111 lines
3.8 KiB
Markdown
111 lines
3.8 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- unsloth/OpenMathReasoning-mini
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- mlabonne/FineTome-100k
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language:
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- en
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base_model:
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- Qwen/Qwen3-4B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- moe
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- math
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- code
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- text-generation-inference
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- trl
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---
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# Draconis-Qwen3\_Math-4B-Preview
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> **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.
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> [!note]
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GGUF [Q4_K_M] : https://huggingface.co/prithivMLmods/Draconis-Qwen3_Math-4B-Preview-Q4_K_M-GGUF
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> [!note]
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GGUF [Q5_K_M] : https://huggingface.co/prithivMLmods/Draconis-Qwen3_Math-4B-Preview-Q5_K_M-GGUF
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## Key Features
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1. **Mathematical and Logical Reasoning**
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Finetuned to solve symbolic logic, arithmetic, and multi-step mathematical problems, making it ideal for STEM learning, competitions, and educational use.
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2. **Compact Code Understanding**
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Efficient in writing and interpreting code in Python, JavaScript, and other languages, suitable for lightweight coding tasks and algorithmic explanations.
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3. **Factual Precision**
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Trained on high-quality, curated data with reasoning benchmarks to reduce hallucinations and ensure correctness in technical outputs.
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4. **Instruction-Tuned**
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Strong adherence to instructions, ideal for structured queries, step-by-step problem solving, and producing formatted outputs (Markdown, JSON, tables).
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5. **Multilingual Support**
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Capable of understanding and responding in over 20 languages, useful for multilingual education and technical translation.
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6. **Efficient Performance**
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Based on the 4B parameter variant of Qwen3, optimized for resource-constrained environments without compromising core reasoning capability.
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## Quickstart with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Draconis-Qwen3_Math-4B-Preview"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Solve the equation: 3x + 7 = 22. Show all steps."
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messages = [
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{"role": "system", "content": "You are a step-by-step math tutor."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## Intended Use
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* Solving math and logic problems
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* Code assistance and basic debugging
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* Education-focused applications (STEM tutoring)
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* Structured content generation (e.g., JSON, Markdown)
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* Multilingual reasoning and translations
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* Lightweight deployment in reasoning tasks
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## Limitations
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* Limited creativity in open-ended or fictional content
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* May struggle with ambiguous or multi-intent prompts
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* Smaller context window compared to 14B+ variants
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* Still subject to factual errors in edge cases or adversarial queries
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## References
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1. [AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models] : https://arxiv.org/pdf/2504.16891
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2. [YaRN: Efficient Context Window Extension of Large Language Models] : https://arxiv.org/pdf/2309.00071 |