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💡 The Idea

Good answers come from good thinking.

Qwen-R1-0.5B is a fine-tuned version of Qwen2.5-0.5B-Instruct trained to reason before it answers using explicit <thinking> tags.

Instead of jumping straight to the answer, this model generates its reasoning first — making it more transparent, more reliable, and easier to debug.

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🧠 How It Works

Every response is structured as:

User: {question}
Assistant: <thinking>{reasoning}</thinking>
{answer}

The model learns to:

1. Think – generate step-by-step reasoning
2. Answer – provide the final response

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📊 Training Details

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🚀 Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "WhirlwindAI/Qwen-R1-0.5B"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

prompt = "User: What is 2+2?\nAssistant: <thinking>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.7)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📋 Sample Output

User: What is the capital of France?
Assistant: <thinking>Paris is the capital of France.</thinking>
Paris

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📈 Performance

The model was evaluated on 10 out-of-distribution questions:

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🔬 What It Learned

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🧪 Test It Yourself

questions = [
    "What is the capital of France?",
    "Explain entropy like I'm 5.",
    "Write a short poem about a robot.",
]

for q in questions:
    prompt = f"User: {q}\nAssistant: <thinking>"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=80)
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📜 Citation

@model{qwenr1_2026,
  title={Qwen-R1-0.5B},
  author={WhirlwindAI},
  year={2026},
  publisher={Hugging Face}
}

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🌪️ WhirlwindAI

Efficient Models • Practical Research • Open AI