Draconis-Qwen3_Math-4B-Preview

Draconis-Qwen3_Math-4B-Preview is fine-tuned on the Qwen3-4B architecture, optimized for excellence in mathematical reasoning, logical problem solving, and structured content generation. This preview model focuses on precision, step-by-step reasoning, and efficient inference, making it ideal for educational and technical applications where reliability and compact performance are essential.

GGUF [Q4_K_M] : https://huggingface.co/prithivMLmods/Draconis-Qwen3_Math-4B-Preview-Q4_K_M-GGUF

GGUF [Q5_K_M] : https://huggingface.co/prithivMLmods/Draconis-Qwen3_Math-4B-Preview-Q5_K_M-GGUF

Key Features

1. Mathematical and Logical Reasoning Finetuned to solve symbolic logic, arithmetic, and multi-step mathematical problems, making it ideal for STEM learning, competitions, and educational use.

2. Compact Code Understanding Efficient in writing and interpreting code in Python, JavaScript, and other languages, suitable for lightweight coding tasks and algorithmic explanations.

3. Factual Precision Trained on high-quality, curated data with reasoning benchmarks to reduce hallucinations and ensure correctness in technical outputs.

4. Instruction-Tuned Strong adherence to instructions, ideal for structured queries, step-by-step problem solving, and producing formatted outputs (Markdown, JSON, tables).

5. Multilingual Support Capable of understanding and responding in over 20 languages, useful for multilingual education and technical translation.

6. Efficient Performance Based on the 4B parameter variant of Qwen3, optimized for resource-constrained environments without compromising core reasoning capability.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Draconis-Qwen3_Math-4B-Preview"

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

prompt = "Solve the equation: 3x + 7 = 22. Show all steps."

messages = [
    {"role": "system", "content": "You are a step-by-step math tutor."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Intended Use

• Solving math and logic problems
• Code assistance and basic debugging
• Education-focused applications (STEM tutoring)
• Structured content generation (e.g., JSON, Markdown)
• Multilingual reasoning and translations
• Lightweight deployment in reasoning tasks

Limitations

• Limited creativity in open-ended or fictional content
• May struggle with ambiguous or multi-intent prompts
• Smaller context window compared to 14B+ variants
• Still subject to factual errors in edge cases or adversarial queries

References

1. [AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models] : https://arxiv.org/pdf/2504.16891
2. [YaRN: Efficient Context Window Extension of Large Language Models] : https://arxiv.org/pdf/2309.00071