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Model: sarimahsan101/qwen2.5-7b-thinking-esp
Source: Original Platform

README.md

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+---
+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)