Quantization made by Richard Erkhov.
Python_Ass - GGUF
Original model description:
license: llama3.1
language:
- en
- it
base_model:
- meta-llama/Llama-3.1-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- code
Python Code Assistant based on LLaMA 3.1
This model is a specialized Python coding assistant, fine-tuned from LLaMA 3.1 8B Instruct using a two-stage training approach with carefully curated Python programming datasets.
Model Description
The model has been trained to assist with Python programming tasks through a progressive fine-tuning approach:
First Training Stage
- Base Model: LLaMA 3.1 8B Instruct
- Dataset: iamtarun/python_code_instructions_18k_alpaca
- Training Focus: Understanding Python programming instructions and generating appropriate code responses
Second Training Stage
- Dataset: flytech/python-codes-25k
- Focus: Enhancing code generation capabilities and understanding of advanced Python concepts
Training Methodology
The model employs several advanced training techniques to ensure optimal performance:
-
LoRA Fine-tuning Parameters:
- Rank (r): 8
- Alpha: 16
- Dropout: 0.1
- Target Modules: Query and Value Projections
-
Training Optimizations:
- 4-bit quantization (NF4 format)
- Gradient checkpointing
- Dynamic learning rate adjustment
- Early stopping with patience=3
- Adaptive batch processing
- Memory-efficient training with automated cleanup
Model Architecture
- Base Architecture: LLaMA 3.1 8B Instruct
- Training Format: 4-bit quantization with double quantization
- Memory Efficient: Optimized for deployment with reduced memory footprint
Intended Uses
This model is designed for:
- Generating Python code from natural language descriptions
- Assisting with code completion and suggestions
- Explaining Python concepts and best practices
- Helping with code debugging and optimization
- Supporting Python development tasks
Training Data
The model was trained on a combination of:
- 18,000 Python programming instructions and implementations from the Alpaca dataset
- 25,000 Python code examples and explanations
Performance and Limitations
Strengths
- Specialized in Python programming tasks
- Memory-efficient implementation
- Trained with gradient stability monitoring
- Optimized for practical coding assistance
Limitations
- Limited to Python programming language
- Based on LLaMA 3.1's knowledge cutoff
- May require context for complex programming tasks
Usage Tips
To get the best results from this model:
- Provide clear and specific instructions
- Include relevant context when asking for code
- Specify any particular Python version or library requirements
- Mention any performance or style preferences
Training Hardware Requirements
The model was trained using:
- GPU RTX4090 24GB VRAM
- CUDA compatibility
- Optimized for memory efficiency through 4-bit quantization
License and Usage Rights
- Base model: LLaMA 3.1 license applies
- Additional training: [Specify your license]
Citation and Contact
