mrm8488/llama-2-coder-7b
The mrm8488/llama-2-coder-7b is a 7 billion parameter Llama 2 model fine-tuned by mrm8488 for code generation tasks. It leverages the QLoRA method on the CodeAlpaca 20k instructions dataset, specializing in following coding instructions. This model is optimized for developers seeking an efficient, instruction-tuned language model for various programming assistance needs, offering a 4096-token context length.
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Overview
The mrm8488/llama-2-coder-7b is a 7 billion parameter Llama 2 model, fine-tuned specifically for code-related tasks. Developed by mrm8488, this model utilizes the QLoRA method with the PEFT library, building upon the foundational Llama 2 architecture by Meta.
Key Capabilities
- Instruction-Following Code Generation: Fine-tuned on the CodeAlpaca_20K dataset, it excels at understanding and executing coding instructions.
- Efficient Fine-tuning: Employs QLoRA for efficient adaptation of the base Llama 2 model.
- Dialogue Optimization: Inherits Llama 2's optimization for dialogue use cases, making it suitable for interactive coding assistance.
Training Details
The model was trained for 2 epochs with a learning rate of 2e-4, using paged_adamw_32bit optimizer and fp16 precision. Validation loss decreased steadily during training, indicating effective learning.
Good For
- Code Generation: Ideal for generating code snippets based on natural language instructions.
- Coding Assistant Applications: Can be integrated into tools requiring an AI to help resolve coding instructions.
- XML Editing: Demonstrated capability in editing XML code based on instructions, suggesting broader applicability to various code formats.