

--- base_model: sentence-transformers/multi-qa-MiniLM-L6-dot-v1 datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9729 - loss:TripletLoss widget: - source_sentence: What is the penalty awarded to the attacking team when a defending player fails to retire the required seven metres or to the defending try line after effecting a touch? sentences: - '13. 3 a player must not perform a voluntary rollball. ruling = a penalty to the defending team at the point of the infringement. 13. 4 a player must not delay in performing the rollball. ruling = a penalty to the defending team at the point of the infringement. 13. 5 a player may only perform a rollball at the mark under the following circumstances : 13. 5. 1 when a touch has been made ; or 13. 5. 2 when possession changes following the sixth touch ; or 13. 5. 3 when possession changes due to the ball being dropped or passed and goes to the ground ; or 13. 5. 4 when possession changes due to an infringement by an attacking player at a penalty, a tap or a rollball ; or fit playing rules - 5th edition copyright © touch football australia 2020 11 13. 5. 5 when possession changes after the half is touched or when the half places the ball on or over the try line ; or 13. 5. 6 in replacement of a penalty tap ; or 13. 5. 7 when so directed by the referee.' - 5th edition rules touch football tion rules touch football touch football australia ( tfa ) undertook an extensive internal review of their domestic playing rules throughout 2018 and 2019. the review was led by an vastly experienced group of current and past players, coaches, referees and administrators of the sport from community competitions to the elite international game. this group consulted broadly within the australian community to develop a set of playing rules that could be applied across all levels of the sport. the result was the tfa 8th edition playing rules. at the federation of international touch paris convention held in october 2019 touch football australia presented the tfa 8th edition playing rules and subsequently offered fit and all national touch associations ( ntas ) royalty free rights to use the newly developed rules. consequently, the fit board resolved to adopt the tfa 8th edition playing rules as the 5th edition fit playing rules to be used across all levels of the game internationally. fit and its members acknowledge and thank touch football australia for the rights to use these rules. whilst consistency in the application of the rules of the game is important, fit encourages its members to offer features in local competition rules to ensure that all participants enjoy a high quality experience. - ruling = a penalty to the attacking team at the point of the infringement or on the seven ( 7 ) metre line directly forward of the infringement. 15. 4 when a rollball occurs within defending team ’ s seven metre zone or a penalty tap within ten ( 10 ) metres of the defending team ’ s try line, all players from the defending team must have both feet on or behind their try line and no other part of the body in contact with the ground forward of their try line. ruling = a penalty to the attacking team at the seven ( 7 ) metre line directly forward of the point of the infringement. 15. 5 after effecting the touch, the defending player must retire the required seven ( 7 ) metres or to the defending try line as indicated by the referee without interfering with the attacking team. ruling = a penalty to the attacking team ten ( 10 ) metres forward of the infringement or if on the defensive try line, on the seven ( 7 ) metre line. fit playing rules - 5th edition copyright © touch football australia 2020 13 16 obstruction 16. 1 a player in possession must not run or otherwise move behind other attacking players or the referee in an attempt to avoid an imminent touch. - source_sentence: What is the definition of 'infringement' in Touch Rugby? sentences: - 2. 2 teams playing unregistered players are liable to forfeit any match in which unregistered players have competed. fit playing rules - 5th edition copyright © touch football australia 2020 5 3 the ball 3. 1 the game is played with an oval, inflated ball of a shape, colour and size approved by fit or the nta. 3. 2 the ball shall be inflated to the manufacturers ’ recommended air pressure. 3. 3 the referee shall immediately pause the match if the size and shape of the ball no longer complies with clauses 3. 1 or 3. 2 to allow for the ball to replaced or the issue rectified. 3. 4 the ball must not be hidden under player attire. 4 playing uniform 4. 1 participating players are to be correctly attired in matching team uniforms 4. 2 playing uniforms consist of shirt, singlet or other item as approved by the nta or nta competition provider, shorts and / or tights and socks. 4. 3 all players are to wear a unique identifying number not less than 16cm in height, clearly displayed on the rear of the playing top. 4. 3. 1 identifying numbers must feature no more than two ( 2 ) digits. - end of play when the referee indicates completion of the match. exclusion when a player is sent to the nearest sin bin area following three ( 3 ) penalties by the defending team upon entering their seven metre zone. the player is counted as a player on the field of play and cannot be replaced or interchanged. fit playing rules - 5th edition copyright © touch football australia 2020 1 fit federation of international touch field of play the playing area bounded by the sidelines and dead ball lines, both of which are out of bounds. see appendix 1. forced interchange when a player is required to undertake a compulsory interchange for an infringement ruled more serious than a penalty but less serious than a permanent interchange, sin bin or dismissal. forward a position or direction towards the dead ball line beyond the team ’ s attacking try line. full time the expiration of the second period of time allowed for play. half the player who takes possession following a rollball. half time the break in play between the two halves of a match. imminent about to occur, it is almost certain to occur. infringement the action of a player contrary to the rules of the game. - 2. 2 teams playing unregistered players are liable to forfeit any match in which unregistered players have competed. fit playing rules - 5th edition copyright © touch football australia 2020 5 3 the ball 3. 1 the game is played with an oval, inflated ball of a shape, colour and size approved by fit or the nta. 3. 2 the ball shall be inflated to the manufacturers ’ recommended air pressure. 3. 3 the referee shall immediately pause the match if the size and shape of the ball no longer complies with clauses 3. 1 or 3. 2 to allow for the ball to replaced or the issue rectified. 3. 4 the ball must not be hidden under player attire. 4 playing uniform 4. 1 participating players are to be correctly attired in matching team uniforms 4. 2 playing uniforms consist of shirt, singlet or other item as approved by the nta or nta competition provider, shorts and / or tights and socks. 4. 3 all players are to wear a unique identifying number not less than 16cm in height, clearly displayed on the rear of the playing top. 4. 3. 1 identifying numbers must feature no more than two ( 2 ) digits. - source_sentence: What is the penalty awarded to the non-offending team when an offence is identified? sentences: - ruling = a penalty to the attacking team at a point ten ( 10 ) metres directly forward of the infringement. 13. 12 players of the defending team must not move forward of the onside position until the half has made contact with the ball, unless directed to do so by the referee or in accordance with 13. 12. 1. 13. 12. 1 when the half is not within one ( 1 ) metre of the rollball, onside players of the defending team may move forward as soon as the player performing the rollball releases the ball. if the half is not in position and a defending player moves forward and makes contact with the ball, a change of possession results. 13. 13 if in the act of performing the rollball, the attacking player makes contact with the sideline or any ground outside the field of play a change of possession will occur with the rollball to be taken seven ( 7 ) metres in field. 13. 14 after a touch is made between the dead ball line and the seven ( 7 ) metre line, an attacking team is permitted to rollball on the seven ( 7 ) metre line at a point directly in line with where the touch was made. - 10. 4 if the ball is accidentally knocked from the hands of a player in possession during a touch, the touch counts and the attacking team retains possession. 10. 5 the defending player must not deliberately knock the ball from the hands of a player in possession during a touch. ruling = a penalty to the attacking team at the point of the infringement. 10. 6 a player must not pass or otherwise deliver the ball after a touch has been made. ruling = a penalty to the defending team at the point of the infringement, or if in - goal the nearest point on the seven ( 7 ) metre line. 10. 7 the half may pass or run with the ball but cannot get touched while in possession of the ball. ruling = a change of possession occurs at the point of the touch, or if in - goal the nearest point on the seven ( 7 ) metre line. 10. 8 if a touch is made in the in - goal area before the ball is grounded, the player in possession is to perform a rollball seven ( 7 ) metres from the team ’ s attacking try line, provided it is not the sixth touch and the player is not half. - 4. 10 referees and players may wear sport monitoring equipment and medical supports such as knee or ankle braces provided, at the sole discretion of competition ’ s controlling body, the items are not dangerous. 5 team composition 5. 1 a team consists of a maximum of 14 players, no more than six ( 6 ) of whom are allowed on the field at any time. fit playing rules - 5th edition 6 copyright © touch football australia 2020 ruling = a penalty awarded to the non - offending team at the time the offence is identified seven ( 7 ) metres infield on the halfway line or the position of the ball, whichever is the greater advantage. 5. 2 a team must have a minimum of four ( 4 ) players on the field for a match to commence or continue, except during a drop - off. 5. 3 where the number of players on the field from one team falls below four ( 4 ), the match is to be abandoned and the non - offending team is to be declared the winner. 5. 3. 1 this does not apply for players sent to the sin bin area. - source_sentence: What is the requirement for adapting or altering rules for local competitions? sentences: - 'whilst consistency in the application of the rules of the game is important, fit encourages its members to offer features in local competition rules to ensure that all participants enjoy a high quality experience. these rules in no way restrict any nta or their authorised competition providers from having different match conditions to these rules. any adaptation of or alterations to the rules for local competitions should be clearly articulated in relevant competition guidelines and be readily available for players, coaches and referees alike preamble copyright © touch football australia 2020 all rights reserved. these touch football rules are protected by copyright laws. except as permitted under the copyright act, these rules must not be reproduced by any process, electronic or otherwise, without the written permission of touch football australia. fit playing rules - 5th edition copyright © touch football australia 2020 appendix 1 – field of play contents 01 i the field of play 5 02 i player registration 5 03 i the ball 6 04 i playing uniform 6 05 i team composition 6 06 i team coach and team officials 7 07 i commencement and recommencement of play 7 08 i match duration 8 09 i possession 8 10 i the touch 9 11 i passing 10 12 i ball touched in flight 10 13 i the rollball 11 14 i scoring 13 15 i offside 13 16 i obstruction 14 17 i interchange 14 18 i penalty 15 19 i advantage 16 20 i misconduct 16 21 i forced interchange 16 22 i sin bin 16 23 i dismissal 17 24 i drop - off 17 25 i match officials 18 fit playing rules - 5th edition copyright © touch football australia 2020 fit playing rules - 5th edition copyright © touch football australia 2020 definitions and terminology unless the contrary intention appears, the following definitions and terminology apply to the game of touch : term / phrase definition / description advantage the period of time after an infringement in which the non - offending side has the opportunity to gain advantage either territorial, tactical or in the form of a try.' - 5th edition rules touch football tion rules touch football touch football australia ( tfa ) undertook an extensive internal review of their domestic playing rules throughout 2018 and 2019. the review was led by an vastly experienced group of current and past players, coaches, referees and administrators of the sport from community competitions to the elite international game. this group consulted broadly within the australian community to develop a set of playing rules that could be applied across all levels of the sport. the result was the tfa 8th edition playing rules. at the federation of international touch paris convention held in october 2019 touch football australia presented the tfa 8th edition playing rules and subsequently offered fit and all national touch associations ( ntas ) royalty free rights to use the newly developed rules. consequently, the fit board resolved to adopt the tfa 8th edition playing rules as the 5th edition fit playing rules to be used across all levels of the game internationally. fit and its members acknowledge and thank touch football australia for the rights to use these rules. whilst consistency in the application of the rules of the game is important, fit encourages its members to offer features in local competition rules to ensure that all participants enjoy a high quality experience. - 5. 3. 1 this does not apply for players sent to the sin bin area. 5. 4 in mixed gender competitions, the maximum number of males allowed on the field of play is three ( 3 ), the minimum male requirement is one ( 1 ) and the minimum female requirement is one ( 1 ). 6 team coach and team officials 6. 1 the team coach ( s ) and team officials may be permitted inside the perimeter but shall be required to be positioned either in the interchange area or at the end of the field of play for the duration of the match. 6. 2 the team coach ( s ) and team officials may move from one position to the other but shall do so without delay. while in a position at the end of the field of play, the team coach ( s ) or team official must remain no closer than five ( 5 ) metres from the dead ball line and must not coach or communicate ( verbal or non - verbal ) with either team or the referees. - source_sentence: What is the minimum number of males and females required on the field of play in mixed gender competitions? sentences: - ruling = a penalty to the attacking team at the seven ( 7 ) metre line in line with the point of the infringement. 11 passing 11. 1 a player in possession may not kick, pass, flick, knock, throw, hand - off or otherwise propel the ball in a forward direction, either intentionally or otherwise to another player. ruling = a penalty will be awarded to the defending team at the mark where the ball was propelled forward unless advantage is applied. 11. 2 a player in possession may not intentionally kick, pass, flick, knock, throw, hand - off or otherwise propel the ball in a forward direction over an opposition player and regain possession. ruling = a penalty will be awarded to the defending team at the mark where the ball was propelled forward. 12 ball touched in flight 12. 1 if a player from the defending team deliberately makes contact with the ball in flight and the ball goes to ground, the attacking team retains the ball and the touch count restarts as zero ( 0 ) touch. 12. 2 if a player from the defending team deliberately makes contact with the ball in flight and the ball is retrieved by an attacking player, without touching the ground, play continues and the next touch is zero ( 0 ) touch. - 5. 3. 1 this does not apply for players sent to the sin bin area. 5. 4 in mixed gender competitions, the maximum number of males allowed on the field of play is three ( 3 ), the minimum male requirement is one ( 1 ) and the minimum female requirement is one ( 1 ). 6 team coach and team officials 6. 1 the team coach ( s ) and team officials may be permitted inside the perimeter but shall be required to be positioned either in the interchange area or at the end of the field of play for the duration of the match. 6. 2 the team coach ( s ) and team officials may move from one position to the other but shall do so without delay. while in a position at the end of the field of play, the team coach ( s ) or team official must remain no closer than five ( 5 ) metres from the dead ball line and must not coach or communicate ( verbal or non - verbal ) with either team or the referees. - tap and tap penalty the method of commencing the match, recommencing the match after half time and after a try has been scored. the tap is also the method of recommencing play when a penalty is awarded. the tap is taken by placing the ball on the ground at or behind the mark, releasing both hands from the ball, tapping the ball gently with either foot or touching the foot on the ball. the ball must not roll or move more than one ( 1 ) metre in any direction and must be retrieved cleanly, without touching the ground again. the player may face any direction and use either foot. provided it is at the mark, the ball does not have to be lifted from the ground prior to a tap being taken. team a group of players constituting one ( 1 ) side in a competition match. tfa touch football australia limited touch any contact between the player in possession and a defending player. a touch includes contact on the ball, hair or clothing and may be made by a defending player or by the player in possession. touch count the progressive number of touches that each team has before a change of possession, from zero ( 0 ) to six ( 6 ). --- # SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-dot-v1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-dot-v1). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/multi-qa-MiniLM-L6-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-dot-v1) <!-- at revision c3bdeb02464bc83f9b85156a3386a50bfbf3e6a8 --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Dot Product <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Trelis/multi-qa-MiniLM-L6-dot-v1-ft-triplets-2-cst-epoch-overlap") # Run inference sentences = [ 'What is the minimum number of males and females required on the field of play in mixed gender competitions?', '5. 3. 1 this does not apply for players sent to the sin bin area. 5. 4 in mixed gender competitions, the maximum number of males allowed on the field of play is three ( 3 ), the minimum male requirement is one ( 1 ) and the minimum female requirement is one ( 1 ). 6 team coach and team officials 6. 1 the team coach ( s ) and team officials may be permitted inside the perimeter but shall be required to be positioned either in the interchange area or at the end of the field of play for the duration of the match. 6. 2 the team coach ( s ) and team officials may move from one position to the other but shall do so without delay. while in a position at the end of the field of play, the team coach ( s ) or team official must remain no closer than five ( 5 ) metres from the dead ball line and must not coach or communicate ( verbal or non - verbal ) with either team or the referees.', 'tap and tap penalty the method of commencing the match, recommencing the match after half time and after a try has been scored. the tap is also the method of recommencing play when a penalty is awarded. the tap is taken by placing the ball on the ground at or behind the mark, releasing both hands from the ball, tapping the ball gently with either foot or touching the foot on the ball. the ball must not roll or move more than one ( 1 ) metre in any direction and must be retrieved cleanly, without touching the ground again. the player may face any direction and use either foot. provided it is at the mark, the ball does not have to be lifted from the ground prior to a tap being taken. team a group of players constituting one ( 1 ) side in a competition match. tfa touch football australia limited touch any contact between the player in possession and a defending player. a touch includes contact on the ball, hair or clothing and may be made by a defending player or by the player in possession. touch count the progressive number of touches that each team has before a change of possession, from zero ( 0 ) to six ( 6 ).', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `lr_scheduler_type`: constant - `warmup_ratio`: 0.3 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: constant - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.3 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | loss | |:------:|:----:|:-------------:|:------:| | 0.0066 | 2 | 4.2577 | - | | 0.0131 | 4 | 4.0287 | - | | 0.0197 | 6 | 4.1022 | - | | 0.0262 | 8 | 3.8676 | - | | 0.0328 | 10 | 3.836 | - | | 0.0393 | 12 | 3.5016 | - | | 0.0459 | 14 | 3.5338 | - | | 0.0525 | 16 | 3.2488 | - | | 0.0590 | 18 | 3.1999 | - | | 0.0656 | 20 | 3.1266 | - | | 0.0721 | 22 | 2.7272 | - | | 0.0787 | 24 | 2.9005 | - | | 0.0852 | 26 | 2.4328 | - | | 0.0918 | 28 | 2.0929 | - | | 0.0984 | 30 | 1.9004 | - | | 0.1049 | 32 | 2.0227 | - | | 0.1115 | 34 | 1.8577 | - | | 0.1180 | 36 | 1.6767 | - | | 0.1246 | 38 | 1.6381 | - | | 0.1311 | 40 | 1.391 | - | | 0.1377 | 42 | 1.6477 | - | | 0.1443 | 44 | 1.7922 | - | | 0.1508 | 46 | 1.3904 | - | | 0.1574 | 48 | 1.7869 | - | | 0.1639 | 50 | 1.6701 | - | | 0.1705 | 52 | 1.1823 | - | | 0.1770 | 54 | 0.9928 | - | | 0.1836 | 56 | 1.4254 | - | | 0.1902 | 58 | 1.4535 | - | | 0.1967 | 60 | 1.3876 | - | | 0.2033 | 62 | 1.411 | - | | 0.2098 | 64 | 0.9778 | - | | 0.2164 | 66 | 0.9914 | - | | 0.2230 | 68 | 0.9946 | - | | 0.2295 | 70 | 0.9828 | - | | 0.2361 | 72 | 0.7432 | - | | 0.2426 | 74 | 1.315 | - | | 0.2492 | 76 | 0.6955 | - | | 0.2525 | 77 | - | 0.5755 | | 0.2557 | 78 | 0.7919 | - | | 0.2623 | 80 | 1.001 | - | | 0.2689 | 82 | 1.086 | - | | 0.2754 | 84 | 0.8213 | - | | 0.2820 | 86 | 0.9834 | - | | 0.2885 | 88 | 1.0083 | - | | 0.2951 | 90 | 0.6879 | - | | 0.3016 | 92 | 0.672 | - | | 0.3082 | 94 | 0.663 | - | | 0.3148 | 96 | 1.0285 | - | | 0.3213 | 98 | 1.0634 | - | | 0.3279 | 100 | 0.9316 | - | | 0.3344 | 102 | 0.88 | - | | 0.3410 | 104 | 0.9057 | - | | 0.3475 | 106 | 0.7303 | - | | 0.3541 | 108 | 0.8927 | - | | 0.3607 | 110 | 0.6541 | - | | 0.3672 | 112 | 0.6616 | - | | 0.3738 | 114 | 0.9198 | - | | 0.3803 | 116 | 0.4953 | - | | 0.3869 | 118 | 0.7159 | - | | 0.3934 | 120 | 0.6596 | - | | 0.4 | 122 | 0.8359 | - | | 0.4066 | 124 | 0.7748 | - | | 0.4131 | 126 | 0.5949 | - | | 0.4197 | 128 | 0.4204 | - | | 0.4262 | 130 | 1.0151 | - | | 0.4328 | 132 | 0.4979 | - | | 0.4393 | 134 | 0.6496 | - | | 0.4459 | 136 | 0.6039 | - | | 0.4525 | 138 | 0.6333 | - | | 0.4590 | 140 | 0.5494 | - | | 0.4656 | 142 | 0.5599 | - | | 0.4721 | 144 | 0.353 | - | | 0.4787 | 146 | 0.6529 | - | | 0.4852 | 148 | 0.5215 | - | | 0.4918 | 150 | 0.6408 | - | | 0.4984 | 152 | 0.6084 | - | | 0.5049 | 154 | 0.8947 | 0.2713 | | 0.5115 | 156 | 0.515 | - | | 0.5180 | 158 | 0.4075 | - | | 0.5246 | 160 | 0.7453 | - | | 0.5311 | 162 | 0.4385 | - | | 0.5377 | 164 | 0.5747 | - | | 0.5443 | 166 | 0.725 | - | | 0.5508 | 168 | 0.6338 | - | | 0.5574 | 170 | 0.2453 | - | | 0.5639 | 172 | 0.4578 | - | | 0.5705 | 174 | 0.3541 | - | | 0.5770 | 176 | 0.5326 | - | | 0.5836 | 178 | 0.3699 | - | | 0.5902 | 180 | 0.1714 | - | | 0.5967 | 182 | 0.3149 | - | | 0.6033 | 184 | 0.561 | - | | 0.6098 | 186 | 0.2852 | - | | 0.6164 | 188 | 0.2715 | - | | 0.6230 | 190 | 0.5521 | - | | 0.6295 | 192 | 0.2852 | - | | 0.6361 | 194 | 0.5103 | - | | 0.6426 | 196 | 0.3866 | - | | 0.6492 | 198 | 0.4369 | - | | 0.6557 | 200 | 0.3936 | - | | 0.6623 | 202 | 0.5084 | - | | 0.6689 | 204 | 0.4912 | - | | 0.6754 | 206 | 0.2659 | - | | 0.6820 | 208 | 0.6209 | - | | 0.6885 | 210 | 0.3022 | - | | 0.6951 | 212 | 0.2738 | - | | 0.7016 | 214 | 0.5555 | - | | 0.7082 | 216 | 0.3672 | - | | 0.7148 | 218 | 0.3489 | - | | 0.7213 | 220 | 0.6139 | - | | 0.7279 | 222 | 0.4402 | - | | 0.7344 | 224 | 0.2829 | - | | 0.7410 | 226 | 0.3669 | - | | 0.7475 | 228 | 0.729 | - | | 0.7541 | 230 | 0.2565 | - | | 0.7574 | 231 | - | 0.1849 | | 0.7607 | 232 | 0.2596 | - | | 0.7672 | 234 | 0.2359 | - | | 0.7738 | 236 | 0.4406 | - | | 0.7803 | 238 | 0.2629 | - | | 0.7869 | 240 | 0.3583 | - | | 0.7934 | 242 | 0.5298 | - | | 0.8 | 244 | 0.6225 | - | | 0.8066 | 246 | 0.3853 | - | | 0.8131 | 248 | 0.4741 | - | | 0.8197 | 250 | 0.3991 | - | | 0.8262 | 252 | 0.5629 | - | | 0.8328 | 254 | 0.2935 | - | | 0.8393 | 256 | 0.3563 | - | | 0.8459 | 258 | 0.3628 | - | | 0.8525 | 260 | 0.2416 | - | | 0.8590 | 262 | 0.1493 | - | | 0.8656 | 264 | 0.2488 | - | | 0.8721 | 266 | 0.4055 | - | | 0.8787 | 268 | 0.1286 | - | | 0.8852 | 270 | 0.4217 | - | | 0.8918 | 272 | 0.3529 | - | | 0.8984 | 274 | 0.1921 | - | | 0.9049 | 276 | 0.1736 | - | | 0.9115 | 278 | 0.4308 | - | | 0.9180 | 280 | 0.0992 | - | | 0.9246 | 282 | 0.3927 | - | | 0.9311 | 284 | 0.3451 | - | | 0.9377 | 286 | 0.4504 | - | | 0.9443 | 288 | 0.3065 | - | | 0.9508 | 290 | 0.2844 | - | | 0.9574 | 292 | 0.4308 | - | | 0.9639 | 294 | 0.1754 | - | | 0.9705 | 296 | 0.2608 | - | | 0.9770 | 298 | 0.4232 | - | | 0.9836 | 300 | 0.3234 | - | | 0.9902 | 302 | 0.24 | - | | 0.9967 | 304 | 0.2112 | - | | 1.0033 | 306 | 0.6322 | - | | 1.0098 | 308 | 0.2987 | 0.1357 | | 1.0164 | 310 | 0.4052 | - | | 1.0230 | 312 | 0.1458 | - | | 1.0295 | 314 | 0.2593 | - | | 1.0361 | 316 | 0.193 | - | | 1.0426 | 318 | 0.29 | - | | 1.0492 | 320 | 0.299 | - | | 1.0557 | 322 | 0.0841 | - | | 1.0623 | 324 | 0.0534 | - | | 1.0689 | 326 | 0.2166 | - | | 1.0754 | 328 | 0.2431 | - | | 1.0820 | 330 | 0.2621 | - | | 1.0885 | 332 | 0.0986 | - | | 1.0951 | 334 | 0.4274 | - | | 1.1016 | 336 | 0.2388 | - | | 1.1082 | 338 | 0.0899 | - | | 1.1148 | 340 | 0.158 | - | | 1.1213 | 342 | 0.1748 | - | | 1.1279 | 344 | 0.1226 | - | | 1.1344 | 346 | 0.1815 | - | | 1.1410 | 348 | 0.2312 | - | | 1.1475 | 350 | 0.4114 | - | | 1.1541 | 352 | 0.2258 | - | | 1.1607 | 354 | 0.1519 | - | | 1.1672 | 356 | 0.1536 | - | | 1.1738 | 358 | 0.103 | - | | 1.1803 | 360 | 0.2901 | - | | 1.1869 | 362 | 0.1629 | - | | 1.1934 | 364 | 0.1541 | - | | 1.2 | 366 | 0.1986 | - | | 1.2066 | 368 | 0.2492 | - | | 1.2131 | 370 | 0.2137 | - | | 1.2197 | 372 | 0.1954 | - | | 1.2262 | 374 | 0.1947 | - | | 1.2328 | 376 | 0.2114 | - | | 1.2393 | 378 | 0.4277 | - | | 1.2459 | 380 | 0.3636 | - | | 1.2525 | 382 | 0.4151 | - | | 1.2590 | 384 | 0.2258 | - | | 1.2623 | 385 | - | 0.1095 | | 1.2656 | 386 | 0.2794 | - | | 1.2721 | 388 | 0.2504 | - | | 1.2787 | 390 | 0.3785 | - | | 1.2852 | 392 | 0.2448 | - | | 1.2918 | 394 | 0.3936 | - | | 1.2984 | 396 | 0.1686 | - | | 1.3049 | 398 | 0.2301 | - | | 1.3115 | 400 | 0.1533 | - | | 1.3180 | 402 | 0.2516 | - | | 1.3246 | 404 | 0.1238 | - | | 1.3311 | 406 | 0.1629 | - | | 1.3377 | 408 | 0.1395 | - | | 1.3443 | 410 | 0.1093 | - | | 1.3508 | 412 | 0.0899 | - | | 1.3574 | 414 | 0.1793 | - | | 1.3639 | 416 | 0.0648 | - | | 1.3705 | 418 | 0.2402 | - | | 1.3770 | 420 | 0.2711 | - | | 1.3836 | 422 | 0.1457 | - | | 1.3902 | 424 | 0.1338 | - | | 1.3967 | 426 | 0.3074 | - | | 1.4033 | 428 | 0.0738 | - | | 1.4098 | 430 | 0.1702 | - | | 1.4164 | 432 | 0.111 | - | | 1.4230 | 434 | 0.249 | - | | 1.4295 | 436 | 0.1143 | - | | 1.4361 | 438 | 0.2255 | - | | 1.4426 | 440 | 0.3167 | - | | 1.4492 | 442 | 0.0751 | - | | 1.4557 | 444 | 0.1101 | - | | 1.4623 | 446 | 0.2098 | - | | 1.4689 | 448 | 0.2086 | - | | 1.4754 | 450 | 0.0978 | - | | 1.4820 | 452 | 0.3184 | - | | 1.4885 | 454 | 0.1347 | - | | 1.4951 | 456 | 0.2259 | - | | 1.5016 | 458 | 0.1651 | - | | 1.5082 | 460 | 0.2183 | - | | 1.5148 | 462 | 0.1315 | 0.0771 | | 1.5213 | 464 | 0.2672 | - | | 1.5279 | 466 | 0.1783 | - | | 1.5344 | 468 | 0.0408 | - | | 1.5410 | 470 | 0.4634 | - | | 1.5475 | 472 | 0.1762 | - | | 1.5541 | 474 | 0.0553 | - | | 1.5607 | 476 | 0.2445 | - | | 1.5672 | 478 | 0.1988 | - | | 1.5738 | 480 | 0.1985 | - | | 1.5803 | 482 | 0.1484 | - | | 1.5869 | 484 | 0.1403 | - | | 1.5934 | 486 | 0.1993 | - | | 1.6 | 488 | 0.1486 | - | | 1.6066 | 490 | 0.2899 | - | | 1.6131 | 492 | 0.2464 | - | | 1.6197 | 494 | 0.1352 | - | | 1.6262 | 496 | 0.1233 | - | | 1.6328 | 498 | 0.0413 | - | | 1.6393 | 500 | 0.091 | - | | 1.6459 | 502 | 0.0828 | - | | 1.6525 | 504 | 0.1488 | - | | 1.6590 | 506 | 0.1246 | - | | 1.6656 | 508 | 0.2795 | - | | 1.6721 | 510 | 0.067 | - | | 1.6787 | 512 | 0.168 | - | | 1.6852 | 514 | 0.2215 | - | | 1.6918 | 516 | 0.0854 | - | | 1.6984 | 518 | 0.2192 | - | | 1.7049 | 520 | 0.1479 | - | | 1.7115 | 522 | 0.1924 | - | | 1.7180 | 524 | 0.2075 | - | | 1.7246 | 526 | 0.208 | - | | 1.7311 | 528 | 0.1743 | - | | 1.7377 | 530 | 0.0817 | - | | 1.7443 | 532 | 0.1513 | - | | 1.7508 | 534 | 0.3422 | - | | 1.7574 | 536 | 0.1101 | - | | 1.7639 | 538 | 0.2815 | - | | 1.7672 | 539 | - | 0.0693 | | 1.7705 | 540 | 0.1837 | - | | 1.7770 | 542 | 0.0879 | - | | 1.7836 | 544 | 0.0746 | - | | 1.7902 | 546 | 0.2052 | - | | 1.7967 | 548 | 0.1416 | - | | 1.8033 | 550 | 0.1141 | - | | 1.8098 | 552 | 0.0312 | - | | 1.8164 | 554 | 0.139 | - | | 1.8230 | 556 | 0.1078 | - | | 1.8295 | 558 | 0.1302 | - | | 1.8361 | 560 | 0.0124 | - | | 1.8426 | 562 | 0.2641 | - | | 1.8492 | 564 | 0.1625 | - | | 1.8557 | 566 | 0.1907 | - | | 1.8623 | 568 | 0.0 | - | | 1.8689 | 570 | 0.1721 | - | | 1.8754 | 572 | 0.1178 | - | | 1.8820 | 574 | 0.0345 | - | | 1.8885 | 576 | 0.0924 | - | | 1.8951 | 578 | 0.0513 | - | | 1.9016 | 580 | 0.0929 | - | | 1.9082 | 582 | 0.1502 | - | | 1.9148 | 584 | 0.0338 | - | | 1.9213 | 586 | 0.1348 | - | | 1.9279 | 588 | 0.0297 | - | | 1.9344 | 590 | 0.0306 | - | | 1.9410 | 592 | 0.1416 | - | | 1.9475 | 594 | 0.0427 | - | | 1.9541 | 596 | 0.1916 | - | | 1.9607 | 598 | 0.1969 | - | | 1.9672 | 600 | 0.0765 | - | | 1.9738 | 602 | 0.1035 | - | | 1.9803 | 604 | 0.261 | - | | 1.9869 | 606 | 0.0845 | - | | 1.9934 | 608 | 0.0566 | - | | 2.0 | 610 | 0.704 | - | </details> ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.3 - PyTorch: 2.1.1+cu121 - Accelerate: 0.31.0 - Datasets: 2.17.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

jw-hf-test/jw4

jw-hf-test

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Ammartatox/sentientscribeGGUF

Ammartatox

transformers

--- base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - **Developed by:** Ammartatox - **License:** apache-2.0 - **Finetuned from model :** NousResearch/Nous-Hermes-2-Mistral-7B-DPO This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

hasininawoda/output1

hasininawoda

diffusers

text-to-image

--- base_model: CompVis/stable-diffusion-v1-4 library_name: diffusers license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - hasininawoda/output1 These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the None dataset. You can find some example images in the following. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

efeno/RAFT_biomedical_60_PEFT

efeno

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

debiao29/Qwen-Qwen1.5-0.5B-1719937379

debiao29

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) TeenyTinyLlama-460m - GGUF - Model creator: https://huggingface.co/nicholasKluge/ - Original model: https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m/ | Name | Quant method | Size | | ---- | ---- | ---- | | [TeenyTinyLlama-460m.Q2_K.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q2_K.gguf) | Q2_K | 0.17GB | | [TeenyTinyLlama-460m.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.IQ3_XS.gguf) | IQ3_XS | 0.19GB | | [TeenyTinyLlama-460m.IQ3_S.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.IQ3_S.gguf) | IQ3_S | 0.2GB | | [TeenyTinyLlama-460m.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q3_K_S.gguf) | Q3_K_S | 0.2GB | | [TeenyTinyLlama-460m.IQ3_M.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.IQ3_M.gguf) | IQ3_M | 0.21GB | | [TeenyTinyLlama-460m.Q3_K.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q3_K.gguf) | Q3_K | 0.22GB | | [TeenyTinyLlama-460m.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q3_K_M.gguf) | Q3_K_M | 0.22GB | | [TeenyTinyLlama-460m.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q3_K_L.gguf) | Q3_K_L | 0.24GB | | [TeenyTinyLlama-460m.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.IQ4_XS.gguf) | IQ4_XS | 0.24GB | | [TeenyTinyLlama-460m.Q4_0.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q4_0.gguf) | Q4_0 | 0.25GB | | [TeenyTinyLlama-460m.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.IQ4_NL.gguf) | IQ4_NL | 0.26GB | | [TeenyTinyLlama-460m.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q4_K_S.gguf) | Q4_K_S | 0.26GB | | [TeenyTinyLlama-460m.Q4_K.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q4_K.gguf) | Q4_K | 0.27GB | | [TeenyTinyLlama-460m.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q4_K_M.gguf) | Q4_K_M | 0.27GB | | [TeenyTinyLlama-460m.Q4_1.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q4_1.gguf) | Q4_1 | 0.28GB | | [TeenyTinyLlama-460m.Q5_0.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q5_0.gguf) | Q5_0 | 0.3GB | | [TeenyTinyLlama-460m.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q5_K_S.gguf) | Q5_K_S | 0.3GB | | [TeenyTinyLlama-460m.Q5_K.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q5_K.gguf) | Q5_K | 0.31GB | | [TeenyTinyLlama-460m.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q5_K_M.gguf) | Q5_K_M | 0.31GB | | [TeenyTinyLlama-460m.Q5_1.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q5_1.gguf) | Q5_1 | 0.33GB | | [TeenyTinyLlama-460m.Q6_K.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q6_K.gguf) | Q6_K | 0.36GB | | [TeenyTinyLlama-460m.Q8_0.gguf](https://huggingface.co/RichardErkhov/nicholasKluge_-_TeenyTinyLlama-460m-gguf/blob/main/TeenyTinyLlama-460m.Q8_0.gguf) | Q8_0 | 0.46GB | Original model description: --- language: - pt license: apache-2.0 library_name: transformers tags: - text-generation-inference datasets: - nicholasKluge/Pt-Corpus-Instruct metrics: - perplexity pipeline_tag: text-generation widget: - text: 'A PUCRS é uma universidade ' example_title: Exemplo - text: A muitos anos atrás, em uma galáxia muito distante, vivia uma raça de example_title: Exemplo - text: Em meio a um escândalo, a frente parlamentar pediu ao Senador Silva para example_title: Exemplo inference: parameters: repetition_penalty: 1.2 temperature: 0.2 top_k: 20 top_p: 0.2 max_new_tokens: 150 co2_eq_emissions: emissions: 41100 source: CodeCarbon training_type: pre-training geographical_location: Germany hardware_used: NVIDIA A100-SXM4-40GB model-index: - name: TeenyTinyLlama-460m results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 20.15 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 25.73 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 27.02 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 53.61 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 13.0 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 46.41 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 33.59 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 22.99 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia-temp/tweetsentbr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 17.28 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m name: Open Portuguese LLM Leaderboard --- # TeenyTinyLlama-460m <img src="./logo.png" alt="A curious llama exploring a mushroom forest." height="200"> ## Model Summary Large language models (LLMs) have significantly advanced natural language processing, but their progress has yet to be equal across languages. While most LLMs are trained in high-resource languages like English, multilingual models generally underperform monolingual ones. Additionally, aspects of their multilingual foundation sometimes restrict the byproducts they produce, like computational demands and licensing regimes. Hence, we developed the _TeenyTinyLlama_ pair: two compact models for Brazilian Portuguese text generation. Read our preprint on [Article](https://www.sciencedirect.com/science/article/pii/S2666827024000343). ## Details - **Architecture:** a Transformer-based model pre-trained via causal language modeling - **Size:** 468,239,360 parameters - **Context length:** 2048 tokens - **Dataset:** [Pt-Corpus Instruct](https://huggingface.co/datasets/nicholasKluge/Pt-Corpus-Instruct) (6.2B tokens) - **Language:** Portuguese - **Number of steps:** 1,200,000 - **GPU:** 1 NVIDIA A100-SXM4-40GB - **Training time**: ~ 280 hours - **Emissions:** 41.1 KgCO2 (Germany) - **Total energy consumption:** 115.69 kWh This repository has the [source code](https://github.com/Nkluge-correa/TeenyTinyLlama) used to train this model. The main libraries used are: - [Transformers](https://github.com/huggingface/transformers) - [PyTorch](https://github.com/pytorch/pytorch) - [Datasets](https://github.com/huggingface/datasets) - [Tokenizers](https://github.com/huggingface/tokenizers) - [Sentencepiece](https://github.com/google/sentencepiece) - [Accelerate](https://github.com/huggingface/accelerate) - [FlashAttention](https://github.com/Dao-AILab/flash-attention) - [Codecarbon](https://github.com/mlco2/codecarbon) ## Intended Uses The primary intended use of TeenyTinyLlama is to research the challenges related to developing language models for low-resource languages. Checkpoints saved during training are intended to provide a controlled setting for performing scientific experiments. You may also further fine-tune and adapt TeenyTinyLlama for deployment, as long as your use is following the Apache 2.0 license. If you decide to use pre-trained TeenyTinyLlama as a basis for your fine-tuned model, please conduct your own risk and bias assessment. ## Out-of-scope Use TeenyTinyLlama is not intended for deployment. It is not a product and should not be used for human-facing interactions. TeenyTinyLlama models are Brazilian Portuguese language only and are not suitable for translation or generating text in other languages. TeenyTinyLlama has not been fine-tuned for downstream contexts in which language models are commonly deployed. ## Basic usage Using the `pipeline`: ```python from transformers import pipeline generator = pipeline("text-generation", model="nicholasKluge/TeenyTinyLlama-460m") completions = generator("Astronomia é a ciência", num_return_sequences=2, max_new_tokens=100) for comp in completions: print(f"🤖 {comp['generated_text']}") ``` Using the `AutoTokenizer` and `AutoModelForCausalLM`: ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Load model and the tokenizer tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-460m", revision='main') model = AutoModelForCausalLM.from_pretrained("nicholasKluge/TeenyTinyLlama-460m", revision='main') # Pass the model to your device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.eval() model.to(device) # Tokenize the inputs and pass them to the device inputs = tokenizer("Astronomia é a ciência", return_tensors="pt").to(device) # Generate some text completions = model.generate(**inputs, num_return_sequences=2, max_new_tokens=100) # Print the generated text for i, completion in enumerate(completions): print(f'🤖 {tokenizer.decode(completion)}') ``` ## Limitations Like almost all other language models trained on large text datasets scraped from the web, the TTL pair exhibited behavior that does not make them an out-of-the-box solution to many real-world applications, especially those requiring factual, reliable, nontoxic text generation. Our models are all subject to the following: - **Hallucinations:** This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination. - **Biases and Toxicity:** This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities. - **Unreliable Code:** The model may produce incorrect code snippets and statements. These code generations should not be treated as suggestions or accurate solutions. - **Language Limitations:** The model is primarily designed to understand standard Brazilian Portuguese. Other languages might challenge its comprehension, leading to potential misinterpretations or errors in response. - **Repetition and Verbosity:** The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given. Hence, even though our models are released with a permissive license, we urge users to perform their risk analysis on these models if intending to use them for real-world applications and also have humans moderating the outputs of these models in applications where they will interact with an audience, guaranteeing users are always aware they are interacting with a language model. ## Evaluations During our training runs, both models showed consistent convergence. At no point did our evaluation curves show signs of overfitting or saturation. In the case of our 460m parameter model, we intentionally trained past the optimal point by approximately 75,000 steps to assess if there were any signs of saturation, but our evaluations consistently gave better results. We hypothesize that our models are under-trained but can improve if further trained to pass the Chinchilla optimal range. | Processed Tokens | Perplexity | Energy Consumption (kWh) | Emissions (KgCO2eq) | |------------------|------------|---------------------------|----------------------| | 8.1M | 20.49 | 9.40 | 3.34 | | 1.6B | 16.90 | 18.82 | 6.70 | | 2.4B | 15.43 | 28.59 | 10.16 | | 3.2B | 14.64 | 38.20 | 13.57 | | 4.0B | 14.08 | 48.04 | 17.07 | | 4.9B | 13.61 | 57.74 | 20.52 | | 5.7B | 13.25 | 67.32 | 23.92 | | 6.5B | 12.87 | 76.84 | 27.30 | | 7.3B | 12.57 | 86.40 | 30.70 | | 8.1B | 12.27 | 96.19 | 34.18 | | 9.0B | 11.96 | 106.06 | 37.70 | | 9.8B | 11.77 | 115.69 | 41.31 | ## Benchmarks Evaluations on benchmarks were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)). [Laiviet](https://github.com/laiviet/lm-evaluation-harness) translated the tasks from the LM-Evaluation-Harness we used. The results of models marked with an "*" were extracted from the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). | | **ARC** | **HellaSwag** | **MMLU** | **TruthfulQA** | **Average** | |------------------|-----------|---------------|-----------|----------------|-------------| | Pythia-410m | 24.83* | 41.29* | 25.99* | 40.95* | 33.26 | | **TTL-460m** | 29.40 | 33.00 | 28.55 | 41.10 | 33.01 | | Bloom-560m | 24.74* | 37.15* | 24.22* | 42.44* | 32.13 | | Xglm-564M | 25.56 | 34.64* | 25.18* | 42.53 | 31.97 | | OPT-350m | 23.55* | 36.73* | 26.02* | 40.83* | 31.78 | | **TTL-160m** | 26.15 | 29.29 | 28.11 | 41.12 | 31.16 | | Pythia-160m | 24.06* | 31.39* | 24.86* | 44.34* | 31.16 | | OPT-125m | 22.87* | 31.47* | 26.02* | 42.87* | 30.80 | | GPorTuguese-2 | 22.48 | 29.62 | 27.36 | 41.44 | 30.22 | | Gpt2-small | 21.48* | 31.60* | 25.79* | 40.65* | 29.97 | | Multilingual GPT | 23.81 | 26.37* | 25.17* | 39.62 | 28.73 | Evaluations on Brazilian Portuguese benchmarks were performed using a [Portuguese implementation of the EleutherAI LM Evaluation Harness](https://github.com/eduagarcia/lm-evaluation-harness-pt) (created by [Eduardo Garcia](https://github.com/eduagarcia/lm-evaluation-harness-pt)). | | **ASSIN2 RTE** | **ASSIN2 STS** | **BLUEX** | **ENEM** | **FAQUAD NLI** | **HateBR** | **OAB Exams** | **Average** | |----------------|----------------|----------------|-----------|----------|----------------|------------|---------------|-------------| | Qwen-1.8B | 64.83 | 19.53 | 26.15 | 30.23 | 43.97 | 33.33 | 27.20 | 35.03 | | TinyLlama-1.1B | 58.93 | 13.57 | 22.81 | 22.25 | 43.97 | 36.92 | 23.64 | 31.72 | | **TTL-460m** | 53.93 | 12.66 | 22.81 | 19.87 | 49.01 | 33.59 | 27.06 | 31.27 | | XGLM-564m | 49.61 | 22.91 | 19.61 | 19.38 | 43.97 | 33.99 | 23.42 | 30.41 | | Bloom-1b7 | 53.60 | 4.81 | 21.42 | 18.96 | 43.97 | 34.89 | 23.05 | 28.67 | | **TTL-160m** | 53.36 | 2.58 | 21.84 | 18.75 | 43.97 | 36.88 | 22.60 | 28.56 | | OPT-125m | 39.77 | 2.00 | 21.84 | 17.42 | 43.97 | 47.04 | 22.78 | 27.83 | | Pythia-160 | 33.33 | 12.81 | 16.13 | 16.66 | 50.36 | 41.09 | 22.82 | 27.60 | | OLMo-1b | 34.12 | 9.28 | 18.92 | 20.29 | 43.97 | 41.33 | 22.96 | 27.26 | | Bloom-560m | 33.33 | 8.48 | 18.92 | 19.03 | 43.97 | 37.07 | 23.05 | 26.26 | | Pythia-410m | 33.33 | 4.80 | 19.47 | 19.45 | 43.97 | 33.33 | 23.01 | 25.33 | | OPT-350m | 33.33 | 3.65 | 20.72 | 17.35 | 44.71 | 33.33 | 23.01 | 25.15 | | GPT-2 small | 33.26 | 0.00 | 10.43 | 11.20 | 43.52 | 33.68 | 13.12 | 20.74 | | GPorTuguese | 33.33 | 3.85 | 14.74 | 3.01 | 28.81 | 33.33 | 21.23 | 19.75 | | Samba-1.1B | 33.33 | 1.30 | 8.07 | 10.22 | 17.72 | 35.79 | 15.03 | 17.35 | ## Fine-Tuning Comparisons To further evaluate the downstream capabilities of our models, we decided to employ a basic fine-tuning procedure for our TTL pair on a subset of tasks from the Poeta benchmark. We apply the same procedure for comparison purposes on both [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) models, given that they are also LLM trained from scratch in Brazilian Portuguese and have a similar size range to our models. We used these comparisons to assess if our pre-training runs produced LLM capable of producing good results ("good" here means "close to BERTimbau") when utilized for downstream applications. | Models | IMDB | FaQuAD-NLI | HateBr | Assin2 | AgNews | Average | |-----------------|-----------|------------|-----------|-----------|-----------|---------| | BERTimbau-large | **93.58** | 92.26 | 91.57 | **88.97** | 94.11 | 92.10 | | BERTimbau-small | 92.22 | **93.07** | 91.28 | 87.45 | 94.19 | 91.64 | | **TTL-460m** | 91.64 | 91.18 | **92.28** | 86.43 | **94.42** | 91.19 | | **TTL-160m** | 91.14 | 90.00 | 90.71 | 85.78 | 94.05 | 90.34 | All the shown results are the higher accuracy scores achieved on the respective task test sets after fine-tuning the models on the training sets. All fine-tuning runs used the same hyperparameters, and the code implementation can be found in the [model cards](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m-HateBR) of our fine-tuned models. ## Cite as 🤗 ```latex @misc{correa24ttllama, title = {TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese}, author = {Corr{\^e}a, Nicholas Kluge and Falk, Sophia and Fatimah, Shiza and Sen, Aniket and De Oliveira, Nythamar}, journal={arXiv preprint arXiv:2401.16640}, year={2024} } @misc{correa24ttllama, doi = {10.1016/j.mlwa.2024.100558}, url = {https://www.sciencedirect.com/science/article/pii/S2666827024000343}, title = {TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese}, author = {Corr{\^e}a, Nicholas Kluge and Falk, Sophia and Fatimah, Shiza and Sen, Aniket and De Oliveira, Nythamar}, journal={Machine Learning With Applications}, publisher = {Springer}, year={2024} } ``` ## Funding This repository was built as part of the RAIES ([Rede de Inteligência Artificial Ética e Segura](https://www.raies.org/)) initiative, a project supported by FAPERGS - ([Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul](https://fapergs.rs.gov.br/inicial)), Brazil. ## License TeenyTinyLlama-460m is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.

CarlosJefte/gemma-7b-bnb-4bit

CarlosJefte

transformers

text-generation

--- base_model: unsloth/gemma-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft --- # Uploaded model - **Developed by:** CarlosJefte - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

karpathy/gpt2_1558M_final2_hf

karpathy

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lucasbalponti/split6

lucasbalponti

transformers

text-classification

--- license: mit base_model: neuralmind/bert-large-portuguese-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: split6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # split6 This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1595 - Accuracy: 0.9586 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.29 | 1.0 | 8509 | 0.1733 | 0.9394 | | 0.2369 | 2.0 | 17018 | 0.1300 | 0.9642 | | 0.2126 | 3.0 | 25527 | 0.1595 | 0.9586 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu118 - Datasets 2.20.0 - Tokenizers 0.19.1

hassanaitnacer/wav2vec2-large-xlsr-moroccan-darija-v1

hassanaitnacer

transformers

automatic-speech-recognition

Entry not found

allSafe101/test

allSafe101

Entry not found

rhuang1/fraud-classification-18-llama-2-7b

rhuang1

transformers

text-generation

Entry not found

somashekar2002/LLM-for-quiz-gen

somashekar2002

Entry not found

RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Tiny-Pirate-1.1b-v0.1 - GGUF - Model creator: https://huggingface.co/phanerozoic/ - Original model: https://huggingface.co/phanerozoic/Tiny-Pirate-1.1b-v0.1/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Tiny-Pirate-1.1b-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q2_K.gguf) | Q2_K | 0.4GB | | [Tiny-Pirate-1.1b-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.IQ3_XS.gguf) | IQ3_XS | 0.44GB | | [Tiny-Pirate-1.1b-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.IQ3_S.gguf) | IQ3_S | 0.47GB | | [Tiny-Pirate-1.1b-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q3_K_S.gguf) | Q3_K_S | 0.47GB | | [Tiny-Pirate-1.1b-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.IQ3_M.gguf) | IQ3_M | 0.48GB | | [Tiny-Pirate-1.1b-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q3_K.gguf) | Q3_K | 0.51GB | | [Tiny-Pirate-1.1b-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q3_K_M.gguf) | Q3_K_M | 0.51GB | | [Tiny-Pirate-1.1b-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q3_K_L.gguf) | Q3_K_L | 0.55GB | | [Tiny-Pirate-1.1b-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.IQ4_XS.gguf) | IQ4_XS | 0.57GB | | [Tiny-Pirate-1.1b-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q4_0.gguf) | Q4_0 | 0.59GB | | [Tiny-Pirate-1.1b-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.IQ4_NL.gguf) | IQ4_NL | 0.6GB | | [Tiny-Pirate-1.1b-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q4_K_S.gguf) | Q4_K_S | 0.6GB | | [Tiny-Pirate-1.1b-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q4_K.gguf) | Q4_K | 0.62GB | | [Tiny-Pirate-1.1b-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q4_K_M.gguf) | Q4_K_M | 0.62GB | | [Tiny-Pirate-1.1b-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q4_1.gguf) | Q4_1 | 0.65GB | | [Tiny-Pirate-1.1b-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q5_0.gguf) | Q5_0 | 0.71GB | | [Tiny-Pirate-1.1b-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q5_K_S.gguf) | Q5_K_S | 0.71GB | | [Tiny-Pirate-1.1b-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q5_K.gguf) | Q5_K | 0.73GB | | [Tiny-Pirate-1.1b-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q5_K_M.gguf) | Q5_K_M | 0.73GB | | [Tiny-Pirate-1.1b-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q5_1.gguf) | Q5_1 | 0.77GB | | [Tiny-Pirate-1.1b-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q6_K.gguf) | Q6_K | 0.84GB | | [Tiny-Pirate-1.1b-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/phanerozoic_-_Tiny-Pirate-1.1b-v0.1-gguf/blob/main/Tiny-Pirate-1.1b-v0.1.Q8_0.gguf) | Q8_0 | 1.09GB | Original model description: --- license: cc-by-nc-4.0 language: - en widget: - text: | What is best in life? example_title: "Healthy Eating Tips" ---  # Tiny-Pirate-1.1b-v0.1 Tiny-Pirate-1.1b-v0.1 is a compact and specialized language model designed for generating authentic pirate-themed content. This version is fine-tuned from the TinyLlama-1.1B model, specifically adapted to operate efficiently in CPU-only and resource-limited environments. - **Developed by**: phanerozoic - **License**: cc-by-nc-4.0 - **Finetuned from**: TinyLlama-1.1B ### Version Control Introducing Tiny-Pirate-1.1b-v0.1 to mark the initial release of this specialized language model. ### Performance The Tiny-Pirate-1.1B model exhibits a robust ability to generate pirate-themed content, demonstrating a strong grasp of pirate vernacular and thematic elements. The responses are notably coherent and contextually appropriate, reflecting the model's adeptness at maintaining a consistent pirate tone. However, there are instances where the responses could benefit from more precise and direct answers to the questions posed, suggesting a potential area for further fine-tuning. ### Direct Use Ideal for applications requiring thematic language generation in resource-constrained environments, such as edge computing, mobile devices, and lightweight AI applications. ### Training Data Utilized the same pirate-themed dataset as MistralPirate-7b-v0.3, ensuring rich and diverse inputs for fine-tuning. ### Custom Stopping Strings To enhance output quality, the following custom stopping strings were employed: - "}," - "User:" - "You:" - "\nUser" - "\nUser:" - "me:" - ""\n" ### Training Hyperparameters and Fine-Tuning Details - **LoRA Rank**: 16 - **LoRA Alpha**: 32 - **True Batch Size**: 4 - **Gradient Accumulation Steps**: 1 - **Epochs**: 1 - **Learning Rate**: 3e-4 - **LR Scheduler**: Linear - **LLaMA Target Projections**: All targets modified - **Fine-Tuning Approach**: LoRA peft merged back into the base model ### Limitations While adept at generating pirate-themed content, Tiny-Pirate-v0.1 may not handle highly complex language tasks as larger models do. Its specialization in pirate dialect limits its use in general language applications. ### Compute Infrastructure Efficiently trained on an RTX 6000 Ada GPU, taking approximately 2-3 minutes, showcasing resource-effective training for specialized models. ### Results The model successfully produced responses that are thematically aligned with typical pirate lore and language. The outputs are engaging and largely relevant to the queries, showcasing the model's capacity to handle a variety of pirate-related topics from navigation to mythology. The use of pirate dialect is consistent and immersive, contributing to the overall thematic experience. However, the depth of responses varies, indicating room for improvement in handling more complex queries or providing more detailed explanations. ### Summary Tiny-Pirate-1.1B stands out as an effective tool for generating pirate-themed content, particularly suitable for applications where thematic consistency and lighter computational demands are key. While the model shows competence in creating thematically rich and linguistically coherent outputs, there is potential for enhancing its ability to handle complex scenarios and provide more detailed, context-specific responses. Overall, Tiny-Pirate-1.1B represents a promising step in the realm of specialized, lightweight language models, combining thematic accuracy with operational efficiency. ### Acknowledgments Gratitude is extended to the developers of TinyLlama-1.1B for their foundational work, which was instrumental in the creation of Tiny-Pirate-v0.1.

RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Fimbulvetr-10.7B-v1 - GGUF - Model creator: https://huggingface.co/Sao10K/ - Original model: https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Fimbulvetr-10.7B-v1.Q2_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q2_K.gguf) | Q2_K | 3.73GB | | [Fimbulvetr-10.7B-v1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.IQ3_XS.gguf) | IQ3_XS | 4.14GB | | [Fimbulvetr-10.7B-v1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.IQ3_S.gguf) | IQ3_S | 4.37GB | | [Fimbulvetr-10.7B-v1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q3_K_S.gguf) | Q3_K_S | 4.34GB | | [Fimbulvetr-10.7B-v1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.IQ3_M.gguf) | IQ3_M | 4.51GB | | [Fimbulvetr-10.7B-v1.Q3_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q3_K.gguf) | Q3_K | 4.84GB | | [Fimbulvetr-10.7B-v1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q3_K_M.gguf) | Q3_K_M | 4.84GB | | [Fimbulvetr-10.7B-v1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q3_K_L.gguf) | Q3_K_L | 5.26GB | | [Fimbulvetr-10.7B-v1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.IQ4_XS.gguf) | IQ4_XS | 5.43GB | | [Fimbulvetr-10.7B-v1.Q4_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q4_0.gguf) | Q4_0 | 5.66GB | | [Fimbulvetr-10.7B-v1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.IQ4_NL.gguf) | IQ4_NL | 5.72GB | | [Fimbulvetr-10.7B-v1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q4_K_S.gguf) | Q4_K_S | 5.7GB | | [Fimbulvetr-10.7B-v1.Q4_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q4_K.gguf) | Q4_K | 6.02GB | | [Fimbulvetr-10.7B-v1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q4_K_M.gguf) | Q4_K_M | 6.02GB | | [Fimbulvetr-10.7B-v1.Q4_1.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q4_1.gguf) | Q4_1 | 6.27GB | | [Fimbulvetr-10.7B-v1.Q5_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q5_0.gguf) | Q5_0 | 6.89GB | | [Fimbulvetr-10.7B-v1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q5_K_S.gguf) | Q5_K_S | 6.89GB | | [Fimbulvetr-10.7B-v1.Q5_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q5_K.gguf) | Q5_K | 7.08GB | | [Fimbulvetr-10.7B-v1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q5_K_M.gguf) | Q5_K_M | 7.08GB | | [Fimbulvetr-10.7B-v1.Q5_1.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q5_1.gguf) | Q5_1 | 7.51GB | | [Fimbulvetr-10.7B-v1.Q6_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q6_K.gguf) | Q6_K | 8.2GB | | [Fimbulvetr-10.7B-v1.Q8_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Fimbulvetr-10.7B-v1-gguf/blob/main/Fimbulvetr-10.7B-v1.Q8_0.gguf) | Q8_0 | 10.62GB | Original model description: --- license: cc-by-nc-4.0 language: - en --- My current low-budget daily driver, so far. Frostwindv2 + Sensualize v1.1 + ___ data on uncen Instruct Solar. This is meant to be a verbose, smart Roleplaying model. I think I captured those two parts this time. Well, for my own cards and scenarios anyway, it passed with flying colours. I recommend using min-p, I liked Universal-Light preset in SillyTavern. Experimental. *** ### Prompt Format: Alpaca ``` ### Instruction: <Prompt> ### Input: <Insert Context Here> ### Response: ``` ### 31-Jan-24 Update: forgot to link GGUF quants here: https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1-GGUF or thebloke already quantized them to gptq and others. lonestriker did exl2 quants so ty ty a lot. anyway, I have a few solar-based ideas before I move to the new InternLM, Yi, Mixtral or back to 70B.

isanthosh2004/llama3-fake-news

isanthosh2004

transformers

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** isanthosh2004 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

PereLluis13/relik-reader-deberta-large-wikipedia-aida-full-interleave-cont

PereLluis13

transformers

feature-extraction

Entry not found

styalai/XTmath-0.2b

styalai

transformers

--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

PereLluis13/relik-entity-linking-large-wikipedia-aida-interleave-cont

PereLluis13

Entry not found

nalf3in/gemma-2-9b-Q4_K_M-GGUF

nalf3in

transformers

text-generation

--- base_model: google/gemma-2-9b library_name: transformers license: gemma pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license --- # nalf3in/gemma-2-9b-Q4_K_M-GGUF This model was converted to GGUF format from [`google/gemma-2-9b`](https://huggingface.co/google/gemma-2-9b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/google/gemma-2-9b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo nalf3in/gemma-2-9b-Q4_K_M-GGUF --hf-file gemma-2-9b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo nalf3in/gemma-2-9b-Q4_K_M-GGUF --hf-file gemma-2-9b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo nalf3in/gemma-2-9b-Q4_K_M-GGUF --hf-file gemma-2-9b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo nalf3in/gemma-2-9b-Q4_K_M-GGUF --hf-file gemma-2-9b-q4_k_m.gguf -c 2048 ```

InfiniteEcho/q-FrozenLake-v1-4x4-noSlippery

InfiniteEcho

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="InfiniteEcho/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-100hr-v2

KasuleTrevor

transformers

automatic-speech-recognition

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

juanpablomesa/bge-small-bioasq-batch64

juanpablomesa

sentence-transformers

sentence-similarity

--- base_model: BAAI/bge-small-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:4012 - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'Extensive messenger RNA editing generates transcript and protein diversity in genes involved in neural excitability, as previously described, as well as in genes participating in a broad range of other cellular functions. ' sentences: - Do cephalopods use RNA editing less frequently than other species? - GV1001 vaccine targets which enzyme? - Which event results in the acetylation of S6K1? - source_sentence: Yes, exposure to household furry pets influences the gut microbiota of infants. sentences: - Can pets affect infant microbiomed? - What is the mode of action of Thiazovivin? - What are the effects of CAMK4 inhibition? - source_sentence: "In children with heart failure evidence of the effect of enalapril\ \ is empirical. Enalapril was clinically safe and effective in 50% to 80% of for\ \ children with cardiac failure secondary to congenital heart malformations before\ \ and after cardiac surgery, impaired ventricular function , valvar regurgitation,\ \ congestive cardiomyopathy, , arterial hypertension, life-threatening arrhythmias\ \ coexisting with circulatory insufficiency. \nACE inhibitors have shown a transient\ \ beneficial effect on heart failure due to anticancer drugs and possibly a beneficial\ \ effect in muscular dystrophy-associated cardiomyopathy, which deserves further\ \ studies." sentences: - Which receptors can be evaluated with the [18F]altanserin? - In what proportion of children with heart failure has Enalapril been shown to be safe and effective? - Which major signaling pathways are regulated by RIP1? - source_sentence: Cellular senescence-associated heterochromatic foci (SAHFS) are a novel type of chromatin condensation involving alterations of linker histone H1 and linker DNA-binding proteins. SAHFS can be formed by a variety of cell types, but their mechanism of action remains unclear. sentences: - What is the relationship between the X chromosome and a neutrophil drumstick? - Which microRNAs are involved in exercise adaptation? - How are SAHFS created? - source_sentence: Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss of all three clusters (tricluster deletion) led to a severe axonal arborization defect and loss of self-avoidance. sentences: - What are the effects of the deletion of all three Pcdh clusters (tricluster deletion) in mice? - what is the role of MEF-2 in cardiomyocyte differentiation? - How many periods of regulatory innovation led to the evolution of vertebrates? --- # BGE small finetuned BIOASQ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("juanpablomesa/bge-small-bioasq-batch64") # Run inference sentences = [ 'Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss of all three clusters (tricluster deletion) led to a severe axonal arborization defect and loss of self-avoidance.', 'What are the effects of the deletion of all three Pcdh clusters (tricluster deletion) in mice?', 'How many periods of regulatory innovation led to the evolution of vertebrates?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 4,012 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 63.38 tokens</li><li>max: 485 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.13 tokens</li><li>max: 49 tokens</li></ul> | * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | <code>Aberrant patterns of H3K4, H3K9, and H3K27 histone lysine methylation were shown to result in histone code alterations, which induce changes in gene expression, and affect the proliferation rate of cells in medulloblastoma.</code> | <code>What is the implication of histone lysine methylation in medulloblastoma?</code> | | <code>STAG1/STAG2 proteins are tumour suppressor proteins that suppress cell proliferation and are essential for differentiation.</code> | <code>What is the role of STAG1/STAG2 proteins in differentiation?</code> | | <code>The association between cell phone use and incident glioblastoma remains unclear. Some studies have reported that cell phone use was associated with incident glioblastoma, and with reduced survival of patients diagnosed with glioblastoma. However, other studies have repeatedly replicated to find an association between cell phone use and glioblastoma.</code> | <code>What is the association between cell phone use and glioblastoma?</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Framework Versions - Python: 3.11.5 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3-11B-Instruct-v0.1 - GGUF - Model creator: https://huggingface.co/MaziyarPanahi/ - Original model: https://huggingface.co/MaziyarPanahi/Llama-3-11B-Instruct-v0.1/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Llama-3-11B-Instruct-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q2_K.gguf) | Q2_K | 4.16GB | | [Llama-3-11B-Instruct-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.IQ3_XS.gguf) | IQ3_XS | 4.61GB | | [Llama-3-11B-Instruct-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.IQ3_S.gguf) | IQ3_S | 4.83GB | | [Llama-3-11B-Instruct-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 4.81GB | | [Llama-3-11B-Instruct-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.IQ3_M.gguf) | IQ3_M | 4.98GB | | [Llama-3-11B-Instruct-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q3_K.gguf) | Q3_K | 5.3GB | | [Llama-3-11B-Instruct-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 5.3GB | | [Llama-3-11B-Instruct-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 5.73GB | | [Llama-3-11B-Instruct-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 5.93GB | | [Llama-3-11B-Instruct-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q4_0.gguf) | Q4_0 | 6.17GB | | [Llama-3-11B-Instruct-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.IQ4_NL.gguf) | IQ4_NL | 6.23GB | | [Llama-3-11B-Instruct-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 6.21GB | | [Llama-3-11B-Instruct-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q4_K.gguf) | Q4_K | 6.53GB | | [Llama-3-11B-Instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 6.53GB | | [Llama-3-11B-Instruct-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q4_1.gguf) | Q4_1 | 6.81GB | | [Llama-3-11B-Instruct-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q5_0.gguf) | Q5_0 | 7.45GB | | [Llama-3-11B-Instruct-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q5_K_S.gguf) | Q5_K_S | 7.45GB | | [Llama-3-11B-Instruct-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q5_K.gguf) | Q5_K | 7.64GB | | [Llama-3-11B-Instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 7.64GB | | [Llama-3-11B-Instruct-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q5_1.gguf) | Q5_1 | 8.09GB | | [Llama-3-11B-Instruct-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q6_K.gguf) | Q6_K | 8.81GB | | [Llama-3-11B-Instruct-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_Llama-3-11B-Instruct-v0.1-gguf/blob/main/Llama-3-11B-Instruct-v0.1.Q8_0.gguf) | Q8_0 | 11.41GB | Original model description: --- base_model: "meta-llama/Meta-Llama-3-8B-Instruct" library_name: transformers tags: - mergekit - merge - facebook - meta - pytorch - llama - llama-3 language: - en pipeline_tag: text-generation license: other license_name: llama3 license_link: LICENSE inference: false model_creator: MaziyarPanahi model_name: Llama-3-11B-Instruct-v0.1 quantized_by: MaziyarPanahi --- <img src="./llama-3-merges.webp" alt="Goku 8x22B v0.1 Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Llama-3-11B-Instruct-v0.1 This model is a self-merge of `meta-llama/Meta-Llama-3-8B-Instruct` model. # How to use You can use this model by using `MaziyarPanahi/Llama-3-11B-Instruct-v0.1` as the model name in Hugging Face's transformers library. ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer from transformers import pipeline import torch model_id = "MaziyarPanahi/Llama-3-11B-Instruct-v0.1" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True, # attn_implementation="flash_attention_2" ) tokenizer = AutoTokenizer.from_pretrained( model_id, trust_remote_code=True ) streamer = TextStreamer(tokenizer) pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, model_kwargs={"torch_dtype": torch.bfloat16}, streamer=streamer ) # Then you can use the pipeline to generate text. messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.95, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Prompt template ```text <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|> what's 25-4*2+3<|eot_id|><|start_header_id|>assistant<|end_header_id|> To evaluate this expression, we need to follow the order of operations (PEMDAS): 1. First, multiply 4 and 2: 4*2 = 8 2. Then, subtract 8 from 25: 25 - 8 = 17 3. Finally, add 3: 17 + 3 = 20 So, 25-4*2+3 = 20!<|eot_id|> To evaluate this expression, we need to follow the order of operations (PEMDAS): 1. First, multiply 4 and 2: 4*2 = 8 2. Then, subtract 8 from 25: 25 - 8 = 17 3. Finally, add 3: 17 + 3 = 20 So, 25-4*2+3 = 20! ```

InfiniteEcho/Taxi-v3

InfiniteEcho

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.78 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="InfiniteEcho/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

orangeX/TEST

orangeX

--- license: openrail ---

KUD-genai/TAIDE_healthedu_v6_gguf

KUD-genai

Entry not found

manbeast3b/ZZZZZZZZdriver136c

manbeast3b

transformers

text-generation

Entry not found

gisang-lee/mistral-7b-qlora-arc-wandb-test-arc-challenge-train-val

gisang-lee

transformers

text-generation

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hungkvbn/naschainhk6

hungkvbn

Entry not found

dyada/mistral-Multiclass-company-industry-V0

dyada

transformers

text-classification

Entry not found

mradermacher/Nethena-13B-GGUF

mradermacher

transformers

--- base_model: NeverSleep/Nethena-13B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/NeverSleep/Nethena-13B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.IQ3_XS.gguf) | IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.IQ3_M.gguf) | IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Nethena-13B-GGUF/resolve/main/Nethena-13B.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) TinyLlama-1.1B-2.5T-chat - GGUF - Model creator: https://huggingface.co/AIGym/ - Original model: https://huggingface.co/AIGym/TinyLlama-1.1B-2.5T-chat/ | Name | Quant method | Size | | ---- | ---- | ---- | | [TinyLlama-1.1B-2.5T-chat.Q2_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q2_K.gguf) | Q2_K | 0.4GB | | [TinyLlama-1.1B-2.5T-chat.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.IQ3_XS.gguf) | IQ3_XS | 0.44GB | | [TinyLlama-1.1B-2.5T-chat.IQ3_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.IQ3_S.gguf) | IQ3_S | 0.47GB | | [TinyLlama-1.1B-2.5T-chat.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q3_K_S.gguf) | Q3_K_S | 0.47GB | | [TinyLlama-1.1B-2.5T-chat.IQ3_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.IQ3_M.gguf) | IQ3_M | 0.48GB | | [TinyLlama-1.1B-2.5T-chat.Q3_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q3_K.gguf) | Q3_K | 0.51GB | | [TinyLlama-1.1B-2.5T-chat.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q3_K_M.gguf) | Q3_K_M | 0.51GB | | [TinyLlama-1.1B-2.5T-chat.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q3_K_L.gguf) | Q3_K_L | 0.55GB | | [TinyLlama-1.1B-2.5T-chat.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.IQ4_XS.gguf) | IQ4_XS | 0.57GB | | [TinyLlama-1.1B-2.5T-chat.Q4_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q4_0.gguf) | Q4_0 | 0.59GB | | [TinyLlama-1.1B-2.5T-chat.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.IQ4_NL.gguf) | IQ4_NL | 0.6GB | | [TinyLlama-1.1B-2.5T-chat.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q4_K_S.gguf) | Q4_K_S | 0.6GB | | [TinyLlama-1.1B-2.5T-chat.Q4_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q4_K.gguf) | Q4_K | 0.62GB | | [TinyLlama-1.1B-2.5T-chat.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q4_K_M.gguf) | Q4_K_M | 0.62GB | | [TinyLlama-1.1B-2.5T-chat.Q4_1.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q4_1.gguf) | Q4_1 | 0.65GB | | [TinyLlama-1.1B-2.5T-chat.Q5_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q5_0.gguf) | Q5_0 | 0.71GB | | [TinyLlama-1.1B-2.5T-chat.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q5_K_S.gguf) | Q5_K_S | 0.71GB | | [TinyLlama-1.1B-2.5T-chat.Q5_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q5_K.gguf) | Q5_K | 0.73GB | | [TinyLlama-1.1B-2.5T-chat.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q5_K_M.gguf) | Q5_K_M | 0.73GB | | [TinyLlama-1.1B-2.5T-chat.Q5_1.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q5_1.gguf) | Q5_1 | 0.77GB | | [TinyLlama-1.1B-2.5T-chat.Q6_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q6_K.gguf) | Q6_K | 0.84GB | | [TinyLlama-1.1B-2.5T-chat.Q8_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-gguf/blob/main/TinyLlama-1.1B-2.5T-chat.Q8_0.gguf) | Q8_0 | 1.09GB | Original model description: --- license: apache-2.0 tags: - finetuned pipeline_tag: text-generation model-index: - name: TinyLlama-1.1B-2.5T-chat results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 34.47 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 59.71 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 26.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 38.8 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 61.01 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 1.14 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat name: Open LLM Leaderboard --- # TinyLlama-1.1B-2.5T-chat It was created by starting with the TinyLlama-1.1B-2.5T-chat and training it on a llama dataset. We have attached the wandb report in pdf form to view the training run at a glance. # Reson This model was fine tuned to allow it to follow direction and is a steeping stone to further training. # Referrals Run Pod - This is who I use to train th emodels on huggingface. If you use it we both get free crdits. - <a href="https://runpod.io?ref=kilq83n1" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit Runpod's Website!</a> Paypal - If you want to leave a tip, it is appecaheted. - <a href="https://paypal.me/OpenSourceTraining" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit My Paypal!</a> # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AIGym__TinyLlama-1.1B-2.5T-chat) | Metric |Value| |---------------------------------|----:| |Avg. |36.93| |AI2 Reasoning Challenge (25-Shot)|34.47| |HellaSwag (10-Shot) |59.71| |MMLU (5-Shot) |26.45| |TruthfulQA (0-shot) |38.80| |Winogrande (5-shot) |61.01| |GSM8k (5-shot) | 1.14|

ProElectro07/subbb

ProElectro07

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Nikhilbk/ppo-LunarLander-v2

Nikhilbk

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.31 +/- 16.53 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

jaich/identify_countries

jaich

transformers

image-classification

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: identify_countries results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.0 --- # identify_countries Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### german  #### india  #### italy  #### london  #### paris 

Ammartatox/sentientscribe

Ammartatox

transformers

text-generation

--- base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** Ammartatox - **License:** apache-2.0 - **Finetuned from model :** NousResearch/Nous-Hermes-2-Mistral-7B-DPO This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

twright8/setfit-oversample-labels-lobbying

twright8

setfit

text-classification

--- library_name: setfit metrics: - f1 - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: To make introductions between Camelot's Chairman and the Cabinet Secretary. We discussed the operation of the UK National Lottery and how to maximise returns to National Lottery Good Causes as well as our plans to celebrate the 25th birthday of The National Lottery. - text: Discussion on crime - text: To discuss Northern Powerhouse Rail and HS2 - text: To discuss food security - text: Electricity market inference: false model-index: - name: SetFit results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: f1 value: 0.9056603773584904 name: F1 - type: accuracy value: 0.9572649572649573 name: Accuracy --- # SetFit This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) --> - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 4 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Evaluation ### Metrics | Label | F1 | Accuracy | |:--------|:-------|:---------| | **all** | 0.9057 | 0.9573 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("twright8/setfit-oversample-labels-lobbying") # Run inference preds = model("Electricity market") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 2 | 21.5644 | 153 | ### Training Hyperparameters - batch_size: (16, 2) - num_epochs: (6, 9) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (7.928034854554858e-06, 2.7001088851580374e-05) - head_learning_rate: 0.009321171293151879 - loss: CoSENTLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: True - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:--------:|:-------------:|:---------------:| | 0.0018 | 1 | 8.669 | - | | 0.0880 | 50 | 8.6617 | - | | 0.1761 | 100 | 12.5549 | - | | 0.2641 | 150 | 3.1895 | - | | 0.3521 | 200 | 16.3181 | - | | 0.4401 | 250 | 0.7513 | - | | 0.5282 | 300 | 4.6653 | - | | 0.0018 | 1 | 0.0059 | - | | 0.0880 | 50 | 3.4564 | - | | 0.1761 | 100 | 0.5523 | - | | 0.2641 | 150 | 0.2372 | - | | 0.3521 | 200 | 4.288 | - | | 0.4401 | 250 | 0.0027 | - | | 0.5282 | 300 | 0.0002 | - | | 0.6162 | 350 | 0.0002 | - | | 0.7042 | 400 | 0.0001 | - | | 0.7923 | 450 | 0.0015 | - | | 0.8803 | 500 | 3.5596 | - | | 0.9683 | 550 | 0.0 | - | | 1.0 | 568 | - | 10.2261 | | 1.0563 | 600 | 0.0 | - | | 1.1444 | 650 | 0.0011 | - | | 1.2324 | 700 | 0.0013 | - | | 1.3204 | 750 | 0.0037 | - | | 1.4085 | 800 | 0.0013 | - | | 1.4965 | 850 | 0.0002 | - | | 1.5845 | 900 | 0.0 | - | | 1.6725 | 950 | 0.0 | - | | 1.7606 | 1000 | 0.0001 | - | | 1.8486 | 1050 | 0.0001 | - | | 1.9366 | 1100 | 0.0001 | - | | 2.0 | 1136 | - | 8.4908 | | 2.0246 | 1150 | 0.0001 | - | | 2.1127 | 1200 | 0.0 | - | | 2.2007 | 1250 | 0.0005 | - | | 2.2887 | 1300 | 0.0004 | - | | 2.3768 | 1350 | 0.0 | - | | 2.4648 | 1400 | 0.0009 | - | | 2.5528 | 1450 | 0.0 | - | | 2.6408 | 1500 | 0.0 | - | | 2.7289 | 1550 | 0.0 | - | | 2.8169 | 1600 | 0.0 | - | | 2.9049 | 1650 | 0.0001 | - | | 2.9930 | 1700 | 0.0003 | - | | 3.0 | 1704 | - | 8.5594 | | 3.0810 | 1750 | 0.0001 | - | | 3.1690 | 1800 | 0.0 | - | | 3.2570 | 1850 | 0.0002 | - | | 3.3451 | 1900 | 0.0001 | - | | 3.4331 | 1950 | 0.0 | - | | 3.5211 | 2000 | 0.0 | - | | 3.6092 | 2050 | 0.0 | - | | 3.6972 | 2100 | 0.0 | - | | 3.7852 | 2150 | 0.0 | - | | 3.8732 | 2200 | 0.0002 | - | | 3.9613 | 2250 | 0.0001 | - | | **4.0** | **2272** | **-** | **8.4573** | | 4.0493 | 2300 | 0.0 | - | | 4.1373 | 2350 | 0.0 | - | | 4.2254 | 2400 | 0.0002 | - | | 4.3134 | 2450 | 0.0 | - | | 4.4014 | 2500 | 0.0003 | - | | 4.4894 | 2550 | 0.0001 | - | | 4.5775 | 2600 | 0.0001 | - | | 4.6655 | 2650 | 0.0001 | - | | 4.7535 | 2700 | 0.0001 | - | | 4.8415 | 2750 | 0.0001 | - | | 4.9296 | 2800 | 0.0012 | - | | 5.0 | 2840 | - | 8.6305 | | 5.0176 | 2850 | 0.0009 | - | | 5.1056 | 2900 | 0.0 | - | | 5.1937 | 2950 | 0.0001 | - | | 5.2817 | 3000 | 0.0 | - | | 5.3697 | 3050 | 0.0 | - | | 5.4577 | 3100 | 0.0001 | - | | 5.5458 | 3150 | 0.0007 | - | | 5.6338 | 3200 | 0.0002 | - | | 5.7218 | 3250 | 0.0 | - | | 5.8099 | 3300 | 0.0001 | - | | 5.8979 | 3350 | 0.0002 | - | | 5.9859 | 3400 | 0.0 | - | | 6.0 | 3408 | - | 8.9528 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 3.0.1 - Transformers: 4.39.0 - PyTorch: 2.3.1+cu118 - Datasets: 2.20.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->