XformAI-india/qwen-0.6b-reasoning

🧠 Qwen-0.6B Reasoning – XformAI Fine-Tuned Model

Model: XformAI-india/qwen-0.6b-reasoning
Base Model: Qwen/Qwen3-0.6B
Architecture: Transformer decoder (GPT-style)
Fine-Tuned By: XformAI
Release Date: May 2025
License: MIT

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🧠 What is it?

qwen-0.6b-reasoning is a compact transformer model fine-tuned for reasoning, logic, and analytical thinking.

Despite its size, it demonstrates strong performance across:

• 🧩 Riddles & Puzzles
• 🧮 Math Word Problems
• 🧠 Symbolic Reasoning
• 💬 Chain-of-Thought Prompting
• 🔍 Common Sense Logic

Fine-tuned on a curated instruction-style dataset focused on multi-step reasoning.

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🚀 Why it Matters

• Performs like a 7B model on reasoning benchmarks
• Lightweight (600M) and can run on CPU or mobile edge devices
• Excels in step-by-step explanations and problem solving

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🧪 Fine-Tuning Overview

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Category	Detail
Base Model	Qwen 0.6B
Target Objective	Reasoning, logic, CoT
Fine-Tuning Type	Instruction
Optimizer	AdamW (LoRA tuning)
Precision	bfloat16
Epochs	2
Max Tokens	2048

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🧩 Prompt Example

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("XformAI-india/qwen-0.6b-reasoning")
tokenizer = AutoTokenizer.from_pretrained("XformAI-india/qwen-0.6b-reasoning")

prompt = "A farmer has 17 sheep. All but 9 run away. How many are left?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))