---
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/)