Logics-Qwen3-Math-4B

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.

Key Features

1. Mathematical & Logical Reasoning Fine-tuned for high-precision math reasoning, algorithmic problem-solving, and logical coding tasks.

2. Event-Driven & Probabilistic Modeling Performs probability-based simulations, structured decision-making, and multi-step logical reasoning with strong accuracy.

3. Multilingual Problem Solving Supports math and logic tasks across multiple languages, suitable for global research and education workflows.

4. Hybrid Symbolic-Algorithmic Thinking Combines structured logic, symbolic computation, and probabilistic inference to handle uncertainty-driven problems efficiently.

5. Structured Output Mastery Generates outputs in LaTeX, Markdown, JSON, CSV, and YAML, enabling smooth integration into technical and research workflows.

6. Optimized 4B Parameter Footprint Deployable on mid-range GPUs, offline clusters, and edge devices, maintaining high reasoning quality while being resource-efficient.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Logics-Qwen3-Math-4B"

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

prompt = "Solve the equation x^2 - 5x + 6 = 0 and show all reasoning steps."

messages = [
    {"role": "system", "content": "You are a math and logic tutor skilled in algebra, probability, and structured programming reasoning."},
    {"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

• High-precision mathematical reasoning and problem-solving
• Algorithmic logic, structured coding tasks, and probability analysis
• Educational and research-focused workflows
• Deployment on mid-resource environments with efficient reasoning
• Structured data and technical content generation

Limitations

• Focused on math and logic—less suited for creative writing or casual conversation
• Very complex multi-hop reasoning may challenge the 4B parameter capacity
• Prioritizes structured reasoning over conversational tone
• Outputs may be inconsistent for extremely long or cross-domain multi-document contexts