Instructions to use prithivMLmods/Logics-Qwen3-Math-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Logics-Qwen3-Math-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Logics-Qwen3-Math-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Logics-Qwen3-Math-4B")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Logics-Qwen3-Math-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Logics-Qwen3-Math-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Logics-Qwen3-Math-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Logics-Qwen3-Math-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Logics-Qwen3-Math-4B

SGLang

How to use prithivMLmods/Logics-Qwen3-Math-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Logics-Qwen3-Math-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Logics-Qwen3-Math-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Logics-Qwen3-Math-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Logics-Qwen3-Math-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Logics-Qwen3-Math-4B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Logics-Qwen3-Math-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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

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

Downloads last month: 51

Safetensors

Model size

4B params

Tensor type

F16

Model tree for prithivMLmods/Logics-Qwen3-Math-4B

Base model

Qwen/Qwen3-4B-Thinking-2507

Finetuned

(232)

this model

Merges

1 model

Quantizations

3 models

prithivMLmods
/

Logics-Qwen3-Math-4B