Math Mini 0.6B (Preview) is a compact, specialized model developed by Enosis Labs as part of the "Mini Series." It is designed to deliver efficient and precise mathematical reasoning, with a realistic and practical focus for its size. This model is fine-tuned from unsloth/Qwen3-0.6B-unsloth-bnb-4bit.

Philosophy & Capabilities

The Mini Series, along with the "Enosis Math" and "Enosis Code" models, incorporates step-by-step reasoning by default, enabling more efficient, clear, and well-founded answers. All models in the Math series have been trained with carefully curated step-by-step problem-solving datasets, resulting in a greater ability to reason and explain solutions in a structured way.

Math Mini 0.6B (Preview) is optimized for:

Basic Algebra: Solving equations and manipulating expressions.
Arithmetic & Sequential Reasoning: Calculations and breaking down problems into logical steps.
Elementary Logic: Applying deduction in mathematical contexts.
Introductory Competition Problem Solving: Focus on foundational skills adapted to the model's scale.

Larger models in the "Enosis Math" series address advanced topics such as calculus, higher algebra, and olympiad problems. The "Code Mini" and "Enosis Code" series are oriented towards programming and algorithmic tasks, maintaining the same philosophy of explicit and efficient reasoning.

This model is a preview version and is under continuous improvement and evaluation.

Quick Start

Available in Hugging Face Transformers format and for high-throughput inference servers like vLLM.

vLLM (Inference Server)

Install vLLM:

pip install vllm

Start the vLLM server with the model (16-bit version):

vllm serve "enosislabs/math-mini-0.6b-preview-16bits"

Call the server using curl:

bash

curl -X POST "http://localhost:8000/v1/chat/completions" \
    -H "Content-Type: application/json" \
    --data '{
        "model": "enosislabs/math-mini-0.6b-preview-16bits",
        "messages": [
            {"role": "user", "content": "What is the capital of France?"}
        ]
    }'

Transformers (Hugging Face)

Use a pipeline as a high-level helper:

python

from transformers import pipeline

pipe = pipeline("text-generation", model="enosislabs/math-mini-0.6b-preview-16bits")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

Prompt Format (Qwen3 ChatML)

For best results, use the Qwen3 ChatML format. The tokenizer.apply_chat_template method handles this automatically.

text

<|im_start|>system
You are a helpful AI assistant. Provide a detailed step-by-step solution.
<|im_end|>
<|im_start|>user
{user_question}
<|im_end|>
<|im_start|>assistant

Acknowledgements

Fine-tuned from unsloth/Qwen3-0.6B-unsloth-bnb-4bit.
Training process accelerated and optimized thanks to Unsloth.

Citation

If you use this model, please cite:

bibtex

@software{enosislabs_math_mini_0.6b_preview_2025,
  author = {{Enosis Labs}},
  title = {{Math Mini 0.6B (Preview)}},
  year = {2025},
  publisher = {Hugging Face},
  version = {0.1-preview},
  url = {https://huggingface.co/enosislabs/math-mini-0.6b-preview-16bits}
}