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:
- Understanding and interacting with RESTful APIs
- Generating code snippets to call APIs based on user questions
- Interpreting JSON REST schemas
- 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
- Base Model: LLaMA-7B-Instruct
- Quantization: The base model was loaded in 4-bit precision using Unsloth for efficient training
- Fine-tuning Method: SFTTrainer (Supervised Fine-Tuning Trainer) was used for the fine-tuning process
- LoRA (Low-Rank Adaptation): The model was fine-tuned using LoRA to generate an adapter
- 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.
