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]