Create README.md

README.md

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+---
+language:
+- en
+tags:
+- llama
+- instruct
+- conversational
+- api
+- code-generation
+- lora
+license: apache-2.0
+---
+
+# LLaMA-7B-Instruct-API-Coder
+
+## Model Description
+
+This model is a fine-tuned version of the LLaMA-7B-Instruct model, specifically trained on conversational data related to RESTful API usage and code generation. The training data was generated by LLaMA-70B-Instruct, focusing on API interactions and code creation based on user queries and JSON REST schemas.
+
+## Intended Use
+
+This model is designed to assist developers and API users in:
+
+1. Understanding and interacting with RESTful APIs
+2. Generating code snippets to call APIs based on user questions
+3. Interpreting JSON REST schemas
+4. Providing conversational guidance on API usage
+
+## Training Data
+
+The model was fine-tuned on a dataset of conversational interactions generated by LLaMA-70B-Instruct. This dataset includes:
+
+- Discussions about RESTful API concepts
+- Examples of API usage
+- Code generation based on API schemas
+- Q&A sessions about API integration
+
+## Training Procedure
+
+1. Base Model: LLaMA-7B-Instruct
+2. Quantization: The base model was loaded in 4-bit precision using Unsloth for efficient training
+3. Fine-tuning Method: SFTTrainer (Supervised Fine-Tuning Trainer) was used for the fine-tuning process
+4. LoRA (Low-Rank Adaptation): The model was fine-tuned using LoRA to generate an adapter
+5. Merging: The LoRA adapter was merged back with the original model to create the final fine-tuned version
+
+This approach allows for efficient fine-tuning while maintaining model quality and reducing computational requirements.
+
+## Limitations
+
+- The model's knowledge is limited to the APIs and schemas present in the training data
+- It may not be up-to-date with the latest API standards or practices
+- The generated code should be reviewed and tested before use in production environments
+- Performance may vary compared to the full-precision model due to 4-bit quantization
+
+## Ethical Considerations
+
+- The model should not be used to access or manipulate APIs without proper authorization
+- Users should be aware of potential biases in the generated code or API usage suggestions
+
+## Additional Information
+
+- Model Type: Causal Language Model
+- Language: English
+- License: Apache 2.0
+- Fine-tuning Technique: LoRA (Low-Rank Adaptation)
+- Quantization: 4-bit precision
+
+For any questions or issues, please open an issue in the GitHub repository.