01Coder Readme

README.md

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 ---
-license: apache-2.0
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+tags:
+- code-llm
+- mistral-7b
+- language-model
 ---
+
+# Model Card for 01Coder 7B
+
+This model card provides details about a code language model (LLM) based on Mistral 7B architecture. It has been trained on a combination of three datasets: ise-uiuc/Magicoder-OSS-Instruct-75K, HuggingFaceH4/CodeAlpaca_20K, and theblackcat102/evol-codealpaca-v1.
+
+## Model Details
+
+### Model Description
+
+This model is a language model fine-tuned for code generation tasks, leveraging the Mistral 7B base model architecture. It has been trained on a combination of three datasets, namely Magicoder-OSS-Instruct-75K, CodeAlpaca_20K, and evol-codealpaca-v1. The model aims to assist developers in generating code snippets for various programming tasks, ranging from natural language instructions to specific coding prompts.
+
+- **Developed by:** Manoj Athreya A
+- **Model type:** Language model (LLM)
+- **License:** [Apache 2.0 License]
+- **Finetuned from model:** Mistral 7B
+
+
+## Intended Uses
+
+- Code generation from natural language prompts.
+- Assisting developers in completing code snippets.
+- Augmenting code-related tasks with automated generation capabilities.
+
+## Limitations and Ethical Considerations
+
+- **Bias:** As with any language model, biases present in the training data may manifest in the generated code snippets.
+- **Accuracy:** While the model aims to generate accurate code, it may occasionally produce incorrect or suboptimal solutions, especially for complex tasks.
+- **Security:** Generated code should be reviewed for security vulnerabilities, as the model may inadvertently produce insecure implementations.
+- **Ethical Use:** Users are encouraged to employ the model responsibly and ethically, avoiding harmful or malicious use cases.
+
+### Recommendations
+
+- Fine-tuning the model on specific domains or tasks may improve its performance.
+- Validate generated code in real-world scenarios to ensure its correctness and reliability.
+- Provide feedback to continuously improve the model's performance and address any issues encountered during usage.
+
+## License
+
+- The source code in this repo is licensed under the Apache 2.0 license. 
+
+## Version History
+
+- 01-Coder-7Bv0.1