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

Mathematical & Logical Reasoning Fine-tuned for high-precision math reasoning, algorithmic problem-solving, and logical coding tasks.
Event-Driven & Probabilistic Modeling Performs probability-based simulations, structured decision-making, and multi-step logical reasoning with strong accuracy.
Multilingual Problem Solving Supports math and logic tasks across multiple languages, suitable for global research and education workflows.
Hybrid Symbolic-Algorithmic Thinking Combines structured logic, symbolic computation, and probabilistic inference to handle uncertainty-driven problems efficiently.
Structured Output Mastery Generates outputs in LaTeX, Markdown, JSON, CSV, and YAML, enabling smooth integration into technical and research workflows.
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

python

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