Instructions to use Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B with Transformers:

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

pipe = pipeline("text-generation", model="Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B")
model = AutoModelForCausalLM.from_pretrained("Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B")
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 Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B

SGLang

How to use Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B 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 "Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B" \
    --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": "Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B",
		"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 "Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B" \
        --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": "Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B with Docker Model Runner:
```
docker model run hf.co/Jasaxion/MathSmith-HC-Problem-Synthesizer-Qwen3-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MathSmith-HC-Problem-Synthesizer-Qwen3-8B

MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy

Overview

MathSmith is a framework for synthesizing challenging mathematical problems to enhance LLM reasoning. This model is a reinforced policy-based synthesizer optimized to generate novel, Olympiad-level mathematical problems from scratch.

The model generates <rationale>–<problem> pairs, where:

<rationale>: structured reasoning describing concept integration and difficulty design strategies.
<problem>: a single Olympiad-level mathematical question that admits a verifiable numeric or symbolic answer.

MathSmith-HC (High Consistency) combines complexity and consistency as difficulty rewards during reinforcement learning, producing more stable problems than the version optimized solely for complexity.

MathSmith Pipeline

The MathSmith framework consists of four main stages:

Concept Collection: Randomly sample concept–explanation pairs from PlanetMath to ensure data independence and avoid benchmark contamination.
Supervised Fine-tuning (SFT): Train the model on collected concept–explanation pairs to establish foundational understanding of problem generation.
Reinforcement Learning (RL): Optimize the model using GRPO with rewards based on structural validity, reasoning complexity (trace length), and answer consistency.
Weakness-Focused Self-Improvement: Iteratively identify and address model weaknesses by generating targeted problem variants for specific mathematical concepts.

Dependence

Transformers 4.52.4
Pytorch 2.7.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

Citation

If you find this work useful, please cite:

@article{zhan2025mathsmith,
  title={MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy},
  author={Zhan, Shaoxiong and Lai, Yanlin and Lu, Ziyu and Lin, Dahua and Yang, Ziqing and Tan, Fei},
  journal={arXiv preprint arXiv:2508.05592},
  year={2025}
}