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[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Python_Ass - GGUF
- Model creator: https://huggingface.co/chrisnic/
- Original model: https://huggingface.co/chrisnic/Python_Ass/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Python_Ass.Q2_K.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q2_K.gguf) | Q2_K | 2.96GB |
| [Python_Ass.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Python_Ass.IQ3_S.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Python_Ass.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Python_Ass.IQ3_M.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Python_Ass.Q3_K.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q3_K.gguf) | Q3_K | 3.74GB |
| [Python_Ass.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Python_Ass.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Python_Ass.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Python_Ass.Q4_0.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Python_Ass.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Python_Ass.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Python_Ass.Q4_K.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q4_K.gguf) | Q4_K | 4.58GB |
| [Python_Ass.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Python_Ass.Q4_1.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Python_Ass.Q5_0.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Python_Ass.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Python_Ass.Q5_K.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q5_K.gguf) | Q5_K | 5.34GB |
| [Python_Ass.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Python_Ass.Q5_1.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Python_Ass.Q6_K.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q6_K.gguf) | Q6_K | 6.14GB |
| [Python_Ass.Q8_0.gguf](https://huggingface.co/RichardErkhov/chrisnic_-_Python_Ass-gguf/blob/main/Python_Ass.Q8_0.gguf) | Q8_0 | 7.95GB |
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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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:
1. 18,000 Python programming instructions and implementations from the Alpaca dataset
2. 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:
1. Provide clear and specific instructions
2. Include relevant context when asking for code
3. Specify any particular Python version or library requirements
4. 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
[christiannicoletti75@gmail.com]
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