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RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-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-attenuated - GGUF - Model creator: https://huggingface.co/kuotient/ - Original model: https://huggingface.co/kuotient/Llama-3-11B-Instruct-attenuated/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Llama-3-11B-Instruct-attenuated.Q2_K.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q2_K.gguf) | Q2_K | 4.16GB | | [Llama-3-11B-Instruct-attenuated.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.IQ3_XS.gguf) | IQ3_XS | 4.61GB | | [Llama-3-11B-Instruct-attenuated.IQ3_S.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.IQ3_S.gguf) | IQ3_S | 4.83GB | | [Llama-3-11B-Instruct-attenuated.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q3_K_S.gguf) | Q3_K_S | 4.81GB | | [Llama-3-11B-Instruct-attenuated.IQ3_M.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.IQ3_M.gguf) | IQ3_M | 4.98GB | | [Llama-3-11B-Instruct-attenuated.Q3_K.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q3_K.gguf) | Q3_K | 5.3GB | | [Llama-3-11B-Instruct-attenuated.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q3_K_M.gguf) | Q3_K_M | 5.3GB | | [Llama-3-11B-Instruct-attenuated.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q3_K_L.gguf) | Q3_K_L | 5.73GB | | [Llama-3-11B-Instruct-attenuated.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.IQ4_XS.gguf) | IQ4_XS | 5.93GB | | [Llama-3-11B-Instruct-attenuated.Q4_0.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q4_0.gguf) | Q4_0 | 6.17GB | | [Llama-3-11B-Instruct-attenuated.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.IQ4_NL.gguf) | IQ4_NL | 6.23GB | | [Llama-3-11B-Instruct-attenuated.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q4_K_S.gguf) | Q4_K_S | 6.21GB | | [Llama-3-11B-Instruct-attenuated.Q4_K.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q4_K.gguf) | Q4_K | 6.53GB | | [Llama-3-11B-Instruct-attenuated.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q4_K_M.gguf) | Q4_K_M | 6.53GB | | [Llama-3-11B-Instruct-attenuated.Q4_1.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q4_1.gguf) | Q4_1 | 6.81GB | | [Llama-3-11B-Instruct-attenuated.Q5_0.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q5_0.gguf) | Q5_0 | 7.45GB | | [Llama-3-11B-Instruct-attenuated.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q5_K_S.gguf) | Q5_K_S | 7.45GB | | [Llama-3-11B-Instruct-attenuated.Q5_K.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q5_K.gguf) | Q5_K | 7.64GB | | [Llama-3-11B-Instruct-attenuated.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q5_K_M.gguf) | Q5_K_M | 7.64GB | | [Llama-3-11B-Instruct-attenuated.Q5_1.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q5_1.gguf) | Q5_1 | 8.09GB | | [Llama-3-11B-Instruct-attenuated.Q6_K.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q6_K.gguf) | Q6_K | 8.81GB | | [Llama-3-11B-Instruct-attenuated.Q8_0.gguf](https://huggingface.co/RichardErkhov/kuotient_-_Llama-3-11B-Instruct-attenuated-gguf/blob/main/Llama-3-11B-Instruct-attenuated.Q8_0.gguf) | Q8_0 | 11.41GB | Original model description: --- base_model: - kuotient/Meta-Llama-3-8B-Instruct library_name: transformers tags: - mergekit - merge license: other license_name: llama3 --- # Llama-3-11.5B-Instruct-attenuated The core idea came from @jukofyork, see this [issue;](https://github.com/arcee-ai/mergekit/issues/198) As I understand, The concept of the idea is to make model think twice but leap same distances like original. but why 0.7071067812? > The scale factor to use, eg: solve x^2 = 1/2 --> x = 1/sqrt(2) ≈ 0.7071067812 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [kuotient/Meta-Llama-3-8B-Instruct](https://huggingface.co/kuotient/Meta-Llama-3-8B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml ############################### # llama-3-attenuated.yaml # ############################### # Use: mergekit-yaml --clone-tensors ./llama-3-attenuated.yaml ./llama-3-attenuated # See: https://github.com/arcee-ai/mergekit/issues/198 for discussion/reasoning behind this idea. # --- # The scale factor to use, eg: solve x^2 = 1/2 --> x = 1/sqrt(2) ≈ 0.7071067812 const_tag: &scale_factor 0.7071067812 # 1/sqrt(2) # The filter parameters of a scaled block. attenuate-env: &attenuated_env parameters: scale: - filter: q_proj value: *scale_factor - filter: k_proj value: *scale_factor - value: 1.0 # --- slices: ########################### # Block 1: miqu-1 [0, 16] # ########################### - sources: - model: kuotient/Meta-Llama-3-8B-Instruct layer_range: [0, 8] # The first 8 layers of Block 1 are not duplicated - sources: - model: kuotient/Meta-Llama-3-8B-Instruct layer_range: [8, 16] # The last 8 layers of Block 1 are are duplicated twice <<: *attenuated_env ########################### # Block 2: miqu-1 [8, 24] # ########################### - sources: - model: kuotient/Meta-Llama-3-8B-Instruct layer_range: [8, 24] # All the layers of Block 2 are are duplicated twice <<: *attenuated_env ########################## # Block 3: miqu-1 [16, 32] # ########################## - sources: - model: kuotient/Meta-Llama-3-8B-Instruct layer_range: [16, 24] # The first 8 layers of Block 3 are are duplicated twice <<: *attenuated_env - sources: - model: kuotient/Meta-Llama-3-8B-Instruct layer_range: [24, 32] # The last 8 layers of Block 3 are not duplicated merge_method: passthrough dtype: bfloat16 ```

CoprolaliacPress/Thoth-2-Q6_K-GGUF

CoprolaliacPress

transformers

--- base_model: CoprolaliacPress/Thoth-2 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # CoprolaliacPress/Thoth-2-Q6_K-GGUF This model was converted to GGUF format from [`CoprolaliacPress/Thoth-2`](https://huggingface.co/CoprolaliacPress/Thoth-2) 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/CoprolaliacPress/Thoth-2) 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 CoprolaliacPress/Thoth-2-Q6_K-GGUF --hf-file thoth-2-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo CoprolaliacPress/Thoth-2-Q6_K-GGUF --hf-file thoth-2-q6_k.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 CoprolaliacPress/Thoth-2-Q6_K-GGUF --hf-file thoth-2-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo CoprolaliacPress/Thoth-2-Q6_K-GGUF --hf-file thoth-2-q6_k.gguf -c 2048 ```

susaneileenfox/detr-resnet-50_finetuned_cppe5

susaneileenfox

Entry not found

kheopss/kheops_fr_en_epoch1_2bits_GPTQ_V2

kheopss

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]

yemen2016/memobert3_NC_02

yemen2016

transformers

text-classification

--- base_model: MiMe-MeMo/MeMo-BERT-03 tags: - generated_from_trainer model-index: - name: memobert3_NC_02 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. --> # memobert3_NC_02 This model is a fine-tuned version of [MiMe-MeMo/MeMo-BERT-03](https://huggingface.co/MiMe-MeMo/MeMo-BERT-03) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7842 - F1-score: 0.7244 ## 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 - 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 | F1-score | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 82 | 0.5387 | 0.6753 | | No log | 2.0 | 164 | 0.5441 | 0.6753 | | No log | 3.0 | 246 | 0.5721 | 0.6753 | | No log | 4.0 | 328 | 0.5607 | 0.6753 | | No log | 5.0 | 410 | 0.5970 | 0.6904 | | No log | 6.0 | 492 | 0.6772 | 0.6904 | | 0.4736 | 7.0 | 574 | 0.6971 | 0.7034 | | 0.4736 | 8.0 | 656 | 0.7425 | 0.7125 | | 0.4736 | 9.0 | 738 | 0.7842 | 0.7244 | | 0.4736 | 10.0 | 820 | 0.7960 | 0.7244 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

skai24/test_1_0.5

skai24

transformers

text-generation

Invalid username or password.

RichardErkhov/Yash21_-_SuperChat-7B-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) SuperChat-7B - GGUF - Model creator: https://huggingface.co/Yash21/ - Original model: https://huggingface.co/Yash21/SuperChat-7B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [SuperChat-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q2_K.gguf) | Q2_K | 2.53GB | | [SuperChat-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [SuperChat-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.IQ3_S.gguf) | IQ3_S | 2.96GB | | [SuperChat-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [SuperChat-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.IQ3_M.gguf) | IQ3_M | 3.06GB | | [SuperChat-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q3_K.gguf) | Q3_K | 3.28GB | | [SuperChat-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [SuperChat-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [SuperChat-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [SuperChat-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q4_0.gguf) | Q4_0 | 3.83GB | | [SuperChat-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [SuperChat-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [SuperChat-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q4_K.gguf) | Q4_K | 4.07GB | | [SuperChat-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [SuperChat-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q4_1.gguf) | Q4_1 | 4.24GB | | [SuperChat-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q5_0.gguf) | Q5_0 | 4.65GB | | [SuperChat-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [SuperChat-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q5_K.gguf) | Q5_K | 4.78GB | | [SuperChat-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [SuperChat-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q5_1.gguf) | Q5_1 | 5.07GB | | [SuperChat-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q6_K.gguf) | Q6_K | 5.53GB | | [SuperChat-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Yash21_-_SuperChat-7B-gguf/blob/main/SuperChat-7B.Q8_0.gguf) | Q8_0 | 7.17GB | Original model description: --- license: apache-2.0 tags: - merge --- # SuperChat-7B SuperChat-7B is a merge of the following models: * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) * [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227) * [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) * [jan-hq/supermario-v2](https://huggingface.co/jan-hq/supermario-v2) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Yash21/SuperChat-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` To support my efforts please reach out at maratheyash108@gmail.com

abwabai/gemma-2-9b-it-4bit

abwabai

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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RichardErkhov/Edentns_-_DataVortexTL-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) DataVortexTL-1.1B-v0.1 - GGUF - Model creator: https://huggingface.co/Edentns/ - Original model: https://huggingface.co/Edentns/DataVortexTL-1.1B-v0.1/ | Name | Quant method | Size | | ---- | ---- | ---- | | [DataVortexTL-1.1B-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q2_K.gguf) | Q2_K | 0.4GB | | [DataVortexTL-1.1B-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.IQ3_XS.gguf) | IQ3_XS | 0.44GB | | [DataVortexTL-1.1B-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.IQ3_S.gguf) | IQ3_S | 0.47GB | | [DataVortexTL-1.1B-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q3_K_S.gguf) | Q3_K_S | 0.47GB | | [DataVortexTL-1.1B-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.IQ3_M.gguf) | IQ3_M | 0.48GB | | [DataVortexTL-1.1B-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q3_K.gguf) | Q3_K | 0.51GB | | [DataVortexTL-1.1B-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q3_K_M.gguf) | Q3_K_M | 0.51GB | | [DataVortexTL-1.1B-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q3_K_L.gguf) | Q3_K_L | 0.55GB | | [DataVortexTL-1.1B-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.IQ4_XS.gguf) | IQ4_XS | 0.57GB | | [DataVortexTL-1.1B-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q4_0.gguf) | Q4_0 | 0.59GB | | [DataVortexTL-1.1B-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.IQ4_NL.gguf) | IQ4_NL | 0.6GB | | [DataVortexTL-1.1B-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q4_K_S.gguf) | Q4_K_S | 0.6GB | | [DataVortexTL-1.1B-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q4_K.gguf) | Q4_K | 0.62GB | | [DataVortexTL-1.1B-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q4_K_M.gguf) | Q4_K_M | 0.62GB | | [DataVortexTL-1.1B-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q4_1.gguf) | Q4_1 | 0.65GB | | [DataVortexTL-1.1B-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q5_0.gguf) | Q5_0 | 0.71GB | | [DataVortexTL-1.1B-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q5_K_S.gguf) | Q5_K_S | 0.71GB | | [DataVortexTL-1.1B-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q5_K.gguf) | Q5_K | 0.73GB | | [DataVortexTL-1.1B-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q5_K_M.gguf) | Q5_K_M | 0.73GB | | [DataVortexTL-1.1B-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q5_1.gguf) | Q5_1 | 0.77GB | | [DataVortexTL-1.1B-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q6_K.gguf) | Q6_K | 0.84GB | | [DataVortexTL-1.1B-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/Edentns_-_DataVortexTL-1.1B-v0.1-gguf/blob/main/DataVortexTL-1.1B-v0.1.Q8_0.gguf) | Q8_0 | 1.09GB | Original model description: --- tags: - text-generation license: cc-by-nc-sa-4.0 language: - ko base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 pipeline_tag: text-generation datasets: - beomi/KoAlpaca-v1.1a - jojo0217/korean_rlhf_dataset - kyujinpy/OpenOrca-KO - nlpai-lab/kullm-v2 widget: - text: > <|system|> You are a chatbot who answers User's questions. <|user|> 대한민국의 수도는 어디야? <|assistant|> --- # **DataVortexTL-1.1B-v0.1** <img src="./DataVortex.png" alt="DataVortex" style="height: 8em;"> ## Our Team | Research & Engineering | Product Management | | :--------------------: | :----------------: | | Kwangseok Yang | Seunghyun Choi | | Jeongwon Choi | Hyoseok Choi | ## **Model Details** ### **Base Model** [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) ### **Trained On** - **OS**: Ubuntu 20.04 - **GPU**: H100 80GB 1ea - **transformers**: v4.36.2 ### **Dataset** - [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a) - [jojo0217/korean_rlhf_dataset](https://huggingface.co/datasets/jojo0217/korean_rlhf_dataset) - [kyujinpy/OpenOrca-KO](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO) - [nlpai-lab/kullm-v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2) ### **Instruction format** It follows **TinyLlama** format. E.g. ```python text = """\ <|system|> 당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다.</s> <|user|> 대한민국의 수도는 어디야?</s> <|assistant|> 대한민국의 수도는 서울입니다.</s> <|user|> 서울 인구는 총 몇 명이야?</s> """ ``` ## **Model Benchmark** ### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)** | Task | 0-shot | 5-shot | 10-shot | 50-shot | | :--------------- | -------------: | -------------: | -------------: | -----------: | | kobest_boolq | 0.334282 | 0.516446 | 0.500478 | 0.498941 | | kobest_copa | 0.515061 | 0.504321 | 0.492927 | 0.50809 | | kobest_hellaswag | 0.36253 | 0.357733 | 0.355873 | 0.376502 | | kobest_sentineg | 0.481146 | 0.657411 | 0.687417 | 0.635703 | | **Average** | **0.42325475** | **0.50897775** | **0.50917375** | **0.504809** | ### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)** | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 | | ------: | -----: | -----------: | ------: | ------------: | --------------: | | 31.5 | 25.26 | 33.53 | 24.56 | 43.34 | 30.81 | ## **Implementation Code** This model contains the chat_template instruction format. You can use the code below. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexTL-1.1B-v0.1") tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexTL-1.1B-v0.1") messages = [ {"role": "system", "content": "당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다."}, {"role": "user", "content": "대한민국의 수도는 어디야?"}, {"role": "assistant", "content": "대한민국의 수도는 서울입니다."}, {"role": "user", "content": "서울 인구는 총 몇 명이야?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## **License** The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license. <div align="center"> <a href="https://edentns.com/"> <img src="./Logo.png" alt="Logo" style="height: 3em;"> </a> </div>

adhityaprimandhika/fine-tuned-bge-category-by-notes

adhityaprimandhika

transformers

text-classification

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(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]

root-signals/sentiment_analysis_auto_transformer

root-signals

Entry not found

ferrazzipietro/Meta-Llama-3-8B-Instruct_en.layer1_NoQuant_16_16_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. 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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]

NESPED-GEN/TinyLlama1B-spider-all-8500steps

NESPED-GEN

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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Kaenoob/trained-sd3

Kaenoob

Entry not found

whizzzzkid/whizzzzkid_424_5

whizzzzkid

transformers

text-generation

Entry not found

whizzzzkid/whizzzzkid_425_3

whizzzzkid

transformers

text-generation

Entry not found

arianam2607/generative_ai

arianam2607

Entry not found

whizzzzkid/whizzzzkid_426_4

whizzzzkid

transformers

text-generation

Entry not found

abwabai/Phi-3-small-8k-instruct-4bit

abwabai

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]

manbeast3b/ZZZZZZZZdriver136cd

manbeast3b

transformers

text-generation

Entry not found

kr-manish/mistral_unsloth_hrpolicy_combine_raw_QA

kr-manish

--- license: apache-2.0 ---

RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-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-and-function-calling - GGUF - Model creator: https://huggingface.co/AIGym/ - Original model: https://huggingface.co/AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling/ | Name | Quant method | Size | | ---- | ---- | ---- | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q2_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q2_K.gguf) | Q2_K | 0.4GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ3_XS.gguf) | IQ3_XS | 0.44GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ3_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ3_S.gguf) | IQ3_S | 0.47GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q3_K_S.gguf) | Q3_K_S | 0.47GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ3_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ3_M.gguf) | IQ3_M | 0.48GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q3_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q3_K.gguf) | Q3_K | 0.51GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q3_K_M.gguf) | Q3_K_M | 0.51GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q3_K_L.gguf) | Q3_K_L | 0.55GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ4_XS.gguf) | IQ4_XS | 0.57GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_0.gguf) | Q4_0 | 0.59GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.IQ4_NL.gguf) | IQ4_NL | 0.6GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_K_S.gguf) | Q4_K_S | 0.6GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_K.gguf) | Q4_K | 0.62GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_K_M.gguf) | Q4_K_M | 0.62GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_1.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q4_1.gguf) | Q4_1 | 0.65GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_0.gguf) | Q5_0 | 0.71GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_K_S.gguf) | Q5_K_S | 0.71GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_K.gguf) | Q5_K | 0.73GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_K_M.gguf) | Q5_K_M | 0.73GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_1.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q5_1.gguf) | Q5_1 | 0.77GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q6_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.Q6_K.gguf) | Q6_K | 0.84GB | | [TinyLlama-1.1B-2.5T-chat-and-function-calling.Q8_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_TinyLlama-1.1B-2.5T-chat-and-function-calling-gguf/blob/main/TinyLlama-1.1B-2.5T-chat-and-function-calling.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-and-function-calling 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: 34.39 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling 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: 59.61 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling 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: 26.32 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling 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: 38.92 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling 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: 61.96 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling 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: 1.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling name: Open LLM Leaderboard --- # TinyLlama-1.1B-2.5T-chat-and-function-calling It was created by starting with the TinyLlama-1.1B-2.5T-chat-and-function-calling and training it on the open assistant dataset then training yhat on function calling. 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 work with the openai syntask and will return function when apperate. # Templete Us the following templete when interacting with the fine tuned model. # 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-and-function-calling) | Metric |Value| |---------------------------------|----:| |Avg. |37.16| |AI2 Reasoning Challenge (25-Shot)|34.39| |HellaSwag (10-Shot) |59.61| |MMLU (5-Shot) |26.32| |TruthfulQA (0-shot) |38.92| |Winogrande (5-shot) |61.96| |GSM8k (5-shot) | 1.74|

maxrmorrison/promonet

maxrmorrison

--- license: mit ---

whizzzzkid/whizzzzkid_427_1

whizzzzkid

transformers

text-generation

Entry not found

Zak-Soussi/finbert_peft

Zak-Soussi

Entry not found

akashcsd/1111

akashcsd

Entry not found

TatvaJoshi-AHS/peft-InstructionTuning-training-1719936615

TatvaJoshi-AHS

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: google/flan-t5-base model-index: - name: peft-InstructionTuning-training-1719936615 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. --> # peft-InstructionTuning-training-1719936615 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) 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: 4 - 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_steps: 100 - num_epochs: 20 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

whizzzzkid/whizzzzkid_428_7

whizzzzkid

transformers

text-generation

Entry not found

whizzzzkid/whizzzzkid_429_6

whizzzzkid

transformers

text-generation

Entry not found

thangvip/thedeep-1.8b

thangvip

Entry not found

ZeroWw/Phi-3-mini-4k-instruct-GGUF

ZeroWw

--- license: mit language: - en --- My own (ZeroWw) quantizations. output and embed tensors quantized to f16. all other tensors quantized to q5_k or q6_k. Result: both f16.q6 and f16.q5 are smaller than q8_0 standard quantization and they perform as well as the pure f16. Updated on: Tue Jul 2, 20:00:00

juanpablomesa/bge-base-financial-matryoshka

juanpablomesa

sentence-transformers

sentence-similarity

--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9600 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: The median home value in San Carlos, CA is $2,350,000. sentences: - What does the console property of the WorkerGlobalScope interface provide access to? - What is the last sold price and date for the property at 4372 W 14th Street Dr, Greeley, CO 80634? - What is the median home value in San Carlos, CA? - source_sentence: The four new principals hired by Superintendent of Schools Ken Kenworthy for the Okeechobee school system are Joseph Stanley at Central Elementary, Jody Hays at Yearling Middle School, Tuuli Robinson at North Elementary, and Dr. Thelma Jackson at Seminole Elementary School. sentences: - Who won the gold medal in the men's 1,500m final at the speed skating World Cup? - What is the purpose of the 1,2,3 bowling activity for toddlers? - Who are the four new principals hired by Superintendent of Schools Ken Kenworthy for the Okeechobee school system? - source_sentence: Twitter Audit is used to scan your followers and find out what percentage of them are real people. sentences: - What is the main product discussed in the context of fair trade? - What is the software mentioned in the context suitable for? - What is the purpose of the Twitter Audit tool? - source_sentence: Michael Czysz made the 2011 E1pc lighter and more powerful than the 2010 version, and also improved the software controlling the bike’s D1g1tal powertrain. sentences: - What changes did Michael Czysz make to the 2011 E1pc compared to the 2010 version? - What is the author's suggestion for leaving a legacy for future generations? - What is the most affordable and reliable option to fix a MacBook according to the technician? - source_sentence: HTC called the Samsung Galaxy S4 “mainstream”. sentences: - What is the essential aspect of the vocation to marriage according to Benedict XVI's message on the 40th Anniversary of Humanae Vitae? - What did HTC announce about the Samsung Galaxy S4? - What was Allan Cox's First Class Delivery launched on for his Level 1 certification flight? model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.9675 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9791666666666666 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9829166666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98875 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9675 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3263888888888889 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1965833333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09887499999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9675 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9791666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9829166666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.98875 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9776735843960416 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9741727843915341 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.974471752833939 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.9641666666666666 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9775 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9816666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98875 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9641666666666666 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3258333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1963333333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09887499999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9641666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9775 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9816666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.98875 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9758504869144781 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9717977843915344 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9720465527215371 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.9620833333333333 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9741666666666666 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9804166666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98625 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9620833333333333 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32472222222222225 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1960833333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09862499999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9620833333333333 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9741666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9804166666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.98625 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9737941784937224 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9698406084656085 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9702070899963996 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.9554166666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.97 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9766666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98375 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9554166666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3233333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1953333333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09837499999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9554166666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.97 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9766666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.98375 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.969307497603498 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9647410714285715 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9652034022263717 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.9391666666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9616666666666667 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9666666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9758333333333333 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9391666666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3205555555555556 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1933333333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09758333333333333 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9391666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9616666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9666666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9758333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9577277779716886 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9519417989417989 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9525399354798056 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 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': 768, '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-base-financial-matryoshka") # Run inference sentences = [ 'HTC called the Samsung Galaxy S4 “mainstream”.', 'What did HTC announce about the Samsung Galaxy S4?', "What is the essential aspect of the vocation to marriage according to Benedict XVI's message on the 40th Anniversary of Humanae Vitae?", ] 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.* --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9675 | | cosine_accuracy@3 | 0.9792 | | cosine_accuracy@5 | 0.9829 | | cosine_accuracy@10 | 0.9888 | | cosine_precision@1 | 0.9675 | | cosine_precision@3 | 0.3264 | | cosine_precision@5 | 0.1966 | | cosine_precision@10 | 0.0989 | | cosine_recall@1 | 0.9675 | | cosine_recall@3 | 0.9792 | | cosine_recall@5 | 0.9829 | | cosine_recall@10 | 0.9888 | | cosine_ndcg@10 | 0.9777 | | cosine_mrr@10 | 0.9742 | | **cosine_map@100** | **0.9745** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.9642 | | cosine_accuracy@3 | 0.9775 | | cosine_accuracy@5 | 0.9817 | | cosine_accuracy@10 | 0.9888 | | cosine_precision@1 | 0.9642 | | cosine_precision@3 | 0.3258 | | cosine_precision@5 | 0.1963 | | cosine_precision@10 | 0.0989 | | cosine_recall@1 | 0.9642 | | cosine_recall@3 | 0.9775 | | cosine_recall@5 | 0.9817 | | cosine_recall@10 | 0.9888 | | cosine_ndcg@10 | 0.9759 | | cosine_mrr@10 | 0.9718 | | **cosine_map@100** | **0.972** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9621 | | cosine_accuracy@3 | 0.9742 | | cosine_accuracy@5 | 0.9804 | | cosine_accuracy@10 | 0.9862 | | cosine_precision@1 | 0.9621 | | cosine_precision@3 | 0.3247 | | cosine_precision@5 | 0.1961 | | cosine_precision@10 | 0.0986 | | cosine_recall@1 | 0.9621 | | cosine_recall@3 | 0.9742 | | cosine_recall@5 | 0.9804 | | cosine_recall@10 | 0.9862 | | cosine_ndcg@10 | 0.9738 | | cosine_mrr@10 | 0.9698 | | **cosine_map@100** | **0.9702** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9554 | | cosine_accuracy@3 | 0.97 | | cosine_accuracy@5 | 0.9767 | | cosine_accuracy@10 | 0.9838 | | cosine_precision@1 | 0.9554 | | cosine_precision@3 | 0.3233 | | cosine_precision@5 | 0.1953 | | cosine_precision@10 | 0.0984 | | cosine_recall@1 | 0.9554 | | cosine_recall@3 | 0.97 | | cosine_recall@5 | 0.9767 | | cosine_recall@10 | 0.9838 | | cosine_ndcg@10 | 0.9693 | | cosine_mrr@10 | 0.9647 | | **cosine_map@100** | **0.9652** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9392 | | cosine_accuracy@3 | 0.9617 | | cosine_accuracy@5 | 0.9667 | | cosine_accuracy@10 | 0.9758 | | cosine_precision@1 | 0.9392 | | cosine_precision@3 | 0.3206 | | cosine_precision@5 | 0.1933 | | cosine_precision@10 | 0.0976 | | cosine_recall@1 | 0.9392 | | cosine_recall@3 | 0.9617 | | cosine_recall@5 | 0.9667 | | cosine_recall@10 | 0.9758 | | cosine_ndcg@10 | 0.9577 | | cosine_mrr@10 | 0.9519 | | **cosine_map@100** | **0.9525** | <!-- ## 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: 9,600 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: 50.19 tokens</li><li>max: 435 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.66 tokens</li><li>max: 43 tokens</li></ul> | * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------| | <code>The Berry Export Summary 2028 is a dedicated export plan for the Australian strawberry, raspberry, and blackberry industries. It maps the sectors’ current position, where they want to be, high-opportunity markets, and next steps. The purpose of this plan is to grow their global presence over the next 10 years.</code> | <code>What is the Berry Export Summary 2028 and what is its purpose?</code> | | <code>Benefits reported from having access to Self-supply water sources include convenience, less time spent for fetching water and access to more and better quality water. In some areas, Self-supply sources offer important added values such as water for productive use, income generation, family safety and improved food security.</code> | <code>What are some of the benefits reported from having access to Self-supply water sources?</code> | | <code>The unique features of the Coolands for Twitter app include Real-Time updates without the need for a refresh button, Avatar Indicator which shows small avatars on the title bar for new messages, Direct Link for intuitive and convenient link opening, Smart Bookmark to easily return to previous reading position, and User Level Notification which allows customized notification settings for different users.</code> | <code>What are the unique features of the Coolands for Twitter app?</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `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`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `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`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `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`: True - `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_fused - `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> ### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.5333 | 10 | 0.6065 | - | - | - | - | - | | 0.96 | 18 | - | 0.9583 | 0.9674 | 0.9695 | 0.9372 | 0.9708 | | 1.0667 | 20 | 0.3313 | - | - | - | - | - | | 1.6 | 30 | 0.144 | - | - | - | - | - | | 1.9733 | 37 | - | 0.9630 | 0.9699 | 0.9716 | 0.9488 | 0.9745 | | 2.1333 | 40 | 0.1317 | - | - | - | - | - | | 2.6667 | 50 | 0.0749 | - | - | - | - | - | | 2.9867 | 56 | - | 0.9650 | 0.9701 | 0.9721 | 0.9522 | 0.9747 | | 3.2 | 60 | 0.088 | - | - | - | - | - | | 3.7333 | 70 | 0.0598 | - | - | - | - | - | | **3.84** | **72** | **-** | **0.9652** | **0.9702** | **0.972** | **0.9525** | **0.9745** | * The bold row denotes the saved checkpoint. ### 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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.* -->

MubarakB/nllb-3.3b-ug

MubarakB

transformers

text2text-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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ethedeltae/mistral-7b-oig-unsloth-iitg

ethedeltae

transformers

--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** ethedeltae - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit 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)

Adriana213/gpt2-xl-finetuned-wikitext-2

Adriana213

Entry not found

RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-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) Tokara-0.5B-Chat-v0.1 - GGUF - Model creator: https://huggingface.co/Kendamarron/ - Original model: https://huggingface.co/Kendamarron/Tokara-0.5B-Chat-v0.1/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Tokara-0.5B-Chat-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q2_K.gguf) | Q2_K | 0.23GB | | [Tokara-0.5B-Chat-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.IQ3_XS.gguf) | IQ3_XS | 0.24GB | | [Tokara-0.5B-Chat-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.IQ3_S.gguf) | IQ3_S | 0.25GB | | [Tokara-0.5B-Chat-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q3_K_S.gguf) | Q3_K_S | 0.25GB | | [Tokara-0.5B-Chat-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.IQ3_M.gguf) | IQ3_M | 0.26GB | | [Tokara-0.5B-Chat-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q3_K.gguf) | Q3_K | 0.26GB | | [Tokara-0.5B-Chat-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q3_K_M.gguf) | Q3_K_M | 0.26GB | | [Tokara-0.5B-Chat-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q3_K_L.gguf) | Q3_K_L | 0.28GB | | [Tokara-0.5B-Chat-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.IQ4_XS.gguf) | IQ4_XS | 0.28GB | | [Tokara-0.5B-Chat-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q4_0.gguf) | Q4_0 | 0.29GB | | [Tokara-0.5B-Chat-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.IQ4_NL.gguf) | IQ4_NL | 0.29GB | | [Tokara-0.5B-Chat-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q4_K_S.gguf) | Q4_K_S | 0.29GB | | [Tokara-0.5B-Chat-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q4_K.gguf) | Q4_K | 0.3GB | | [Tokara-0.5B-Chat-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q4_K_M.gguf) | Q4_K_M | 0.3GB | | [Tokara-0.5B-Chat-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q4_1.gguf) | Q4_1 | 0.3GB | | [Tokara-0.5B-Chat-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q5_0.gguf) | Q5_0 | 0.32GB | | [Tokara-0.5B-Chat-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q5_K_S.gguf) | Q5_K_S | 0.32GB | | [Tokara-0.5B-Chat-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q5_K.gguf) | Q5_K | 0.33GB | | [Tokara-0.5B-Chat-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q5_K_M.gguf) | Q5_K_M | 0.33GB | | [Tokara-0.5B-Chat-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q5_1.gguf) | Q5_1 | 0.34GB | | [Tokara-0.5B-Chat-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q6_K.gguf) | Q6_K | 0.36GB | | [Tokara-0.5B-Chat-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/Kendamarron_-_Tokara-0.5B-Chat-v0.1-gguf/blob/main/Tokara-0.5B-Chat-v0.1.Q8_0.gguf) | Q8_0 | 0.47GB | Original model description: --- license: other license_name: tongyi-qianwen-research license_link: >- https://huggingface.co/Qwen/Qwen1.5-0.5B/blob/main/LICENSE language: - ja - en pipeline_tag: text-generation --- ## モデルについて [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B)を日英データ5Bトークンで継続事前学習した[Tokara-0.5B-v0.1](https://huggingface.co/Kendamarron/Tokara-0.5B-v0.1)にchat vectorで対話能力を加えたモデルになります。 0.5Bというモデルサイズにしてはコミュニケーションが行えるモデルになっています。 chat vectorに使ったモデルはマルチターンの学習を行ったモデルになっているので、複数ターンの会話も行えるはずです。 モデルサイズの問題なのか、repetition_penaltyを1.15～1.25くらいにしないと早めに繰り返しが始まります。 詳細は[こちら](https://zenn.dev/kendama/articles/55564e12da6e82)をご覧ください。 ## レシピ - [Tokara-0.5B-v0.1](https://huggingface.co/Kendamarron/Tokara-0.5B-v0.1) - 0.24*([Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) - [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B)) - 0.56*([Kendamarron/Tokara-0.5B-Chat-dolly-jimba](https://huggingface.co/Kendamarron/Tokara-0.5B-Chat-dolly-jimba) - [Kendamarron/Tokara-0.5B-v0.1](https://huggingface.co/Kendamarron/Tokara-0.5B-v0.1)) ## ベンチマーク Japanese MT-benchの6カテゴリをシングルターンで評価  | Extraction | Humanities | Reasoning | Roleplay | STEM | Writing | | ---------- | ---------- | --------- | -------- | ---- | ------- | | 1.3 | 2.6 | 2.5 | 3.8 | 2.3 | 3.2 | ## 名前について 日本の在来馬であるトカラ馬から ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" model = AutoModelForCausalLM.from_pretrained( 'Kendamarron/Tokara-0.5B-Chat-v0.1', torch_dtype=torch.bfloat16, device_map=device, ) tokenizer = AutoTokenizer.from_pretrained('Kendamarron/Tokara-0.5B-Chat-v0.1') messages = [ {"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"}, {"role": "user", "content": "野菜は体にいいですか？"} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=256, do_sample=True, top_p=0.95, top_k=40, temperature=0.7, repetition_penalty=1.1, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=2 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) # はい、野食が健康や美容に良いと言われています。身体を健康的なものにするには、バランス良く摂取することが重要です。 # 野球少年であれば、1日500g程度の野味（たんぱく質）を一日3回以上摌る方法があります。また、ビタミンB群やCも含んでおり、お子様向けには食物繊維（食物中含まれる植物性糖）や亜鉛、カリウムなどの栄養素も豊富です。 # # 一方、過剰な野草農業や化学肥料の使用によって生じる不自然な添加物・残留農薬（有害な生物的散布物）などは、人体にとって害になる可能性がありますので、科学的に根拠のある安全な方法で野生活をしてください。野遊びや野炊き以外にも、季節ごとに特徴ある食材が利用可能ですが、その内容や量は慎重に行われるようにしましょう。ぜひ、手軽に自宅でも楽しめる方法と知識をお伝えできるよう、心から応援いたします！ ```

zhhan/Phi-3-mini-4k-instruct_gguf_derived

zhhan

peft

summarization

--- license: apache-2.0 language: - en pipeline_tag: summarization tags: - phi3 - peft - lora - llama.cpp ---

chaley22/gemma-captioning

chaley22

Entry not found

InderV94/sf_unsloth_adapter

InderV94

transformers

--- base_model: unsloth/gemma-2b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl --- # Uploaded model - **Developed by:** InderV94 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2b-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)

cyan2k/promptvieh_chat_merged

cyan2k

transformers

text-generation

--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- # Uploaded model - **Developed by:** cyan2k - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3-mini-4k-instruct-bnb-4bit 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)

axolotl-ai-co/gemma-2-27b

axolotl-ai-co

transformers

text-generation

--- license: gemma library_name: transformers pipeline_tag: text-generation 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 --- # Gemma 2 model card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs) **Resources and Technical Documentation**: * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma] **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-27b) **Authors**: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained variants and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-27b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-27b", device_map="auto", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` <a name="precisions"></a> #### Running the model on a GPU using different precisions The native weights of this model were exported in `bfloat16` precision. You can use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that precision. You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below. * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-27b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-27b", device_map="auto", torch_dtype=torch.float16, revision="float16", ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-27b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-27b", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Upcasting to `torch.float32`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-27b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-27b", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-27b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-27b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-27b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-27b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations * _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Inputs and outputs * **Input:** Text string, such as a question, a prompt, or a document to be summarized. * **Output:** Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ### Citation ```none @article{gemma_2024, title={Gemma}, url={https://www.kaggle.com/m/3301}, DOI={10.34740/KAGGLE/M/3301}, publisher={Kaggle}, author={Gemma Team}, year={2024} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 13 trillion tokens and the 9B model was trained with 8 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)][tpu] hardware (TPUv5p). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models][foundation-models], including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: | Benchmark | Metric | Gemma PT 9B | Gemma PT 27B | | ------------------------------ | ------------- | ----------- | ------------ | | [MMLU][mmlu] | 5-shot, top-1 | 71.3 | 75.2 | | [HellaSwag][hellaswag] | 10-shot | 81.9 | 86.4 | | [PIQA][piqa] | 0-shot | 81.7 | 83.2 | | [SocialIQA][socialiqa] | 0-shot | 53.4 | 53.7 | | [BoolQ][boolq] | 0-shot | 84.2 | 84.8 | | [WinoGrande][winogrande] | partial score | 80.6 | 83.7 | | [ARC-e][arc] | 0-shot | 88.0 | 88.6 | | [ARC-c][arc] | 25-shot | 68.4 | 71.4 | | [TriviaQA][triviaqa] | 5-shot | 76.6 | 83.7 | | [Natural Questions][naturalq] | 5-shot | 29.2 | 34.5 | | [HumanEval][humaneval] | pass@1 | 40.2 | 51.8 | | [MBPP][mbpp] | 3-shot | 52.4 | 62.6 | | [GSM8K][gsm8k] | 5-shot, maj@1 | 68.6 | 74.0 | | [MATH][math] | 4-shot | 36.6 | 42.3 | | [AGIEval][agieval] | 3-5-shot | 52.8 | 55.1 | | [BIG-Bench][big-bench] | 3-shot, CoT | 68.2 | 74.9 | | ------------------------------ | ------------- | ----------- | ------------ | ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias][winobias] and [BBQ Dataset][bbq]. * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies][safety-policies] for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well-known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. #### Gemma 2.0 | Benchmark | Metric | Gemma 2 IT 9B | Gemma 2 IT 27B | | ------------------------ | ------------- | --------------- | ---------------- | | [RealToxicity][realtox] | average | 8.25 | 8.84 | | [CrowS-Pairs][crows] | top-1 | 37.47 | 36.67 | | [BBQ Ambig][bbq] | 1-shot, top-1 | 88.58 | 85.99 | | [BBQ Disambig][bbq] | top-1 | 82.67 | 86.94 | | [Winogender][winogender] | top-1 | 79.17 | 77.22 | | [TruthfulQA][truthfulqa] | | 50.27 | 51.60 | | [Winobias 1_2][winobias] | | 78.09 | 81.94 | | [Winobias 2_2][winobias] | | 95.32 | 97.22 | | [Toxigen][toxigen] | | 39.30 | 38.42 | | ------------------------ | ------------- | --------------- | ---------------- | ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-2 [terms]: https://ai.google.dev/gemma/terms [vertex-mg-gemma]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335 [sensitive-info]: https://cloud.google.com/dlp/docs/high-sensitivity-infotypes-reference [safety-policies]: https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11 [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/google/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [foundation-models]: https://ai.google/discover/foundation-models/ [gemini-2-paper]: https://goo.gle/gemma2report [mmlu]: https://arxiv.org/abs/2009.03300 [hellaswag]: https://arxiv.org/abs/1905.07830 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [boolq]: https://arxiv.org/abs/1905.10044 [winogrande]: https://arxiv.org/abs/1907.10641 [commonsenseqa]: https://arxiv.org/abs/1811.00937 [openbookqa]: https://arxiv.org/abs/1809.02789 [arc]: https://arxiv.org/abs/1911.01547 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [humaneval]: https://arxiv.org/abs/2107.03374 [mbpp]: https://arxiv.org/abs/2108.07732 [gsm8k]: https://arxiv.org/abs/2110.14168 [realtox]: https://arxiv.org/abs/2009.11462 [bold]: https://arxiv.org/abs/2101.11718 [crows]: https://aclanthology.org/2020.emnlp-main.154/ [bbq]: https://arxiv.org/abs/2110.08193v2 [winogender]: https://arxiv.org/abs/1804.09301 [truthfulqa]: https://arxiv.org/abs/2109.07958 [winobias]: https://arxiv.org/abs/1804.06876 [math]: https://arxiv.org/abs/2103.03874 [agieval]: https://arxiv.org/abs/2304.06364 [big-bench]: https://arxiv.org/abs/2206.04615 [toxigen]: https://arxiv.org/abs/2203.09509

impossibleexchange/curbstomp

impossibleexchange

transformers

text-generation

--- license: mit ---

mnsm92/whisper-small-bd-v5.5

mnsm92

transformers

automatic-speech-recognition

Invalid username or password.

LWDCLS/llama3-8B-DarkIdol-2.2-Uncensored-1048K-GGUF-IQ-Imatrix-Request

LWDCLS

--- license: unlicense ---

qsdcfqsdfcxqfqs/China-says-US-targeting-of-AI-not-helpful-to-healthy-development-bf-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4424154233&Connector=https://unitedstatednews.com Source : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2345334154&Connector=https://unitedstatednews.com Flash News : https://justpaste.it/9wsxl Biden last Talk : https://justpaste.it/exwv7 Russian Ukrain Breaking News : https://wow.curseforge.com/paste/fc3a7562 Other Sources : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4231353425&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_5341353434&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1214223412&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4525245233&Connector=https://unitedstatednews.com https://privatebin.net/?edec75736ef3c57f#EUWQTdebh6FBJMKBADHtpqKWotg4p7eyLpALsWuHmNp4 https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_5533431314&Connector=https://unitedstatednews.com https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_255&Connector=https://unitedstatednews.com https://paste2.org/aWDYUnUG https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4111255343&Connector=https://unitedstatednews.com https://yamcode.com/the-unforgettable-journey-through-time-and-space https://tempaste.com/b6rxJ7oOnEm https://www.wowace.com/paste/687fc464 https://privatebin.net/?c4337d6ac8e2355f#HGXvcFzyGQin4fpocHZkYVTNHG6SsMa2oXCH3A7YYqYS https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_5253431441&Connector=https://unitedstatednews.com UNITED NATIONS (Reuters) - U.S. targeting of certain investments in artificial intelligence in China is not helpful to the "healthy development" of AI technology and will be divisive when it comes to global governance, China's U.N. envoy said on Monday. The United States last month issued draft rules for banning or requiring notification of certain investments in artificial intelligence and other technology sectors in China that could threaten U.S. national security. "We are firmly opposed to these sanctions," Chinese U.N. Ambassador Fu Cong told reporters after the 193-member U.N. General Assembly adopted by consensus a Chinese-drafted resolution aimed at boosting international cooperation on AI capacity-building. The U.N resolution calls upon the international community to "provide and promote a fair, open, inclusive and non-discriminatory business environment across the life cycle of safe, secure and trustworthy artificial intelligence systems." Fu said the U.S. actions do not foster an inclusive business environment and he urged Washington to reverse its decision. "We don't believe that the U.S. government's position or decision will be helpful to the healthy development of the AI technology, per se, and will - by extension - divide the world in terms of the standards and in terms of the rules governing the AI," he said. The U.S. Treasury Department published the proposed rules after U.S. President Joe Biden signed an executive order last August as part of a broader push to prevent U.S. know-how from helping the Chinese to develop sophisticated technology and dominate global markets. (Reporting by Michelle Nichols; Editing by Sandra Maler)....

YashJain/GitAI-Qwen2-0.5B-Instruct

YashJain

transformers

text-generation

--- language: - en license: apache-2.0 tags: - chat - code pipeline_tag: text-generation datasets: - YashJain/GitAI library_name: transformers --- # YashJain/GitAI-Qwen2-0.5B-Instruct ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "YashJain/GitAI-Qwen2-0.5B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("YashJain/GitAI-Qwen2-0.5B-Instruct") prompt = "How to undo my last commit" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ```

RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-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-4x1.1B-MoE - GGUF - Model creator: https://huggingface.co/s3nh/ - Original model: https://huggingface.co/s3nh/TinyLLama-4x1.1B-MoE/ | Name | Quant method | Size | | ---- | ---- | ---- | | [TinyLLama-4x1.1B-MoE.Q2_K.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q2_K.gguf) | Q2_K | 1.17GB | | [TinyLLama-4x1.1B-MoE.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.IQ3_XS.gguf) | IQ3_XS | 1.31GB | | [TinyLLama-4x1.1B-MoE.IQ3_S.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.IQ3_S.gguf) | IQ3_S | 1.38GB | | [TinyLLama-4x1.1B-MoE.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q3_K_S.gguf) | Q3_K_S | 1.38GB | | [TinyLLama-4x1.1B-MoE.IQ3_M.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.IQ3_M.gguf) | IQ3_M | 1.4GB | | [TinyLLama-4x1.1B-MoE.Q3_K.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q3_K.gguf) | Q3_K | 1.52GB | | [TinyLLama-4x1.1B-MoE.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q3_K_M.gguf) | Q3_K_M | 1.52GB | | [TinyLLama-4x1.1B-MoE.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q3_K_L.gguf) | Q3_K_L | 1.65GB | | [TinyLLama-4x1.1B-MoE.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.IQ4_XS.gguf) | IQ4_XS | 1.71GB | | [TinyLLama-4x1.1B-MoE.Q4_0.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q4_0.gguf) | Q4_0 | 1.79GB | | [TinyLLama-4x1.1B-MoE.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.IQ4_NL.gguf) | IQ4_NL | 1.8GB | | [TinyLLama-4x1.1B-MoE.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q4_K_S.gguf) | Q4_K_S | 1.8GB | | [TinyLLama-4x1.1B-MoE.Q4_K.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q4_K.gguf) | Q4_K | 1.9GB | | [TinyLLama-4x1.1B-MoE.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q4_K_M.gguf) | Q4_K_M | 1.9GB | | [TinyLLama-4x1.1B-MoE.Q4_1.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q4_1.gguf) | Q4_1 | 1.98GB | | [TinyLLama-4x1.1B-MoE.Q5_0.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q5_0.gguf) | Q5_0 | 2.18GB | | [TinyLLama-4x1.1B-MoE.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q5_K_S.gguf) | Q5_K_S | 2.18GB | | [TinyLLama-4x1.1B-MoE.Q5_K.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q5_K.gguf) | Q5_K | 2.23GB | | [TinyLLama-4x1.1B-MoE.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q5_K_M.gguf) | Q5_K_M | 2.23GB | | [TinyLLama-4x1.1B-MoE.Q5_1.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q5_1.gguf) | Q5_1 | 2.37GB | | [TinyLLama-4x1.1B-MoE.Q6_K.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q6_K.gguf) | Q6_K | 2.59GB | | [TinyLLama-4x1.1B-MoE.Q8_0.gguf](https://huggingface.co/RichardErkhov/s3nh_-_TinyLLama-4x1.1B-MoE-gguf/blob/main/TinyLLama-4x1.1B-MoE.Q8_0.gguf) | Q8_0 | 3.35GB | Original model description: --- base_model: - TinyLlama/TinyLlama-1.1B-Chat-v1.0 - 78health/TinyLlama_1.1B-function-calling - phanerozoic/Tiny-Pirate-1.1b-v0.1 - Tensoic/TinyLlama-1.1B-3T-openhermes tags: - mergekit - merge license: mit language: - en library_name: transformers pipeline_tag: text-generation --- Example usage: ```python from transformers import AutoModelForCausalLM from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("s3nh/TinyLLama-1.1B-MoE") tokenizer = AutoTokenizer.from_pretrained("s3nh/TinyLLama-1.1B-MoE") input_text = """ ###Input: You are a pirate. tell me a story about wrecked ship. ###Response: """) input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device) output = model.generate(inputs=input_ids, max_length=max_length, do_sample=True, top_k=10, temperature=0.7, pad_token_id=tokenizer.eos_token_id, attention_mask=input_ids.new_ones(input_ids.shape)) tokenizer.decode(output[0], skip_special_tokens=True) ``` This model was possible to create by tremendous work of mergekit developers. I decided to merge tinyLlama models to create mixture of experts. Config used as below: ``` """base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 experts: - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 positive_prompts: - "chat" - "assistant" - "tell me" - "explain" - source_model: 78health/TinyLlama_1.1B-function-calling positive_prompts: - "code" - "python" - "javascript" - "programming" - "algorithm" - source_model: phanerozoic/Tiny-Pirate-1.1b-v0.1 positive_prompts: - "storywriting" - "write" - "scene" - "story" - "character" - source_model: Tensoic/TinyLlama-1.1B-3T-openhermes positive_prompts: - "reason" - "provide" - "instruct" - "summarize" - "count" """ ```

gisang-lee/mistral-7b-qlora-arc-wandb-test-arc-easy-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]

finn03091993/naschainv207

finn03091993

Entry not found

M2LInES/ocean_surface_emulation

M2LInES

--- license: mit --- This model is a ConvNext model trained on Pre-industrial Ocean Surface data from the GFDL CM2.6 coupled climate model. More details can be found in the [paper](https://arxiv.org/abs/2405.18585) and [code](https://github.com/suryadheeshjith/Ocean_Emulator).

whizzzzkid/whizzzzkid_430_2

whizzzzkid

transformers

text-generation

Entry not found

qsdcfqsdfcxqfqs/USS-Mason-to-head-home-after-9months-in-combat-at-sea-53-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4544242532&Connector=https://unitedstatednews.com Source : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_2331452241&Connector=https://unitedstatednews.com Flash News : https://tempaste.com/NzDkpJCN6z5 Biden last Talk : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us3542322225&Connector=https://unitedstatednews.com Russian Ukrain Breaking News : https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_112&Connector=https://unitedstatednews.com Other Sources : https://www.pastery.net/svtyeg/ https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_4555513321&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2121444525&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3442423451&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1552332232&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_2351253222&Connector=https://unitedstatednews.com https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_345&Connector=https://unitedstatednews.com https://binshare.net/EYA45Nr3ob3nFHUanhje https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_322&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us3534455521&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1411341441&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1531335313&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_4113234534&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3435135553&Connector=https://unitedstatednews.com NAVAL STATION MAYPORT, Fla. - The USS Mason will return home Tuesday after extended combat operations over nine months. The USS Mason is a guided missile destroyer armed with torpedoes, a mounted gun, missiles and a defense system. Recommended Videos RELATED: Mayport-based USS Mason among ships fending off attacks from Houthi rebels in Red Sea The ship was deployed in the Red Sea, Gulf of Aden and Mediterranean Seas, protecting vital shipping channels. It came under a lot of fire in the Middle East, destroying more than 22 Houthi targets in Yemen and 5 Iranian-launched medium-range ballistic missiles. The USS Carney, which worked with the Mason,also returned home in June after a seven-month deployment. The Carney destroyed Houthi-launched weapons and 65 targets in Yemen.....

maxseats/SungBeom-whisper-small-ko-set19

maxseats

transformers

automatic-speech-recognition

--- language: ko tags: - whisper - speech-recognition datasets: - maxseats/aihub-464-preprocessed-680GB-set-19 metrics: - cer --- # Model Name : maxseats/SungBeom-whisper-small-ko-set18 # Description - 파인튜닝 데이터셋 : maxseats/aihub-464-preprocessed-680GB-set-19 # 설명 - AI hub의 주요 영역별 회의 음성 데이터셋을 학습 중이에요. - 680GB 중 set_0~18 데이터(190GB)까지 파인튜닝한 모델을 불러와서, set_19 데이터(10GB)를 학습한 모델입니다. - 링크 : https://huggingface.co/datasets/maxseats/aihub-464-preprocessed-680GB-set-19

qsdcfqsdfcxqfqs/Ron-Paul-The-presidential-debate-should-be-a-wakeup-call-for-Americans-cg-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1551222143&Connector=https://unitedstatednews.com Source : https://paste.enginehub.org/7tmwc6Rch Flash News : https://tempaste.com/Wd1RpzIu72u Biden last Talk : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4143221543&Connector=https://unitedstatednews.com Russian Ukrain Breaking News : https://ctxt.io/2/AAAY6n8FFQ Other Sources : https://paste.feed-the-beast.com/view/11286bd7 https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3352522235&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1122114443&Connector=https://unitedstatednews.com https://www.taskade.com/d/gRwr4wda4QjUpT8L?share=view&view=uNycNujf83JseLLQ&as=list https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us3511544552&Connector=https://unitedstatednews.com https://prod.pastebin.prod.webservices.mozgcp.net/DfDNWRip https://paste.imirhil.fr/?00681ab2db88f14d#SvJIzafTI96FWQwvhUFihi74HnGnd5zMXlzubWse3RM= https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3443125134&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1552451335&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1513434325&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3322423525&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_4154214315&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1153441323&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_5352313555&Connector=https://unitedstatednews.com There were plenty of surprises in last week's presidential debate. For one, Americans who rely on the mainstream media for their news learned that they had been lied to for the past three years about President Biden's capability to do the job he was elected to do. The realization that the media has been lying for years about Biden is a positive development, as, hopefully, thoughtful Americans might begin wondering what else the media has been lying about. For example, they will find out that the media has been lying to them for years about Russia and Ukraine and about the Middle East and elsewhere. They will find out that our hyper-interventionist foreign policy does not make us safer and more free, but the opposite. Unfortunately for most Americans, foreign policy is something that happens "over there," with few direct effects back home. Dumping nearly $200 billion into the lost cause called "Ukraine" may at most seem like an annoyance to many Americans, but it's not like they are being snatched up by gangs of military recruiters and sent to the front line as is happening to Ukrainian men. However, $200 billion is real money and the effect on our economy is also real. The bill will be paid by each American family indirectly through the inflation "tax." Each dollar created out of thin air and spent on the Ukraine debacle devalues the rest of the dollars in circulation. The danger posed by our foreign policy seemed to escape both candidates, who each tried to convince us they were "tougher" than the other. Despite Donald Trump's sober and accurate warning that Joe Biden has taken us to the brink of World War III, his solution to the problem is doing more of the same. His stated foreign policy seems to be that were he in office the rest of the world would not dare do anything against his will. He would have been so tough that Russian president Vladimir Putin would never have dared to invade Ukraine, he claimed. He would have been so tough that Hamas would never have dared attack Israel on October 7th. It's only Joe Biden's "weakness" that leads to these disastrous foreign policy outcomes. But the world does not work that way. Decades of US sanctions placed on any country that fails to do what Washington demands have backfired and led to the emergence of a block of countries united in their resistance to American dictates. Being "tough" on less-powerful countries may work...until it doesn't. That's where we are today. Neither candidate seems to realize that the world has changed. I have always said that real strength in foreign policy comes from restraint. To prevent these bad outcomes everywhere, stop intervening everywhere. It is not "toughness" that would have prevented Russia from taking action against Ukraine. It is restraint. Not launching a coup in Ukraine in 2014 would have prevented the disastrous war in Ukraine. Just like not stirring up trouble in the South China Sea would prevent a war with China. Not continuing to occupy and intervene in the Middle East would prevent a major regional war which might include Iran and other big players in the region. Restraint is the real toughness. Non-intervention is the only foreign policy that will keep us safe and free. We've tried it the other way and it does not work. Let's try something different.....

kheopss/kheops_fr_en_epoch1_3bits_GPTQ

kheopss

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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qsdcfqsdfcxqfqs/UN-group-demands-release-of-exPakistan-prime-minister-Imran-Khan-a1-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4354235442&Connector=https://unitedstatednews.com Source : https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_514&Connector=https://unitedstatednews.com Flash News : https://paste.feed-the-beast.com/view/2ba4a29f Biden last Talk : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3122414432&Connector=https://unitedstatednews.com Russian Ukrain Breaking News : https://huggingface.co/qsdcfqsdfcxqfqs/China-says-US-targeting-of-AI-not-helpful-to-healthy-development-bf-updated Other Sources : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4125114145&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3135255341&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_2214223144&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1341441452&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_1514253452&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_5243341235&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3132113212&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us3134253244&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3112525123&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_1413323245&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_2343555411&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us5453143314&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_5112253252&Connector=https://unitedstatednews.com https://paste.enginehub.org/PVr8hBMNc The Geneva-based United Nations Working Group on Arbitrary Detention made this demand after examining Khan's case in which he was sentenced last year on charges of corruption. Khan has been facing multiple prison sentences since 2022 when he was ousted through a vote of no-confidence in the parliament. There was no immediate comment from the government of Prime Minister Shehbaz Sharif, who replaced Khan after his ousting. Khan has been held in prison since August 2023 when a court awarded him a three-year prison sentence after finding him guilty of hiding assets after selling state gifts. It led to a ban on Khan from taking part in politics and contesting the February 8 elections, which his party says were rigged. The Election Commission of Pakistan, which oversaw the vote, has denied the vote-rigging allegations. Despite his conviction in multiple cases, Khan remains the leading opposition figure. Khan's Pakistan Tehreek-e-Insaf party, or PTI, which has a strong presence in the parliament, hailed the demand of the UN group, which said Khan's detention in the graft case "had no legal basis and appears to have been intended to disqualify him from running for office. It said "Khan was detained for exercising his right to freedom of expression or opinion" and that he was also denied a "fair trial and due process rights". The UN working group demanded Khan's immediate release, saying it was an "appropriate remedy". The group further said Khan's conviction in the graft case was "part of a much larger campaign of repression targeting the PTI generally and Khan specifically". It said: "In the lead up to Pakistan's February 2024 general elections, PTI candidates were arrested, tortured, and intimidated into leaving the party; PTI rallies were disrupted and blocked; and the party was deprived of its iconic cricket bat symbol, forcing its candidates to run as independents." The UN group also said Khan himself was facing more than 150 politically motivated criminal cases, and just days before the election, he was convicted in three more cases and sentenced to an additional 10 years, 14 years, and seven years in prison, respectively. "For Khan, who is 71 years old, the combined prison term of 34 years amounts to a life sentence," the group said. Khan's spokesman Zulfi Bukhari, welcomed the group's findings and demands for Khan's release. Khan's party won the most seats in the February 8 vote but fell short of a majority to form a government.....

ProElectro07/subbb750x1

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. 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(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]

qsdcfqsdfcxqfqs/Governor-Yusuf-Queries-Refuse-Board-KAROTA-over-poor-service-eb-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4455143412&Connector=https://unitedstatednews.com Source : https://huggingface.co/qsdcfqsdfcxqfqs/Ron-Paul-The-presidential-debate-should-be-a-wakeup-call-for-Americans-cg-updated Flash News : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4425432454&Connector=https://unitedstatednews.com Biden last Talk : https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_552&Connector=https://unitedstatednews.com Russian Ukrain Breaking News : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us5131114125&Connector=https://unitedstatednews.com Other Sources : https://rift.curseforge.com/paste/02c7c460 https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_5415455555&Connector=https://unitedstatednews.com https://tempaste.com/1UwhW5JaTxL https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4423251421&Connector=https://unitedstatednews.com https://paste.toolforge.org/view/331f84eb https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_144&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2554132423&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1533535251&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_2535344321&Connector=https://unitedstatednews.com https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_545&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_4513343442&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us5155253254&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_2324543523&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_2315354451&Connector=https://unitedstatednews.com ..... Expresses dissatisfaction over the decay of equipment at two public work agencies Kano state Governor Abba Kabir Yusuf has expressed disappointment with performance of Refuse Management and Sanitation Board (REMASAB), and Kano Road Traffic Agency (KAROTA) in the discharge of their public responsibilities. This was contained in a statement issued by Sanusi Bature Dawakin Tofa, the Spokesperson to the Governor. Governor Yusuf also ordered the management of the two agencies to submit comprehensive inventory of their equipment to ascertain poor state of facilities. The Governor gave the orders after unscheduled visit to the two government agencies where he lamented inefficiency in the management of facilities and personnel of the agencies. Yusuf who was compiciously worried by the inactive of the agencies regretted the low out of the government agencies despite huge investment and equipment procurement. At REMASAP, the Governor discovered seven functional waste evacuation trucks despite the availability of 30, just as three payloaders are working of the 15 available. The Governor was also informed that 10 workers of REMASAB are permanent and pensionable while the rest are casuals, expressing concern with the situation of casual staff which he described inhuman keeping such personnel for 20 years. While at KAROTA head office, the Governor discovered significant number of operational vehicles grounded. Dissatisfied with the development, the Governor directed KAROTA management to submit comprehensive inventory of their operational vehicles immediately. Gov Yusuf said government investment in the provision of operational vehicles to REMASAB was a demonstration of priority atteched the sanitation in state but the management failed to live to expectation. "I am not happy with what I saw at the two agencies, this calls for a total overhaul of the system, we cannot afford to fail in the discharge of the madates of agencies like KAROTA and REMASAB" the Governor vowed during an on the spot visit The Managing Directors of the REMASAB and KAROTO Hon. Amadu Haruna Zago and Engr. Faisal Mahmoud arrived the scene and were directed to meet the Chief of Staff to the Governor for emergency meeting on how to address the immediate challenges of the agencies.....

cyan2k/promptvieh_text

cyan2k

transformers

--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - **Developed by:** cyan2k - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3-mini-4k-instruct-bnb-4bit 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)

ShakedAAA/Mixtral-8x7B-v0.1-Colleen_8k_06_10_replyOnly_5000_fixed-adapters_July

ShakedAAA

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. 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(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]

ferrazzipietro/Meta-Llama-3-8B-Instruct_en.layer1_NoQuant_16_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. 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(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]

jmg2016/Splade_PP_en_v2_onnx

jmg2016

--- license: apache-2.0 language: - en datasets: - ms_marco tags: - splade++ - document-expansion - sparse representation - bag-of-words - passage-retrieval - knowledge-distillation - document encoder pretty_name: >- ONNX model for prithivida's Splade_PP_en_v2, an Independent Implementation of SPLADE++ Model with some efficiency tweaks for Industry setting. base_model: prithivida/Splade_PP_en_v2 --- # ONNX model for Splade_PP_en_v2 See [https://huggingface.co/prithivida/Splade_PP_en_v2](https://huggingface.co/prithivida/Splade_PP_en_v2) This is just a script for onnx conversion, and an onnx model, with an output format that is compatible with the [anserini](https://github.com/castorini/anserini) SparseEncoder implementations. Based on advice this [github issue](https://github.com/naver/splade/issues/47). ``` python splade_pp_en_v2_to_onnx.py splade_pp_en_v2.onnx ```

hasininawoda/output2

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/output2 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]

qsdcfqsdfcxqfqs/Important-dates-in-the-2024-US-presidential-race-hh-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_5431545532&Connector=https://unitedstatednews.com Source : https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_135&Connector=https://unitedstatednews.com Flash News : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3522321353&Connector=https://unitedstatednews.com Biden last Talk : https://tempaste.com/xDgNfNinjU9 Russian Ukrain Breaking News : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4141122115&Connector=https://unitedstatednews.com Other Sources : https://snippet.host/tojxpk https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4311235243&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_2255453243&Connector=https://unitedstatednews.com https://www.pastery.net/txtjvb/ https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3455134343&Connector=https://unitedstatednews.com https://commie.io/#kCTo9Mzh https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2512213514&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2115352134&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_2315142534&Connector=https://unitedstatednews.com https://wow.curseforge.com/paste/1986621b https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_2252313353&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3135315421&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4353233433&Connector=https://unitedstatednews.com https://sebsauvage.net/paste/?098e454bff792d61#BVpV4OBAVeC3ytdQdhl7G+tzPC4FZoZtWdmrrsZKHjI= By Costas Pitas July 1 (Reuters) - President Joe Biden, a Democrat, and Republican former President Donald Trump will face each other in the 2024 presidential election in what is expected to be a divisive and closely fought contest. Here is a timeline of events related to the Nov. 5 election between now and Inauguration Day in January 2025. 2024 - July 11: Trump, the first sitting or former U.S. president to be convicted of a crime, will be sentenced in the Manhattan hush money case where he was found guilty of falsifying documents to cover up a payment to silence a porn star. He denies wrongdoing and plans to appeal. - July 15-18: Republican National Convention in Milwaukee, Wisconsin, where the party formally chooses its candidate. - In late July or beyond: Vice President Kamala Harris has agreed to debate Trump's yet-to-be-announced running mate on CBS on either of the proposed dates of July 23 or Aug. 13. Trump has accepted a vice presidential debate to be held by Fox News. Fox wrote to both campaigns suggesting July 23, Aug. 13 or other dates following the party conventions. Trump's campaign has yet to say whether his running mate would debate Harris on CBS. Nor has the Biden campaign said if Harris would debate on Fox. - By Aug. 7: The Democrats are set to hold a "virtual roll call" to nominate Biden. It will take place by Aug. 7, the original ballot certification deadline in Ohio, although that date has since been pushed back. Biden had been due to be officially nominated at the Democratic National Convention later in August. - Aug. 19-22: Democratic National Convention in Chicago. - Sept. 10: The second debate between Biden and Trump will be hosted by ABC. - Nov. 5: Election Day - Later in November: It could take days for the election result to be known, especially if it is close and mail-in ballots are a factor. 2025 - Jan. 6: The vice president presides over the Electoral College vote count at a joint session of Congress, announces the results and declares who has been elected. Ahead of the count on Jan. 6, 2021, then-President Trump lambasted his vice president, Mike Pence, for refusing to try to prevent Congress from certifying Biden's win. On that day, the U.S. Capitol was attacked by rioters, and some people chanted, "hang Mike Pence" as they tried to stop the count. Both chambers of Congress later resumed their work and certified Biden's win. Congress has since passed the Electoral Count Reform and Presidential Transition Improvement Act of 2022, which requires approval of one-fifth of the House and Senate to consider a challenge to a state's results - a much higher bar than existed before, when any single lawmaker from each chamber could trigger a challenge. - Jan. 20: The inauguration of the election winner and their vice president takes place. At this ceremony, the victor and vice president are officially sworn into office. (Reporting by Costas Pitas, Tim Reid and Susan Heavey; Editing by Howard Goller and Stephen Coates)....

qsdcfqsdfcxqfqs/Substandard-prostate-cancer-procedures-cutting-Kiwi-mens-lives-short-experts-say-df-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_4123231334&Connector=https://unitedstatednews.com Source : https://commie.io/#WaoFy644 Flash News : https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_423&Connector=https://unitedstatednews.com Biden last Talk : https://tempaste.com/p7VU0nUXJ0W Russian Ukrain Breaking News : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_2422212441&Connector=https://unitedstatednews.com Other Sources : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us5335343543&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1452455424&Connector=https://unitedstatednews.com https://bitbin.it/2l7dpENH/ https://prod.pastebin.prod.webservices.mozgcp.net/erpnc2sA https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4215525345&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_5332112353&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3433325324&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_2252511535&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_2141115225&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2215122242&Connector=https://unitedstatednews.com https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_421&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us4444315311&Connector=https://unitedstatednews.com https://dev.bukkit.org/paste/cda95cb4 https://tempaste.com/create-paste "New Zealand men are not as well served as they could be in terms of the diagnostic pathway for prostate cancer, especially when compared to other countries," Dickens said. The process to diagnose prostate cancer usually starts with a prostate-specific antigen (PSA) blood test. Patients with a persistent high PSA level are referred for an MRI scan. Should the MRI scan reveal a cancer, a biopsy is conducted to confirm. Biopsies are a critical step in the early detection of prostate cancer. An accurate biopsy can be the difference between a long life and a life cut short. Transrectal biopsies are facing global scrutiny for the complications they can cause, such as rectal bleeding and infection. Despite this, they're the standard practice in New Zealand. International guidelines recommend transperineal biopsies because they "almost entirely eliminate the risk of these complications". Dickens said transrectal techniques can be far less accurate because they cannot access the entire prostate. "The biopsy needle can miss the cancer because transrectally means that they haven't been able to reach the place that the cancer was actually hiding out," he said. "A man may be told that he doesn't have prostate cancer, when, in fact, he does." According to the Prostate Cancer Outcomes Registry of Australia and New Zealand Annual Report 2023, only 29% of biopsies performed in New Zealand during 2021 were transperineal, compared to more than 80% of biopsies in Australia. Dickens said some experts hold strong opinions against the use of transrectal biopsies. "A senior figure from the European Association of Urology commented on social media late last year that transrectal ultrasound biopsy in Europe is almost considered medical malpractice," he said. Dr Simon van Rij is an Auckland-based urologist pushing for New Zealand to shift towards transperineal biopsies. He said with increasing pressure on hospitals, the move would free up operating theatres allowing time for more critical surgeries. "Under local anaesthetic, a biopsy can be done in a clinic, outside of an operating theatre setting so it could also make biopsies more accessible around the country," he said. Both van Rij and Dickens believe the main barrier to standardising transperineal biopsies is funding. "The reason that transrectal biopsy is still used in New Zealand is mostly an issue of cost, particularly in public practice," Dickens said. They are calling on the Government to "improve the diagnostic pathways for men accessing the public health system, with transperineal biopsy more widely available". "If we can find cancer early, it gives our patients the space and confidence to take their time to choose the treatment that's right for them," van Rij said. "We're in a very stretched medical system which is under-resourced and under-financed, and as a result, unfortunately, men in New Zealand pay the price by having a method of biopsy which is not standard of care in other countries."....

sims2k/Saul-Instruct-v1-gdpr-finetuned-v1.1-GGUF

sims2k

Entry not found

maxxi146/llama-3-8b-Instruct-bnb-4bit-personalv2

maxxi146

transformers

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** maxxi146 - **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)

RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-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) Narisumashi-11B-v1.5 - GGUF - Model creator: https://huggingface.co/Alsebay/ - Original model: https://huggingface.co/Alsebay/Narisumashi-11B-v1.5/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Narisumashi-11B-v1.5.Q2_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q2_K.gguf) | Q2_K | 3.73GB | | [Narisumashi-11B-v1.5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.IQ3_XS.gguf) | IQ3_XS | 4.14GB | | [Narisumashi-11B-v1.5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.IQ3_S.gguf) | IQ3_S | 4.37GB | | [Narisumashi-11B-v1.5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q3_K_S.gguf) | Q3_K_S | 4.34GB | | [Narisumashi-11B-v1.5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.IQ3_M.gguf) | IQ3_M | 4.51GB | | [Narisumashi-11B-v1.5.Q3_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q3_K.gguf) | Q3_K | 4.84GB | | [Narisumashi-11B-v1.5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q3_K_M.gguf) | Q3_K_M | 4.84GB | | [Narisumashi-11B-v1.5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q3_K_L.gguf) | Q3_K_L | 5.26GB | | [Narisumashi-11B-v1.5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.IQ4_XS.gguf) | IQ4_XS | 5.43GB | | [Narisumashi-11B-v1.5.Q4_0.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q4_0.gguf) | Q4_0 | 5.66GB | | [Narisumashi-11B-v1.5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.IQ4_NL.gguf) | IQ4_NL | 5.72GB | | [Narisumashi-11B-v1.5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q4_K_S.gguf) | Q4_K_S | 5.7GB | | [Narisumashi-11B-v1.5.Q4_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q4_K.gguf) | Q4_K | 6.02GB | | [Narisumashi-11B-v1.5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q4_K_M.gguf) | Q4_K_M | 6.02GB | | [Narisumashi-11B-v1.5.Q4_1.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q4_1.gguf) | Q4_1 | 6.27GB | | [Narisumashi-11B-v1.5.Q5_0.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q5_0.gguf) | Q5_0 | 6.89GB | | [Narisumashi-11B-v1.5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q5_K_S.gguf) | Q5_K_S | 6.89GB | | [Narisumashi-11B-v1.5.Q5_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q5_K.gguf) | Q5_K | 7.08GB | | [Narisumashi-11B-v1.5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q5_K_M.gguf) | Q5_K_M | 7.08GB | | [Narisumashi-11B-v1.5.Q5_1.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q5_1.gguf) | Q5_1 | 7.51GB | | [Narisumashi-11B-v1.5.Q6_K.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.Q6_K.gguf) | Q6_K | 8.2GB | | [Narisumashi-11B-v1.5.Q8_0.gguf](https://huggingface.co/RichardErkhov/Alsebay_-_Narisumashi-11B-v1.5-gguf/blob/main/Narisumashi-11B-v1.5.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 --- # About this model TSF content Beta for V2 of https://huggingface.co/Alsebay/Narumashi-11B-v0.9 (wrong typo but I'm too lazy to fix), but have only 32 rank and 32 lora rank, which cause the model didn't learn well all dataset information, it just know basis information. Anyways, it good if your have a chinese, japanese prompt to trigger TSF content. Maybe not smart, I haven't test yet. - **Finetuned from model :** Sao10K/Fimbulvetr-11B-v2 . Thank Sao10K a lot :) ## I have text and found that Sao10K/Fimbulvetr-11B-v2 could unlock as 8K context length (maybe logic will go down a bit?), so I leave it alone to reduce RAM and VRAM. That mean you can use as 8k context length although this model say only 4k. ## GGUF version? [here is it](https://huggingface.co/Alsebay/Narisumashi-GGUF). ## Dataset All chinese novels dataset ``` Dataset(all are novels): 60% skinsuit 25% possession 5% transform(shapeshift) 10% 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)

ShakedAAA/mixstral_5000_2ndJuly

ShakedAAA

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]

DerenXd/Cute_Girls-V.2.0

DerenXd

diffusers

text-to-image

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/4280PDKe7M1JJmg24K4kS.png base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null --- # Cute_Girls V.2.0 <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/DerenXd/Cute_Girls-V.2.0/tree/main) them in the Files & versions tab.

Koleshjr/flan-t5-base-finetuned-translation-v2

Koleshjr

transformers

text2text-generation

Entry not found

yizhujiao/llama3-8b-sft-medical

yizhujiao

--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: llama3-8b-sft-medical 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. --> # llama3-8b-sft-medical This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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: 1.41e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - total_eval_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.40.1 - Pytorch 2.5.0.dev20240624+cu118 - Datasets 2.20.0 - Tokenizers 0.19.1

CoprolaliacPress/Lewd-Sydney-20B-Q6_K-GGUF

CoprolaliacPress

Invalid username or password.

TheFinAI/finllm-8B-v0.3

TheFinAI

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]

qsdcfqsdfcxqfqs/Marriage-equality-brings-joy-3b-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://huggingface.co/qsdcfqsdfcxqfqs/USS-Mason-to-head-home-after-9months-in-combat-at-sea-53-updated Source : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2445453525&Connector=https://unitedstatednews.com Flash News : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_5414543232&Connector=https://unitedstatednews.com Biden last Talk : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_4244241324&Connector=https://unitedstatednews.com Russian Ukrain Breaking News : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1314221312&Connector=https://unitedstatednews.com Other Sources : https://paste.imirhil.fr/?4aba5a6917c1feaa#99V9OxJ8W02AZ+XwA2Y66eVo/jScrLK8bbB6YY7vIWo= https://ctxt.io/2/AAAYro1UFg https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4532455455&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_1154243311&Connector=https://unitedstatednews.com https://tempaste.com/PKaNC53z3i2 https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3331225231&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3414423334&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us5432235521&Connector=https://unitedstatednews.com https://tempaste.com/LTr1lxdq2PF https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3441545224&Connector=https://unitedstatednews.com https://tempaste.com/create-paste https://paste2.org/nIN0FD9J https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_2541255523&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1423544342&Connector=https://unitedstatednews.com Same-sex couples have been expressing joy since the passage of Thailand's Marriage Equality Bill in the Senate two weeks ago. They say they have been waiting patiently for this moment for a long time, and once the bill becomes law, they will apply for a marriage licence. The law is meaningful to them because it helps guarantee a happy and healthy relationship as long as they are committed to the values that keep them together. On June 18, the Senate endorsed the Marriage Equality Bill at its final reading. The bill allows same-sex couples to register their marriage, with their relationship protected under the same law that applies to heterosexual couples. The bill is expected to become law and take effect by the end of this year, when Thailand is set to become one of 37 countries in the world and the first Southeast Asian nation to legalise same-sex marriage. A long fight The initiative to allow same-sex marriage was first proposed in 2001 by then-interior minister Purachai Piamsomboon. However, it was shot down by Thaksin Shinawatra, prime minister at the time. The idea was brought back in 2019 during Prime Minister Prayut Chan-o-cha's administration. Legislation on marriage equality was approved but later dropped as parliament was dissolved. The Lower House finally passed the Marriage Equality Bill on March 27, and 84 days later, the Upper House announced its final approval of the bill on a 130:4 vote. The law will take effect 120 days after it is published in the Royal Gazette following royal endorsement. The Thai LGBTQ+ community considers it a victory after having fought for their rights for more than two decades. The Bangkok Post recently sat down with some community members to seek their thoughts on the much-anticipated law. Room to improve Prinn Vadhanavira, 44, and Chakkrit Vadhanavira, 49, are among those looking to tie the knot, despite having been together in a relationship for 22 years. Mr Prinn said that because there was no legal recognition for same-sex couples in the past, the couple faced many difficulties, especially when buying property, getting a loan together or listing each other as insurance beneficiaries. They eventually solved these problems by having Mr Prinn's parents adopt Mr Chakkrit as a son so that they could have legal benefits as legal siblings. The couple said they would register their marriage as soon as the law was in effect, adding they had already consulted legal experts and studied the procedures for changing their legal status from adoptive siblings to spouses. The law is also expected to relieve crucial concerns for Sirorat Kanjanasumranwong, 38, and her partner Palita Areeras, 30. The couple have been in their relationship for three years. "The fact that we couldn't sign medical approval for each other because we were not legally related always bothered us. Now we are relieved that the issue will soon be fixed," Ms Sirorat said. While the law allows same-sex couples to register their marriage, some sections need to be improved, especially regarding gender-specific status, which may lead to the misgendering of some LGBTQ+ people, especially trans people. Nachale Boonyapisomparn, vice president of the Foundation of Transgender Alliance for Human Rights, said that she, as a trans woman, would like to be a mother while her partner, a transman, would like to be a father if they decided to register their relationship. Technically, they can register their marriage as a heterosexual couple. However, they have determined their gender identity is not the same as their biological sex. However, the law still uses "father" and "mother\....

CassioBN/roberta-base_LeNER-Br

CassioBN

transformers

token-classification

--- license: mit base_model: FacebookAI/roberta-base tags: - generated_from_trainer datasets: - lener_br metrics: - precision - recall - f1 - accuracy model-index: - name: 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: 0.765 - name: Recall type: recall value: 0.8415841584158416 - name: F1 type: f1 value: 0.8014667365112624 - name: Accuracy type: accuracy value: 0.9711736213348917 --- <!-- 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. --> # roberta-base_LeNER-Br This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the lener_br dataset. It achieves the following results on the evaluation set: - Loss: nan - Precision: 0.765 - Recall: 0.8416 - F1: 0.8015 - Accuracy: 0.9712 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.293 | 1.0 | 979 | nan | 0.5758 | 0.7525 | 0.6524 | 0.9542 | | 0.0596 | 2.0 | 1958 | nan | 0.6546 | 0.7987 | 0.7195 | 0.9534 | | 0.0376 | 3.0 | 2937 | nan | 0.7366 | 0.8339 | 0.7822 | 0.9672 | | 0.0256 | 4.0 | 3916 | nan | 0.6975 | 0.8042 | 0.7471 | 0.9627 | | 0.0192 | 5.0 | 4895 | nan | 0.7173 | 0.8317 | 0.7702 | 0.9646 | | 0.013 | 6.0 | 5874 | nan | 0.7271 | 0.8498 | 0.7837 | 0.9605 | | 0.013 | 7.0 | 6853 | nan | 0.7426 | 0.8537 | 0.7943 | 0.9680 | | 0.0064 | 8.0 | 7832 | nan | 0.7493 | 0.8399 | 0.7920 | 0.9702 | | 0.0052 | 9.0 | 8811 | nan | 0.7611 | 0.8273 | 0.7928 | 0.9725 | | 0.0044 | 10.0 | 9790 | nan | 0.765 | 0.8416 | 0.8015 | 0.9712 | ### Testing results metrics={'test_loss': 0.08161260932683945, 'test_precision': 0.8342714196372732, 'test_recall': 0.8840291583830351, 'test_f1': 0.8584298584298585, 'test_accuracy': 0.9863512377202157, 'test_runtime': 20.4317, 'test_samples_per_second': 68.032, 'test_steps_per_second': 8.516}) ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

qsdcfqsdfcxqfqs/Sesame-a-vital-oilseed-crop-of-Punjab-42-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_5454221511&Connector=https://unitedstatednews.com Source : https://yamcode.com/raw/global-markets-rally-after-positive-economic-data Flash News : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3531555253&Connector=https://unitedstatednews.com Biden last Talk : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_2124434544&Connector=https://unitedstatednews.com Russian Ukrain Breaking News : https://tempaste.com/9Wysc7uAQ0R Other Sources : https://tempaste.com/zlpCGAbRB1l https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3324451314&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2133333115&Connector=https://unitedstatednews.com https://huggingface.co/qsdcfqsdfcxqfqs/Governor-Yusuf-Queries-Refuse-Board-KAROTA-over-poor-service-eb-updated https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=hackear_cuentasnuevo_3411235113&Connector=https://unitedstatednews.com https://tempaste.com/eUjrI2AwHOj https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4355113511&Connector=https://unitedstatednews.com https://tempaste.com/NAwcFWjt4q1 https://tempaste.com/2l7hoL7EgDT https://yamcode.com/whispers-of-ancient-secrets-in-a-modern-world https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2342112254&Connector=https://unitedstatednews.com https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_332&Connector=https://unitedstatednews.com https://rift.curseforge.com/paste/84323470 https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1111534523&Connector=https://unitedstatednews.com Sesame is an important short-duration oilseed crop cultivated in Punjab for centuries. Its seeds contain more than 50% edible oil and about 22% good quality protein. According to health experts, its properties are very similar to olive oil, which is why it is the best nutrition for humans and livestock. Dr Hafiz Salim Akhtar Anjum says that sesame oil is used in pharmaceuticals, and dry cleaners, as well as in high-end soaps, perfumes, and fast food bakery products. Due to these characteristics, domestic and international demand for sesame seeds is increasing. The cost of cultivation of sesame seeds is low and the income is high. Sajid Hussain, a local leader of the All Pakistan Kisan itehad, said sesame cultivation time in Pakistan is from June 1 to July 15. Light sandy and medium loam soil with good water absorption capacity is most suitable for sesame cultivation. The fertile and lowland is not suitable for this cultivation. The crop is cultivated over about 130,000 acres in the country. According to farmer Haji Muhammad Aslam, at first, the land is prepared by ploughing two or three times and preparing the land well. The land should be level which is very important to avoid the ill effects of water shortage. Sesame cultivation is done by tractor drill, usually in rows. The distance between rows is 45cm and the seed is sown to a depth of 2cm. To speed up the growth of the crop, clean and healthy seeds are sown at 2kg per acre in suitable land. Along with this, one sack of DAP and half a sack of urea fertilizer are used at the time of sowing. A week after sowing, the germination of the seed is complete. According to farmer Razak Hussain, sesame crop generally requires three to four times irrigation at 15 to 20-day intervals. The sesame harvest period is 100 to 120 days, after which harvesting begins. Sesame has an average yield of 15 maunds per acre. According to agronomist Muhammad Arshad Jutt, several diseases attack the sesame crop, which causes a decrease in production. Healthy and treated seeds should be used along with good pesticide spray to ensure a good sesame crop to avoid these diseases. According to Dr Anjum, sesame seeds are called the meat of the poor. People who don't eat meat should use sesame seeds. The seeds are a rich source of vitamins. All the essential building blocks of the human body are present. It is very useful for heart diseases, skin diseases, high blood pressure, stroke, lung and stomach diseases. The use of sesame seeds is useful in improving the complexion as well as lengthening and darkening the hair. For children who wet the bed while sleeping, sesame seeds are very beneficial as they strengthen the bladder. Sesame is widely used in sweets. Sesame laddoos, reoris and Gichak are popular souvenirs, said a baker, Muhammad Shehzad....

MrGonk/Gonk_3

MrGonk

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]

akhilpavuluri/GenAI1

akhilpavuluri

Entry not found

mradermacher/Echidna-13b-v0.3-GGUF

mradermacher

transformers

--- base_model: NeverSleep/Echidna-13b-v0.3 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/Echidna-13b-v0.3 <!-- 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/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.IQ3_XS.gguf) | IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.IQ3_M.gguf) | IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Echidna-13b-v0.3-GGUF/resolve/main/Echidna-13b-v0.3.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 -->

Niggendar/moxiePony_v13

Niggendar

diffusers

text-to-image

--- library_name: diffusers --- # 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 🧨 diffusers 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]

SimplCup/Gideon

SimplCup

--- license: openrail ---

taehyunzzz/switch-base-32-samsum-top-4-choose-1-deconly

taehyunzzz

transformers

text2text-generation

--- license: apache-2.0 base_model: google/switch-base-32 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: switch-base-32-samsum-top-4-choose-1-deconly results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: validation args: samsum metrics: - name: Rouge1 type: rouge value: 48.1483 --- <!-- 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. --> # switch-base-32-samsum-top-4-choose-1-deconly This model is a fine-tuned version of [google/switch-base-32](https://huggingface.co/google/switch-base-32) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.5476 - Rouge1: 48.1483 - Rouge2: 24.7832 - Rougel: 40.7375 - Rougelsum: 44.5607 - Gen Len: 16.791 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.4301 | 0.2172 | 200 | 2.5650 | 36.2949 | 15.7872 | 31.4771 | 33.5875 | 14.291 | | 2.463 | 0.4343 | 400 | 1.9643 | 42.2665 | 19.5129 | 35.4042 | 39.1887 | 16.2946 | | 2.2735 | 0.6515 | 600 | 1.8080 | 44.1285 | 20.7698 | 37.2279 | 40.8229 | 16.39 | | 2.0163 | 0.8686 | 800 | 1.7496 | 43.767 | 21.2272 | 37.4578 | 40.5675 | 15.2604 | | 1.9836 | 1.0858 | 1000 | 1.6872 | 45.4925 | 22.0234 | 38.4465 | 41.9728 | 16.1443 | | 1.9816 | 1.3029 | 1200 | 1.6644 | 46.6391 | 23.2045 | 39.4297 | 43.2476 | 16.3778 | | 2.0067 | 1.5201 | 1400 | 1.6287 | 46.692 | 22.8868 | 39.4165 | 43.1099 | 16.5391 | | 1.8679 | 1.7372 | 1600 | 1.6210 | 46.9779 | 23.5089 | 40.0585 | 43.4129 | 16.0758 | | 1.8658 | 1.9544 | 1800 | 1.6083 | 47.3286 | 24.0168 | 40.148 | 43.7942 | 16.78 | | 1.7036 | 2.1716 | 2000 | 1.5961 | 47.3911 | 23.798 | 39.9685 | 43.8634 | 16.5306 | | 1.7296 | 2.3887 | 2200 | 1.5955 | 47.9152 | 24.4805 | 40.8632 | 44.5938 | 16.2286 | | 1.7464 | 2.6059 | 2400 | 1.5817 | 47.2239 | 23.886 | 40.3105 | 43.9387 | 16.3007 | | 1.7085 | 2.8230 | 2600 | 1.5667 | 47.4369 | 24.0868 | 40.288 | 44.0761 | 16.3337 | | 1.5667 | 3.0402 | 2800 | 1.5834 | 47.6073 | 24.4565 | 40.578 | 44.093 | 16.588 | | 1.6104 | 3.2573 | 3000 | 1.5680 | 47.937 | 24.5777 | 40.7943 | 44.3661 | 16.5489 | | 1.6556 | 3.4745 | 3200 | 1.5446 | 47.8843 | 24.6985 | 40.7419 | 44.3735 | 16.7127 | | 1.6113 | 3.6916 | 3400 | 1.5500 | 47.6546 | 24.2782 | 40.1105 | 44.1072 | 17.0147 | | 1.5974 | 3.9088 | 3600 | 1.5513 | 47.7263 | 24.592 | 40.7256 | 44.3474 | 16.5892 | | 1.4848 | 4.1260 | 3800 | 1.5458 | 47.9634 | 24.7055 | 40.661 | 44.3527 | 16.7347 | | 1.5184 | 4.3431 | 4000 | 1.5441 | 47.7037 | 24.5408 | 40.2843 | 44.2096 | 16.7237 | | 1.5397 | 4.5603 | 4200 | 1.5417 | 48.3854 | 25.1618 | 40.7691 | 44.814 | 16.7702 | | 1.6644 | 4.7774 | 4400 | 1.5459 | 48.2593 | 25.1185 | 40.8583 | 44.6804 | 16.835 | | 1.5555 | 4.9946 | 4600 | 1.5476 | 48.1483 | 24.7832 | 40.7375 | 44.5607 | 16.791 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

qsdcfqsdfcxqfqs/Ron-Paul-The-presidential-debate-should-be-a-wakeup-call-for-Americans-b5-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://www.wowace.com/paste/e60ac04f Source : https://binshare.net/LZWMlhjCnvLd7WwpjNub Flash News : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3221155311&Connector=https://unitedstatednews.com Biden last Talk : https://tempaste.com/YAWo9WFEeBZ Russian Ukrain Breaking News : https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_522&Connector=https://unitedstatednews.com Other Sources : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3244133435&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1345131534&Connector=https://unitedstatednews.com https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_132&Connector=https://unitedstatednews.com https://authors-old.curseforge.com/paste/11649e32 https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us5421115125&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us3451535353&Connector=https://unitedstatednews.com https://tempaste.com/ouowrhNZ23F https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us5544141435&Connector=https://unitedstatednews.com https://bitbin.it/eksCaTzc/ https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3322232213&Connector=https://unitedstatednews.com https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us5352244133&Connector=https://unitedstatednews.com https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1215211155&Connector=https://unitedstatednews.com https://sebsauvage.net/paste/?e8cef9c10c2ae25b#qVE0LMZfzFM0HVsyMB6b4veKN7vvwhFXAfprCaKF8sw= https://www.taskade.com/d/eFiMrArNRqEg98yV?share=view&view=z1eqaj2Zo7AYpVNq&as=list There were plenty of surprises in last week's presidential debate. For one, Americans who rely on the mainstream media for their news learned that they had been lied to for the past three years about President Biden's capability to do the job he was elected to do. The realization that the media has been lying for years about Biden is a positive development, as, hopefully, thoughtful Americans might begin wondering what else the media has been lying about. For example, they will find out that the media has been lying to them for years about Russia and Ukraine and about the Middle East and elsewhere. They will find out that our hyper-interventionist foreign policy does not make us safer and more free, but the opposite. Unfortunately for most Americans, foreign policy is something that happens "over there," with few direct effects back home. Dumping nearly $200 billion into the lost cause called "Ukraine" may at most seem like an annoyance to many Americans, but it's not like they are being snatched up by gangs of military recruiters and sent to the front line as is happening to Ukrainian men. However, $200 billion is real money and the effect on our economy is also real. The bill will be paid by each American family indirectly through the inflation "tax." Each dollar created out of thin air and spent on the Ukraine debacle devalues the rest of the dollars in circulation. The danger posed by our foreign policy seemed to escape both candidates, who each tried to convince us they were "tougher" than the other. Despite Donald Trump's sober and accurate warning that Joe Biden has taken us to the brink of World War III, his solution to the problem is doing more of the same. His stated foreign policy seems to be that were he in office the rest of the world would not dare do anything against his will. He would have been so tough that Russian president Vladimir Putin would never have dared to invade Ukraine, he claimed. He would have been so tough that Hamas would never have dared attack Israel on October 7th. It's only Joe Biden's "weakness" that leads to these disastrous foreign policy outcomes. But the world does not work that way. Decades of US sanctions placed on any country that fails to do what Washington demands have backfired and led to the emergence of a block of countries united in their resistance to American dictates. Being "tough" on less-powerful countries may work...until it doesn't. That's where we are today. Neither candidate seems to realize that the world has changed. I have always said that real strength in foreign policy comes from restraint. To prevent these bad outcomes everywhere, stop intervening everywhere. It is not "toughness" that would have prevented Russia from taking action against Ukraine. It is restraint. Not launching a coup in Ukraine in 2014 would have prevented the disastrous war in Ukraine. Just like not stirring up trouble in the South China Sea would prevent a war with China. Not continuing to occupy and intervene in the Middle East would prevent a major regional war which might include Iran and other big players in the region. Restraint is the real toughness. Non-intervention is the only foreign policy that will keep us safe and free. We've tried it the other way and it does not work. Let's try something different.....

qsdcfqsdfcxqfqs/Srinagar-Records-Seasons-Hottest-Day-Rains-Likely-3e-updated

qsdcfqsdfcxqfqs

--- language: - en --- []() read the full article here : https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us2551451155&Connector=https://unitedstatednews.com Source : https://yamcode.com/global-markets-rally-after-positive-economic-data Flash News : https://tempaste.com/7TAKOEoXXgx Biden last Talk : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3424455354&Connector=https://unitedstatednews.com Russian Ukrain Breaking News : https://authors-old.curseforge.com/paste/f8c04508 Other Sources : https://dev.bukkit.org/paste/c1ef434e https://tempaste.com/2Qrmgwd5CEo https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_134&Connector=https://unitedstatednews.com https://tempaste.com/rJXkORpBV03 https://tempaste.com/fDpUj8jpCYH https://dev.uc.apps.uri.edu/fckeditor/editor/filemanager/browser/default/browser.html?id=howtohack_account_us1143252321&Connector=https://unitedstatednews.com https://huggingface.co/qsdcfqsdfcxqfqs/Important-dates-in-the-2024-US-presidential-race-hh-updated/new/main/?filename=README.md https://tempaste.com/a8SwwOO0488 https://huggingface.co/qsdcfqsdfcxqfqs/Marriage-equality-brings-joy-3b-updated https://tempaste.com/piQTtUGGy7Z https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4133251443&Connector=https://unitedstatednews.com https://huggingface.co/qsdcfqsdfcxqfqs/UN-group-demands-release-of-exPakistan-prime-minister-Imran-Khan-a1-updated https://tempaste.com/ccKkMMHVDX9 https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3551452343&Connector=https://unitedstatednews.com Srinagar- The arrival of the new month has commenced with the heat wave in three stations of Kashmir including Srinagar, the summer capital of Jammu and Kashmir where the hottest day of the season was recorded on Monday at 34.3 degree Celsius. According to the details available, Srinagar, Kokernag and Qazigund, the gateway of Kashmir, have recorded the heat wave today. Pertinently, Srinagar and Qazigund were hotter than Kolkata today where the mercury settled at 31 degree Celsius. As per the details, Qazigund recorded a maximum temperature of 32.4 degree Celsius while Kokernag recorded a maximum temperature of 31.9 degree Celsius. The temperature in Kupwara, however, settled close to the heat wave temperature as 31.9 degree Celsius was recorded today. Pahalgam, a famous tourist destination and Gulmarg, a famous ski-resort recorded 27.6 degree Celsius and 24.4 degree Celsius respectively. Meanwhile, all the Jammu stations recorded above normal temperature. The details reveal that Jammu, a winter capital of the Union Territory, recorded 36.2 degree Celsius while Banihal recorded 31.0 degree Celsius. Batote, Katra and Bhaderwah recorded a maximum temperature of 28.8 degree Celsius, 34.0 degree Celsius and 30.8 degree Celsius respectively. Moreover, Director Meteorological department (MeT), Dr Mukhtar Ahmad has however predicted the possibility of rains and thundershower at many places. There is a possibility of spell of rain and thundershower at many places of Jammu division towards late night and early morning and spell of rain and thundershower at few places of Kashmir division till July 03. He however, stated that from July 4 to 5, there is a possibility of intermittent light to moderate rainfall at most places of J&K with heavy showers and rainfall at few places From July 6 to 7, intermittent light to moderate rainfall is expected at most places of J&K with heavy showers and rainfall at few places. Besides, the weatherman has issued an advisory, saying that the flash floods, landslides, mudslides & shooting stones are expected at few vulnerable places. Few low lying areas may experience temporary water logging conditions, the advisory reads, adding that few places may experience moderate thunderstorm and lightning. J&K To Witness Intermittent Rains: Lotus After a prolonged heatwave, Jammu Kashmir is expected to have 'moderate to heavy' rainfall from July 5 onwards. Weather experts however ruled out the possibility of floods but advised people especially those living in hilly areas of Chenab Valley to be vigilant. Talking about the weather scenario in Jammu and Kashmir, Weather Expert Sonam Lotus told a Srinagar-based news agency that there is the possibility of rainfall from July 5 onwards. He said the rainfall may be intermittent in nature but due to heatwaves, glaciers have melted and water levels in water bodies are expected to rise. "There are no chances of any floods but there may be flash floods, especially in Rajouri, Doda, Poonch, Kupwara and other hilly districts," Sonam Lotus said and advised the nomadic community to be alert. Sonam Lotus also said that due to monsoon rains, the intensity of thunderstorms and cloud bursts may increase in Jammu and Kashmir, especially in hilly areas. "In July-August, the possibility of landslides and mudslides always increases. People should not panic but there is a need to be cautious. In Srinagar or plains, there is no possibility of any floods as is being claimed. There will be no continuous rainfall. The water level may increase," he said. Sonam Lotus said that different places en route to Amarnath Yatra may witness morning or evening showers on a daily or alternate basis.....

WilAI/llama-2-7b-miniguanaco

WilAI

transformers

text-generation

Entry not found

KYAGABA/wav2vec2-large-xls-r-300m-luo-googlefluers-5hr-v1

KYAGABA

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-luo-googlefluers-5hr-v1 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: fleurs type: fleurs config: luo_ke split: test args: luo_ke metrics: - name: Wer type: wer value: 0.5508333333333333 --- <!-- 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/asr-africa-research-team/ASR%20Africa/runs/3yjshy20) # wav2vec2-large-xls-r-300m-luo-googlefluers-5hr-v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.7669 - Wer: 0.5508 - Cer: 0.1450 ## 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-------:|:----:|:---------------:|:------:|:------:| | 14.2071 | 2.6667 | 100 | 5.4183 | 1.0 | 1.0 | | 4.5199 | 5.3333 | 200 | 3.5672 | 1.0 | 1.0 | | 3.2401 | 8.0 | 300 | 2.9414 | 1.0 | 1.0 | | 2.912 | 10.6667 | 400 | 2.8496 | 1.0 | 1.0 | | 2.293 | 13.3333 | 500 | 1.0939 | 0.8385 | 0.2482 | | 0.7468 | 16.0 | 600 | 0.6503 | 0.601 | 0.1549 | | 0.4431 | 18.6667 | 700 | 0.6416 | 0.5808 | 0.1534 | | 0.2886 | 21.3333 | 800 | 0.6753 | 0.5793 | 0.1535 | | 0.2085 | 24.0 | 900 | 0.6925 | 0.562 | 0.1467 | | 0.1715 | 26.6667 | 1000 | 0.7211 | 0.5673 | 0.1477 | | 0.1394 | 29.3333 | 1100 | 0.7347 | 0.5532 | 0.1430 | | 0.1249 | 32.0 | 1200 | 0.7424 | 0.5543 | 0.1449 | | 0.1131 | 34.6667 | 1300 | 0.7561 | 0.5588 | 0.1471 | | 0.1034 | 37.3333 | 1400 | 0.7595 | 0.553 | 0.1445 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

danielkosyra/polynomial_2000_9e-4_16b_w0.075

danielkosyra

transformers

text-generation

--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: polynomial_2000_9e-4_16b_w0.075 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. --> # polynomial_2000_9e-4_16b_w0.075 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8132 ## 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: 0.0009 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 250 - training_steps: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.5244 | 0.8234 | 250 | 3.4016 | | 3.1742 | 1.6468 | 500 | 3.1288 | | 2.9037 | 2.4702 | 750 | 2.9918 | | 2.7072 | 3.2935 | 1000 | 2.9131 | | 2.5479 | 4.1169 | 1250 | 2.8668 | | 2.3946 | 4.9403 | 1500 | 2.8252 | | 2.2317 | 5.7637 | 1750 | 2.8147 | | 2.1172 | 6.5871 | 2000 | 2.8132 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

Cassiamendes/cassia

Cassiamendes

Entry not found

CoprolaliacPress/Thoth-3-Q6_K-GGUF

CoprolaliacPress

transformers

--- base_model: CoprolaliacPress/Thoth-3 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # CoprolaliacPress/Thoth-3-Q6_K-GGUF This model was converted to GGUF format from [`CoprolaliacPress/Thoth-3`](https://huggingface.co/CoprolaliacPress/Thoth-3) 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/CoprolaliacPress/Thoth-3) 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 CoprolaliacPress/Thoth-3-Q6_K-GGUF --hf-file thoth-3-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo CoprolaliacPress/Thoth-3-Q6_K-GGUF --hf-file thoth-3-q6_k.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 CoprolaliacPress/Thoth-3-Q6_K-GGUF --hf-file thoth-3-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo CoprolaliacPress/Thoth-3-Q6_K-GGUF --hf-file thoth-3-q6_k.gguf -c 2048 ```

qsdcfqsdfcxqfqs/Column-The-Supreme-Court-just-gave-itself-a-lot-more-power-ee-updated

qsdcfqsdfcxqfqs

Entry not found

qsdcfqsdfcxqfqs/SelfAssembling-Highly-Conductive-Sensors-Could-Improve-Wearable-Devices-25-updated

qsdcfqsdfcxqfqs

Entry not found

GGarri/whisper_finetuned_ver2

GGarri

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper_finetuned_ver2 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. --> # whisper_finetuned_ver2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0048 - Cer: 0.5262 - Wer: 0.4840 ## 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: 32 - 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_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 0.0 | 35.71 | 1000 | 0.0047 | 0.5496 | 0.5227 | | 0.0001 | 71.43 | 2000 | 0.0048 | 0.5262 | 0.4840 | | 0.0 | 107.14 | 3000 | 0.0051 | 0.5964 | 0.5615 | | 0.0 | 142.86 | 4000 | 0.0053 | 0.6080 | 0.5808 | | 0.0 | 178.57 | 5000 | 0.0054 | 0.6080 | 0.5808 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.15.2

Nutanix/Meta-Llama-3-8B-Instruct_KTO_lora_Anthropic_HH_Golden-processed

Nutanix

peft

--- base_model: meta-llama/Meta-Llama-3-8B-Instruct 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/venkycs_-_Zyte-1B-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) Zyte-1B - GGUF - Model creator: https://huggingface.co/venkycs/ - Original model: https://huggingface.co/venkycs/Zyte-1B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Zyte-1B.Q2_K.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q2_K.gguf) | Q2_K | 0.4GB | | [Zyte-1B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.IQ3_XS.gguf) | IQ3_XS | 0.44GB | | [Zyte-1B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.IQ3_S.gguf) | IQ3_S | 0.47GB | | [Zyte-1B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q3_K_S.gguf) | Q3_K_S | 0.47GB | | [Zyte-1B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.IQ3_M.gguf) | IQ3_M | 0.48GB | | [Zyte-1B.Q3_K.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q3_K.gguf) | Q3_K | 0.51GB | | [Zyte-1B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q3_K_M.gguf) | Q3_K_M | 0.51GB | | [Zyte-1B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q3_K_L.gguf) | Q3_K_L | 0.55GB | | [Zyte-1B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.IQ4_XS.gguf) | IQ4_XS | 0.57GB | | [Zyte-1B.Q4_0.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q4_0.gguf) | Q4_0 | 0.59GB | | [Zyte-1B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.IQ4_NL.gguf) | IQ4_NL | 0.6GB | | [Zyte-1B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q4_K_S.gguf) | Q4_K_S | 0.6GB | | [Zyte-1B.Q4_K.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q4_K.gguf) | Q4_K | 0.62GB | | [Zyte-1B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q4_K_M.gguf) | Q4_K_M | 0.62GB | | [Zyte-1B.Q4_1.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q4_1.gguf) | Q4_1 | 0.65GB | | [Zyte-1B.Q5_0.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q5_0.gguf) | Q5_0 | 0.71GB | | [Zyte-1B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q5_K_S.gguf) | Q5_K_S | 0.71GB | | [Zyte-1B.Q5_K.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q5_K.gguf) | Q5_K | 0.73GB | | [Zyte-1B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q5_K_M.gguf) | Q5_K_M | 0.73GB | | [Zyte-1B.Q5_1.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q5_1.gguf) | Q5_1 | 0.77GB | | [Zyte-1B.Q6_K.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q6_K.gguf) | Q6_K | 0.84GB | | [Zyte-1B.Q8_0.gguf](https://huggingface.co/RichardErkhov/venkycs_-_Zyte-1B-gguf/blob/main/Zyte-1B.Q8_0.gguf) | Q8_0 | 1.09GB | Original model description: --- license: apache-2.0 language: - en metrics: - accuracy - bertscore - bleu tags: - slm - llama - tiny - tinyllama datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized --- # Zyte-1.1b: Tiny but Mighty ## Model Details ### Model Description The **Zyte 1B** model is a cutting-edge advancement in AI language understanding and generation. This version is a sophisticated refinement of the acclaimed **tinyllama** model, incorporating the advanced Direct Parameter Optimization (DPO) technique. Diligently enhanced this model using state-of-the-art datasets, ensuring unparalleled performance and accuracy. - **Model type**: TinyLlama - **Specialization**: AI Language Understanding and Generation The aihub-app/zyte-1.1b model represents a significant advancement in the field of AI language understanding and generation. This model is a meticulously fine-tuned version of the renowned tinyllama model, utilizing the advanced Direct Parameter Optimization (DPO) technique. Our team at AI Hub App has dedicated considerable effort to enhance this model using state-of-the-art datasets. "<|system|> You are a helpful AI assistant.</s><|user|>{prompt}</s><|assistant|>" Inference Code - https://huggingface.co/aihub-app/zyte-1B/blob/main/inference_zyte_1b.ipynb

Jalex00/ddpm-custom-dataset-128

Jalex00

Entry not found

cyan2k/promptvieh_chat

cyan2k

transformers

--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - **Developed by:** cyan2k - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3-mini-4k-instruct-bnb-4bit 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)

Shinyystarz/Furina_eng

Shinyystarz

--- license: bsl-1.0 ---

Niggendar/datassRev3Pony_rev3

Niggendar

diffusers

text-to-image

--- library_name: diffusers --- # 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 🧨 diffusers 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]

gglabs/Solar-kiosk-scenario-2-epoch

gglabs

transformers

--- base_model: gglabs/Solar-kiosk-scenario-1-epoch language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** gglabs - **License:** apache-2.0 - **Finetuned from model :** gglabs/Solar-kiosk-scenario-1-epoch 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)