Plaban81/codegen-finetuned-python
Plaban81/codegen-finetuned-python is a 7 billion parameter Llama-2 based causal language model fine-tuned by Plaban81 for Python code generation. Utilizing QLoRA in 4-bit quantization, this model specializes in generating Python code from instructions. It was trained on the python_code_instructions_18k_alpaca dataset, making it highly effective for code-related tasks.
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Overview
This model, Plaban81/codegen-finetuned-python, is a 7 billion parameter variant of Meta's Llama-2 architecture. It has been specifically fine-tuned using the QLoRA method with 4-bit quantization and the PEFT library to excel at generating Python code. The training leveraged the python_code_instructions_18k_alpaca dataset, which comprises problem descriptions paired with Python code solutions, formatted in an Alpaca-style instruction format.
Key Capabilities
- Python Code Generation: Highly optimized for generating Python code based on given instructions.
- Instruction Following: Fine-tuned on an instruction-based dataset, enabling it to understand and respond to coding prompts effectively.
- Efficient Deployment: Trained with 4-bit QLoRA, making it suitable for environments with limited computational resources.
Good for
- Developers seeking an efficient, smaller-scale model for Python code generation tasks.
- Applications requiring code snippets or full function implementations in Python.
- Experimentation with fine-tuned Llama-2 models for specialized programming tasks.