---
language:
- es
- fr
- en
license: apache-2.0
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- unsloth
- trl
- lora
- reasoning
- chain-of-thought
- multilingual
- instruction-tuned
- qwen
model-index:
- name: Qwen2.5-7B-Thinking-Spanish-French
  results: []
pipeline_tag: text-generation
---

# 🧠 Qwen2.5-7B-Thinking-Spanish-French (LoRA)

A lightweight, reasoning-enhanced multilingual model fine-tuned for **step-by-step thinking in Spanish and French**, built on top of Qwen2.5-7B-Instruct using LoRA.

---

## 🚀 Overview

This model enhances the reasoning capabilities of the base model by encouraging structured "thinking" before answering. It is optimized for:

- 🇪🇸 Spanish reasoning tasks  
- 🇫🇷 French reasoning tasks  
- 🧠 Step-by-step logical explanations  
- 💬 Instruction-following with personality  

The fine-tuning process leverages curated multilingual reasoning datasets to improve coherence, clarity, and depth in responses.

---

## 🏗️ Model Details

| Component               | Description                                                     |
|------------------------|-----------------------------------------------------------------|
| **Base Model**         | Qwen2.5-7B-Instruct                                             |
| **Fine-tuning**        | LoRA (Low-Rank Adaptation) via Unsloth                          |
| **Dataset**            | HuggingFaceH4/Multilingual-Thinking (Spanish & French filtered) |
| **Quantization**       | 4-bit (bitsandbytes)                                            |
| **Max Sequence Length**| 512 tokens                                                      |
| **Framework**          | TRL + Unsloth                                                   |

---

## 🎯 Capabilities

- Generates **chain-of-thought reasoning**  
- Produces **structured, step-by-step answers**  
- Handles **multilingual prompts (ES/FR/EN)**  
- Maintains **engaging and expressive tone**  
- Efficient inference with **low VRAM usage**  

---

## ⚠️ Limitations

- Context limited to **512 tokens** → long reasoning may truncate  
- Performance may degrade for:
  - highly technical domains (e.g., legal/medical)
  - languages outside ES/FR/EN  
- Chain-of-thought is learned behavior → may not always be consistent  

---

## 📦 How to Use

### 🔹 Load with Unsloth

```python
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "sarimahsan101/qwen2.5-7b-thinking-esp",
    max_seq_length = 512,
    load_in_4bit = True,
)

FastLanguageModel.for_inference(model)