TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged
TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged is a 7 billion parameter Llama 2 chat model fine-tuned on 122,000 code instructions. This model is specifically optimized for generating boilerplate code and handling code-related tasks. It demonstrates strong performance in early experiments for code instruction following, making it suitable for developers needing a compact, efficient code generation assistant.
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Model Overview
TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged is a 7 billion parameter Llama 2 chat model, fine-tuned using QLoRA on 122,000 code instructions. Despite its relatively small size, early experiments indicate strong performance in code-related tasks, particularly for generating boilerplate code.
Key Capabilities
- Code Instruction Following: Excels at responding to prompts related to code generation and understanding.
- Efficiency: Designed to be valuable even after quantization, potentially running locally with as little as 4GB RAM.
- Llama 2 Architecture: Leverages the robust Llama 2 base model for its conversational and generative abilities.
Development Plans & Future Improvements
The developer plans to further enhance the model by:
- Quantization: Providing quantized versions for improved local deployment and reduced memory footprint.
- Instruction Tuning Style: Switching from Alpaca-style to Llama 2's
[INST]<<SYS>>style instruction tuning to potentially boost performance. - Evaluation: Conducting HumanEval reports and checking for training data leaks to ensure robustness and fairness.
- Context Extension: Experimenting with 8k context length via RoPE enhancement to assess performance impact.
Commercial Use
The model is considered suitable for commercial use, though users should be aware of Meta's Llama 2 licensing terms, as this model is an adapter built upon it.