| --- |
| license: mit |
| datasets: |
| - iamtarun/python_code_instructions_18k_alpaca |
| language: |
| - en |
| - fr |
| base_model: |
| - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
| pipeline_tag: text-generation |
| library_name: mlx |
| tags: |
| - code |
| - python |
| - deepseek |
| - fine-tuned |
| - lora |
| --- |
| # DeepSeek-R1-Distill-Qwen-7B — Python Code Fine-tune |
|
|
| A LoRA fine-tuned version of [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) specialized for Python code generation. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Developed by:** Armand (@ArmanS11) |
| - **Model type:** Large Language Model — LoRA fine-tune |
| - **Language(s):** English |
| - **License:** MIT |
| - **Finetuned from:** [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) |
|
|
| ### Model Sources |
|
|
| - **Base model:** https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
| - **Training dataset:** https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| Generate Python code from natural language instructions. Examples: |
| - Writing functions, classes, algorithms |
| - Async/await patterns |
| - Data structures and error handling |
|
|
| ### Out-of-Scope Use |
|
|
| - Not intended for other programming languages |
| - Not suitable for production security-critical code without review |
|
|
| ## Bias, Risks, and Limitations |
|
|
| Generated code should always be reviewed before use in production. The model may occasionally produce syntactically incorrect code, particularly for complex async patterns. |
|
|
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) — 18,612 Python code instruction/response pairs. |
|
|
| - **Train split:** 17,681 examples |
| - **Validation split:** 931 examples |
|
|
| ### Training Hyperparameters |
|
|
| | Parameter | Value | |
| |---|---| |
| | Method | LoRA | |
| | LoRA Rank | 8 | |
| | LoRA Layers | 8 | |
| | Learning Rate | 5e-6 | |
| | Batch Size | 2 | |
| | Iterations | 2000 | |
| | Quantization | 4-bit | |
|
|
| ## Technical Specifications |
|
|
| ### Compute Infrastructure |
|
|
| #### Hardware |
| - Apple MacBook Pro M4 — 16 GB unified memory |
|
|
| #### Software |
| - MLX (Apple Silicon optimized) |
| - M-Courtyard fine-tuning app |
|
|
| ## Model Card Authors |
|
|
| Armand — [@ArmandS11](https://huggingface.co/ArmandS11/) |
