---
base_model:
- Qwen/Qwen2.5-Coder-32B-Instruct
library_name: peft
license: cc-by-nc-4.0
datasets:
- Jessylg27/DeepThink-Code-Lite
language:
- en
- fr
tags:
- code
- logic
- reasoning
- qwen2.5
- unsloth
- sft
- trl
---

# Specialized Coding Logic LLM (32B)

This model is a specialized fine-tuned version of [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct).  
It has been optimized to enhance **logical reasoning** and **code generation capabilities**.

## 🧠 Model Description

**Specialized Coding Logic LLM** builds upon the powerful Qwen 2.5 Coder architecture (32B parameters). It has been fine-tuned using the **DeepThink-Code-Lite** dataset to improve its ability to:
- Solve complex algorithmic problems.
- Follow multi-step logical instructions.
- Generate cleaner and more optimized code.

## 📊 Dataset

This model was trained on the custom dataset:  
👉 **[Jessylg27/DeepThink-Code-Lite](https://huggingface.co/datasets/Jessylg27/DeepThink-Code-Lite)**

## 🚀 Quick Start

You can use this model directly with the Hugging Face `pipeline`.

```python
from transformers import pipeline

# Define the model ID
model_id = "Jessylg27/specialized-coding-logic-llm"

# Initialize the pipeline
generator = pipeline("text-generation", model=model_id, device_map="auto")

# Prompt the model
question = "Write a Python function to solve the Traveling Salesman Problem using dynamic programming."
output = generator([{"role": "user", "content": question}], max_new_tokens=512, return_full_text=False)[0]

print(output["generated_text"])

```

## 🛠️ Training procedure

This model was trained with **SFT (Supervised Fine-Tuning)** using the [TRL library](https://github.com/huggingface/trl) and [Unsloth](https://github.com/unslothai/unsloth) for efficient training.

### Framework versions

* **PEFT:** 0.18.1
* **TRL:** 0.24.0
* **Transformers:** 4.57.3
* **Pytorch:** 2.8.0+cu128
* **Datasets:** 4.3.0
* **Tokenizers:** 0.22.2

## 📜 Citations

If you use this model or the TRL library, please cite:

```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{[https://github.com/huggingface/trl](https://github.com/huggingface/trl)}}
}

```