---
library_name: peft
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
tags:
- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: deepseek-r1-python-code-ft
  results: []
thumbnail: https://huggingface.co/N11100/deepseek-r1-python-code-ft/resolve/main/thumbnail.png
github: https://github.com/hubgunter4-ops/deepseek-r1-python-code-ft
inference: true
widget:
- text: "Explain how to secure a linux server."
  example_title: "Security Best Practices"
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# deepseek-r1-python-code-ft

This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- PEFT 0.19.1
- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2