the1ullneversee/Restful-Llama-3-7b

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.