SachinKaushik/llama-2-7b-instruct-pyCode-4bitshards
The SachinKaushik/llama-2-7b-instruct-pyCode-4bitshards model is a 7 billion parameter Llama 2-based instruction-tuned language model. It is specifically optimized for Python code generation and understanding, leveraging 4-bit quantization for efficient deployment. This model is designed for tasks requiring code-related instruction following and generation within a 4096-token context window.
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Model Overview
This model, SachinKaushik/llama-2-7b-instruct-pyCode-4bitshards, is an instruction-tuned variant of the Llama 2 7B parameter model. It has been specifically fine-tuned with an emphasis on Python code-related tasks, making it suitable for developers and applications focused on programming.
Key Technical Details
- Base Model: Llama 2 (7 billion parameters)
- Quantization: Utilizes
bitsandbytes4-bit quantization (nf4type) for reduced memory footprint and efficient inference. - Instruction-Tuned: Designed to follow instructions effectively, particularly for code-centric prompts.
- Context Window: Supports a context length of 4096 tokens.
Training Configuration
The model was trained using bitsandbytes with load_in_4bit: True and bnb_4bit_quant_type: nf4, indicating a focus on efficient deployment and performance. The bnb_4bit_compute_dtype was set to float16 during training. The PEFT framework version 0.4.0 was used in the training procedure.
Ideal Use Cases
This model is particularly well-suited for:
- Python Code Generation: Generating Python code snippets, functions, or scripts based on natural language descriptions.
- Code Explanation: Providing explanations for existing Python code.
- Instruction Following: Executing code-related instructions and tasks efficiently.
- Resource-Constrained Environments: Its 4-bit quantization makes it a good candidate for deployment where memory or computational resources are limited.