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.
