Qwen3.5-4B-Natural_reasoning

Qwen3.5-4B Natural Reasoning Fine-Tuned Model

A fine-tuned version of Qwen/Qwen3.5-4B-Base trained on facebook/natural_reasoning to improve reasoning ability, structured problem solving, and instruction-following performance.

This model was created through supervised fine-tuning (SFT) on reasoning-focused examples, allowing the base model to better understand complex questions, generate more coherent explanations, and follow reasoning patterns more effectively.

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Model Overview

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Intended Use

This model is intended for:

• Reasoning-based question answering
• General-purpose AI assistants
• Educational applications
• Research experiments
• Agentic AI systems
• Local LLM deployment
• Natural language problem solving

Example use cases:

• Answering complex questions
• Explaining concepts step-by-step
• Solving analytical problems
• Building reasoning agents
• Research and experimentation with language models

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Model Capabilities

Improved Reasoning

The fine-tuning process improves the model's ability to:

• Break down complex problems
• Follow logical reasoning patterns
• Produce structured answers
• Explain conclusions clearly
• Handle multi-step questions

Instruction Following

Compared with the original base model, this version is optimized for:

• Better understanding of user instructions
• More helpful responses
• Improved response formatting
• More consistent explanations

Problem Solving

The model is suitable for tasks involving:

• Logical reasoning
• Mathematical thinking
• Analytical questions
• Conceptual explanations
• Multi-step problem solving

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Training Dataset

facebook/natural_reasoning

This model was trained using the facebook/natural_reasoning dataset, which contains reasoning-oriented examples designed to improve language model reasoning capabilities and encourage structured problem solving.

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Training Details

The model was fine-tuned using supervised fine-tuning (SFT).

The model learns from reasoning examples by optimizing the causal language modeling objective, adapting the original Qwen3.5-4B-Base weights toward stronger reasoning behavior.

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Usage

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "YOUR_USERNAME/YOUR_MODEL_NAME"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto"
)

prompt = """
Explain why the Earth has seasons.
"""

inputs = tokenizer(
    prompt,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)

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

print(response)

Recommended Prompting

Use clear natural language instructions. The model performs best when the task and expected output format are clearly described.

Example:

Question:
A car travels 240 kilometers in 3 hours.
What is its average speed?

Answer:

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Limitations

Although this model has improved reasoning capabilities, it still has limitations:

• It can generate incorrect or incomplete answers.
• It does not have real-time access to external information.
• Complex reasoning problems may still contain mistakes.
• Outputs should be verified for critical applications.
• The model may reflect biases present in the training data.

This model should be used as an assistant, not as a replacement for expert judgment.

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Deployment

Recommended inference frameworks:

• Hugging Face Transformers
• vLLM
• llama.cpp (after conversion)
• TensorRT-LLM (after conversion)

Recommended hardware:

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Evaluation

This release focuses on improving reasoning behavior through targeted supervised fine-tuning.

Recommended evaluation benchmarks:

• GSM8K
• MMLU
• ARC
• BIG-Bench reasoning tasks
• Custom reasoning evaluations

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Acknowledgements

Special thanks to:

• The Qwen Team for releasing the Qwen3.5 family of models.
• Facebook AI Research for providing the Natural Reasoning dataset.
• Hugging Face for providing the open-source ML ecosystem.

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Citation

If you use this model in your research, projects, or applications, please cite:

@misc{koohi2026qwen35-natural-reasoning,
  author    = {Reza Koohi},
  title     = {Qwen3.5-4B Natural Reasoning Fine-Tuned Model},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/YOUR_USERNAME/YOUR_MODEL_NAME}
}

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Model Card Authors

Created by: Reza Koohi Hugging Face: https://huggingface.co/YOUR_USERNAME

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License

This model follows the license terms of the original base model: Qwen/Qwen3.5-4B-Base.

Please review the original model license before commercial deployment.

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Note: Replace YOUR_USERNAME and YOUR_MODEL_NAME with your actual Hugging Face repository name before uploading. If you actually used LoRA/QLoRA instead of a full fine-tune, update the "Training Method" and "Training Details" sections accordingly.