| Quantization made by Richard Erkhov. |
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| [Github](https://github.com/RichardErkhov) |
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| [Discord](https://discord.gg/pvy7H8DZMG) |
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| [Request more models](https://github.com/RichardErkhov/quant_request) |
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| Python_Ass - GGUF |
| - Model creator: https://huggingface.co/chrisnic/ |
| - Original model: https://huggingface.co/chrisnic/Python_Ass/ |
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| | 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 | |
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| 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 |
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| 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. |
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| ## Model Description |
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| The model has been trained to assist with Python programming tasks through a progressive fine-tuning approach: |
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| ### 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 |
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| ### 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 |
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| ### Training Methodology |
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| The model employs several advanced training techniques to ensure optimal performance: |
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| - **LoRA Fine-tuning Parameters**: |
| - Rank (r): 8 |
| - Alpha: 16 |
| - Dropout: 0.1 |
| - Target Modules: Query and Value Projections |
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| - **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 |
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| ### 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 |
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| ## Intended Uses |
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| 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 |
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| ## Training Data |
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| 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 |
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| ## Performance and Limitations |
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| ### Strengths |
| - Specialized in Python programming tasks |
| - Memory-efficient implementation |
| - Trained with gradient stability monitoring |
| - Optimized for practical coding assistance |
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| ### Limitations |
| - Limited to Python programming language |
| - Based on LLaMA 3.1's knowledge cutoff |
| - May require context for complex programming tasks |
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| ## Usage Tips |
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| 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 |
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| ## Training Hardware Requirements |
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| The model was trained using: |
| - GPU RTX4090 24GB VRAM |
| - CUDA compatibility |
| - Optimized for memory efficiency through 4-bit quantization |
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| ## License and Usage Rights |
| - Base model: LLaMA 3.1 license applies |
| - Additional training: [Specify your license] |
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| ## Citation and Contact |
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| [christiannicoletti75@gmail.com] |
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