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--- |
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license: apache-2.0 |
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tags: |
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- trl |
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- math |
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- text-generation-inference |
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- code |
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datasets: |
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- nvidia/OpenCodeReasoning |
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- nvidia/OpenMathReasoning |
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- prithivMLmods/Helios-R-6M |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-4B-Thinking-2507 |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# **Logics-Qwen3-Math-4B** |
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> **Logics-Qwen3-Math-4B** is a reasoning-focused model fine-tuned on **Qwen3-4B-Thinking-2507** for **mathematical reasoning** and **logical coding**, trained on **OpenMathReasoning**, **OpenCodeReasoning**, and **Helios-R-6M** datasets. It excels in structured **mathematical problem solving**, **algorithmic logic**, and **probabilistic reasoning**, making it ideal for educators, researchers, and developers focused on computational logic and math. |
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## **Key Features** |
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1. **Mathematical & Logical Reasoning** |
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Fine-tuned for high-precision math reasoning, algorithmic problem-solving, and logical coding tasks. |
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2. **Event-Driven & Probabilistic Modeling** |
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Performs probability-based simulations, structured decision-making, and multi-step logical reasoning with strong accuracy. |
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3. **Multilingual Problem Solving** |
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Supports math and logic tasks across multiple languages, suitable for global research and education workflows. |
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4. **Hybrid Symbolic-Algorithmic Thinking** |
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Combines structured logic, symbolic computation, and probabilistic inference to handle uncertainty-driven problems efficiently. |
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5. **Structured Output Mastery** |
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Generates outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, enabling smooth integration into technical and research workflows. |
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6. **Optimized 4B Parameter Footprint** |
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Deployable on **mid-range GPUs**, **offline clusters**, and **edge devices**, maintaining high reasoning quality while being resource-efficient. |
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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/Logics-Qwen3-Math-4B" |
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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 x^2 - 5x + 6 = 0 and show all reasoning steps." |
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messages = [ |
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{"role": "system", "content": "You are a math and logic tutor skilled in algebra, probability, and structured programming reasoning."}, |
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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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* High-precision mathematical reasoning and problem-solving |
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* Algorithmic logic, structured coding tasks, and probability analysis |
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* Educational and research-focused workflows |
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* Deployment on mid-resource environments with efficient reasoning |
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* Structured data and technical content generation |
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## **Limitations** |
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* Focused on math and logic—less suited for creative writing or casual conversation |
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* Very complex multi-hop reasoning may challenge the 4B parameter capacity |
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* Prioritizes structured reasoning over conversational tone |
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* Outputs may be inconsistent for extremely long or cross-domain multi-document contexts |
