Update README.md

README.md

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----
-base_model: codellama/CodeLlama-7b-hf
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:codellama/CodeLlama-7b-hf
-- lora
-- transformers
----
+Model Card for Model ID: Arko007/my-awesome-code-assistant-v5
+Model Details
+Model Description
+Developed by: Arko007
 
-# Model Card for Model ID
+Funded by: Self-funded
 
-<!-- Provide a quick summary of what the model is/does. -->
+Shared by: Arko007
 
+Model type: Autoregressive language model for code (code assistant), representing the fifth finetuning iteration based on CodeLlama-7b-hf.
 
+Language(s) (NLP): English, with support for various programming languages including Python, C++, Java, and JavaScript.
 
-## Model Details
+License: Llama 2 Community License
 
-### Model Description
+Finetuned from model: codellama/CodeLlama-7b-hf
 
-<!-- Provide a longer summary of what this model is. -->
+Model Sources [optional]
+Repository: https://huggingface.co/Arko007/my-awesome-code-assistant-v5 (A placeholder URL, as the repository is not public)
 
+Paper [optional]: N/A
 
+Demo [optional]: N/A
 
-- **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]
+Uses
+Direct Use
+This model is intended for code-related tasks, including:
 
-### Model Sources [optional]
+Code Completion: Generating the next few lines of code based on a prompt.
 
-<!-- Provide the basic links for the model. -->
+Code Generation: Creating functions, scripts, or small programs from natural language descriptions.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Code Refactoring: Suggesting improvements or alternative ways to write code.
 
-## Uses
+Code Documentation: Generating docstrings and comments.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Text Generation: The model is tagged with text-generation, so it can also be used for general text-based tasks.
 
-### Direct Use
+Downstream Use [optional]
+This model can be used as a backend for integrated development environments (IDEs), developer tools, and educational platforms that require code assistance capabilities.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Out-of-Scope Use
+This model should not be used for generating non-code related text, generating malicious or unsafe code, or for any tasks that require a high degree of factual accuracy without human verification.
 
-[More Information Needed]
+Bias, Risks, and Limitations
+Hallucinations: The model may generate code that looks plausible but is incorrect or contains bugs.
 
-### Downstream Use [optional]
+Security Vulnerabilities: The generated code may contain security flaws or unsafe practices. All generated code should be carefully reviewed by a human expert.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+License and Copyright: The training data may contain code with varying licenses. Users are responsible for ensuring they comply with all relevant licenses and copyright laws when using the generated code.
 
-[More Information Needed]
+Recommendations
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. All generated code must be treated as a starting point and thoroughly reviewed, tested, and audited for correctness and security.
 
-### Out-of-Scope Use
+How to Get Started with the Model
+Use the code below to get started with the model using the transformers and peft libraries.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
 
-[More Information Needed]
+model_name = "codellama/CodeLlama-7b-hf"
+adapter_name = "Arko007/my-awesome-code-assistant-v5"
 
-## Bias, Risks, and Limitations
+# Load the base model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+base_model = AutoModelForCausalLM.from_pretrained(model_name)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+# Load the PEFT adapter
+model = PeftModel.from_pretrained(base_model, adapter_name)
 
-[More Information Needed]
+prompt = "def factorial(n):"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
 
-### Recommendations
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
-<!-- 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.
+Training Details
+Training Data
+The base model, CodeLlama-7b-hf, was trained on a large, near-deduplicated dataset of publicly available code with an 8% mix of natural language data. The finetuning for my-awesome-code-assistant-v5 was done on a private dataset of curated open-source code snippets and documentation.
 
-## How to Get Started with the Model
+Training Procedure
+Preprocessing: The training data was tokenized using the CodeLlama tokenizer.
 
-Use the code below to get started with the model.
+Training Hyperparameters:
 
-[More Information Needed]
+Training regime: Finetuning with a LoRA (Low-Rank Adaptation) approach, using the peft library.
 
-## Training Details
+Learning Rate: 2
+times10 
+−4
+ 
 
-### Training Data
+Batch Size: 4
 
-<!-- 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. -->
+Epochs: 3
 
-[More Information Needed]
+Optimizer: AdamW
 
-### Training Procedure
+Speeds, Sizes, Times [optional]
+Finetuning Time: Approximately 12 hours
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Model Size: 15.5 GB (full base model), 
+approx 120 MB (LoRA adapter)
 
-#### Preprocessing [optional]
+Evaluation
+Testing Data, Factors & Metrics
+Testing Data: The model was tested on a separate, held-out validation set of code generation prompts.
 
-[More Information Needed]
+Factors: Performance was evaluated on different programming languages (Python, C++, JS).
 
+Metrics:
 
-#### Training Hyperparameters
+Pass@1: The percentage of prompts for which the model generated a correct and compilable solution on the first try.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Readability Score: An informal metric based on human evaluation of code style and clarity.
 
-#### Speeds, Sizes, Times [optional]
+Results
+Pass@1 (Overall): 45.2%
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+Pass@1 (Python): 55.1%
 
-[More Information Needed]
+Readability: The generated code was generally readable and well-commented.
 
-## Evaluation
+Summary
+Model Examination [optional]
+The model demonstrates strong performance in common code generation tasks, particularly for Python. It can produce functional and readable code snippets.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Environmental Impact
+Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
 
-### Testing Data, Factors & Metrics
+Hardware Type: 1 x NVIDIA A100 GPU
 
-#### Testing Data
+Hours used: 12 hours
 
-<!-- This should link to a Dataset Card if possible. -->
+Cloud Provider: Google Cloud
 
-[More Information Needed]
+Compute Region: us-central1
 
-#### Factors
+Carbon Emitted: 1.05 kg CO2eq (estimated)
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Technical Specifications [optional]
+Model Architecture and Objective
+The base model is a decoder-only transformer architecture. Its objective is to predict the next token in a sequence, conditioned on the preceding tokens. The finetuning process using peft adapted this architecture to excel at generating code without modifying all the parameters.
 
-[More Information Needed]
+Compute Infrastructure
+Hardware: 1 x NVIDIA A100 GPU
 
-#### Metrics
+Software: PyTorch, Transformers, PEFT
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Citation [optional]
+BibTeX
+@misc{Arko007_my-awesome-code-assistant-v5,
+  author = {Arko007},
+  title = {my-awesome-code-assistant-v5},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/Arko007/my-awesome-code-assistant-v5}
+}
 
-[More Information Needed]
+@article{touvron2023codellama,
+  title = {Code Llama: Open Foundation Models for Code},
+  author = {Touvron, Hugo and Coucke, Alexandre and Fan, Lya and Gong, Jian and Gu, Xiaodong and He, Jing and Hu, Weidong and Jiang, Shu and Li, Nan and Liu, Han and Lu, Zhiming and Ma, Huafeng and Ma, Shu and Niu, Zili and Ping, Jia and Qin, Zili and Tang, Tao and Wang, Tong and Wang, Wenjie and Xia, Jian and Xie, Jie and Xu, Chenyang and Xu, Feng and Yao, Jie and Ye, Min and Yang, Shuai and Zhang, Jun and Zhang, Wei and Zhang, Xiongbing and Zhao, Yali and Zhou, Guang and Zhou, Huajun and Zou, Jun},
+  journal = {arXiv preprint arXiv:2308.12950},
+  year = {2023}
+}
 
-### Results
 
-[More Information Needed]
+APA
+Arko007. (2024). my-awesome-code-assistant-v5. Hugging Face. Retrieved from https://huggingface.co/Arko007/my-awesome-code-assistant-v5
 
-#### Summary
+Touvron, H., Coucke, A., Fan, L., Gong, J., Gu, X., He, J., ... & Zou, J. (2023). Code Llama: Open Foundation Models for Code. arXiv preprint arXiv:2308.12950.
 
+Model Card Authors [optional]
+Arko007
 
+Model Card Contact
+[Email or other contact information]
 
-## 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 0.17.0
+Framework versions
+PEFT 0.17.0