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