qwen2.5-7b-thinking-esp/README.md

language, license, base_model, tags, model-index, pipeline_tag

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

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🚀 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.

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🏗️ 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

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🎯 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

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⚠️ 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

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📦 How to Use

🔹 Load with Unsloth

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)