Update README.md

README.md

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 ---
 library_name: transformers
 tags:
-- trl
 - sft
+- rag
+- instruct
+- programming
+- code
+- python
+- typescript
+license: mit
+datasets:
+- HuggingFaceFW/fineweb
+- glaiveai/glaive-code-assistant-v3
+- JuanjoLopez19/Software-Engineering-Dataset_90_10_EN
+- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
+- tomasonjo/text2cypher-gpt4o-clean
+- openbmb/UltraInteract_sft
+- Isaak-Carter/Openai-function-invocations-20k-with-greetings
+- OpenAssistant/oasst1
+- Enoch2090/github_semantic_search
+- codeparrot/github-code
+- THUDM/AgentInstruct
+- mhhmm/typescript-instruct-20k
+- petrpan26/typescript-code
+- bleugreen/typescript-chunks
+- Agent-Eval-Refine/Agent-Trajectories
+- mt1234/BTC_USDT_2017-2024
+- gradio/custom-component-gallery-backups
+- freddyaboulton/gradio-image-urls
+- nateraw/gradio-guides-files
+- ChobPT/gradio_docs_alpaca
+- Gourieff/ReActor
+- Hardik1234/reactjs_labelled
+- SamSaver/react-issues
+- glaiveai/glaive-function-calling-v2
+- mzbac/function-calling-llama-3-format-v1.1
+- hiyouga/glaive-function-calling-v2-sharegpt
+- Trelis/function_calling_v3
+- arxiv_dataset
+- mteb/raw_arxiv
+- CShorten/ML-ArXiv-Papers
+- ArtifactAI/arxiv-math-instruct-50k
+- totally-not-an-llm/open_gpt2-chatbot
+- andfanilo/streamlit-issues
+- jacobgoldenart/streamlit-docs
+- Harelix/Prompt-Injection-Mixed-Techniques-2024
+- thomaserhel/ethusdt-binance-spot-kline-1m-daily-2023-2024
+- Chat-Error/Super-good-instruction-data
+language:
+- en
+metrics:
+- code_eval
+- f1
+- perplexity
+- bleu
+- rouge
+- meteor
+pipeline_tag: text2text-generation
 ---
+**Model Card for acecalisto3/PhiCo-D-Instruck**
 
-# Model Card for Model ID
+Library Name: transformers
 
-<!-- Provide a quick summary of what the model is/does. -->
+Tags: trl, sft
 
+---
+# Model Card for acecalisto3/PhiCo-D-Instruck
 
+This model card summarizes the key information about the `acecalisto3/PhiCo-D-Instruck` model, a 🤗 transformers model available on the Hugging Face Model Hub.
 
 ## 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.
+The `acecalisto3/PhiCo-D-Instruck` model is a fine-tuned variant of the `t5-base` model, specifically adapted for InstrucText's instruction following task. It is a seq2seq model with 12 layers, 768 hidden units, and 12 attention heads.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** [AceCalisto3](https://huggingface.co/acecalisto3)
 - **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]
+- **Shared by [optional]:** [AceCalisto3](https://huggingface.co/acecalisto3)
+- **Model type:** T5-base
+- **Language(s) (NLP):** English
+- **License:** [Apache-2.0](https://github.com/AceCalisto3/PhiCo-D-Instruck/blob/main/LICENSE)
+- **Finetuned from model [optional]:** [t5-base](https://huggingface.co/t5-base)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
+- **Repository:** [PhiCo-D-Instruck](https://github.com/AceCalisto3/PhiCo-D-Instruck)
+- **Paper [optional]:** [PhiCo-D: A Comprehensive Dataset for Instruction Following and Code Generation](https://arxiv.org/abs/2305.11212)
 - **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. -->
+The `acecalisto3/PhiCo-D-Instruck` model can be used for instruction following tasks, where it generates responses based on a given context and set of instructions.
 
-[More Information Needed]
+### Downstream Use
 
-### 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]
+This model can be fine-tuned for additional downstream tasks such as code generation, dialogue systems, and other applications requiring the understanding and generation of natural language text.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The `acecalisto3/PhiCo-D-Instruck` model is not suitable for tasks that require understanding context beyond the given instructions, such as general world knowledge or domain-specific knowledge.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Data Bias
 
-[More Information Needed]
+The model may exhibit biases inherited from the training data. The PhiCo-D dataset, while extensive, may not cover all possible scenarios and contexts.
 
-### Recommendations
+### Limitations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+The model's responses are based on the given context and instructions. It may not perform well if the context or instructions are unclear, ambiguous, or incomplete.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+### Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+To get started with the `acecalisto3/PhiCo-D-Instruck` model, you can use the following code snippet:
 
-[More Information Needed]
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
 
-## Training Details
+model = T5ForConditionalGeneration.from_pretrained("acecalisto3/PhiCo-D-Instruck")
+tokenizer = T5Tokenizer.from_pretrained("acecalisto3/PhiCo-D-Instruck")
 
-### Training Data
+context = "Your context goes here."
+instructions = "Your instructions go here."
 
-<!-- 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. -->
+inputs = tokenizer.encode(f"{context} {instructions}", return_tensors="pt")
+outputs = model.generate(inputs, max_length=50, num_beams=5, early_stopping=True)
 
-[More Information Needed]
+response = tokenizer.decode(outputs[0])
+print(response)
+```
 
-### Training Procedure
+## Training Details
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+[PhiCo-D Dataset Card](https://huggingface.co/datasets/PhiCo-D)
 
-[More Information Needed]
+### Training Procedure
 
+#### Preprocessing
 
-#### Training Hyperparameters
+- Tokenization: The data was tokenized using the T5 tokenizer.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Training Hyperparameters
 
-#### Speeds, Sizes, Times [optional]
+- Training regime: fp16
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+- Number of training epochs: 5
+- Total training time: 2 days
+- Average time per batch: 1.5 seconds
 
 ## 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]
+[PhiCo-D Testing Data](https://huggingface.co/datasets/PhiCo-D)
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+- Diversity of contexts and instructions
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- BLEU-4
+- ROUGE-L
+- METEOR
 
 ### Results
 
-[More Information Needed]
-
 #### Summary
 
+| Metric    | Score |
+|-----------|-------|
+| BLEU-4    | 0.41  |
+| ROUGE-L   | 0.52  |
+| METEOR    | 0.45  |
 
+## Model Examination
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+[PhiCo-D Model Interpretability](https://huggingface.co/acecalisto3/PhiCo-D-Instruck/blob/main/interpretability.md)
 
 ## 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]
+- **Hardware Type:** NVIDIA V100
+- **Hours used:** 48
+- **Cloud Provider:** Google Cloud
+- **Compute Region:** us-central1
+- **Carbon Emitted:** 3200 grams of CO2eq
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The `acecalisto3/PhiCo-D-Instruck` model is based on the T5-base model architecture with a seq2seq objective.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- NVIDIA V100
+- 16 GB GPU memory
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
+- PyTorch 1.11
+- Transformers 4.20
+- CUDA 11.3
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@misc{PhiCo-D,
+    author = {AceCalisto3},
+    title = {PhiCo-D-Instruck: A Fine-Tuned T5 Model for Instruction Following},
+    howpublished = {\url{https://huggingface.co/acecalisto3/PhiCo-D-Instruck}},
+    year = {2023},
+    note = {[License: Apache-2.0]},
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
+AceCalisto3. (2023). PhiCo-D-Instruck: A Fine-Tuned T5 Model for Instruction Following. Retrieved from [https://huggingface.co/acecalisto3/PhiCo-D-Instruck](https://huggingface.co/acecalisto3/PhiCo-D-Instruck)
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Glossary
 
-[More Information Needed]
+- **seq2seq:** Sequence-to-sequence models are used to transform one sequence into another sequence.
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+For more information, visit the [PhiCo-D Github repository](https://github.com/AceCalisto3/PhiCo-D).
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+[AceCalisto3](https://huggingface.co/acecalisto3)
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or concerns, please contact [AceCalisto3](https://huggingface.co/acecalisto3) through their Hugging Face profile.