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README.md

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+Quantization made by Richard Erkhov.
+
+[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]
+