Instructions to use dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen with Transformers:

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

pipe = pipeline("text-generation", model="dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen")
model = AutoModelForCausalLM.from_pretrained("dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen")
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 dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen

SGLang

How to use dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen 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 "dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen" \
    --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": "dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen",
		"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 "dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen" \
        --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": "dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen with Docker Model Runner:
```
docker model run hf.co/dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch

Model Card for ScaleQuest-Qwen2-Math-7B-QGen

We introduce ScaleQuest, a scalable and novel data synthesis method that utilizes small-size open-source models to generate questions from scratch without the need for seed data with complex augmentation constraints.

📑 Project Page: https://scalequest.github.io
💻 Code: https://github.com/yyDing1/ScaleQuest
📖 Paper: Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
💾 Models in the 🤗 HuggingFace Hub: ScaleQuest-Models

Datasets & Models

Math Dataset: link

We release two question generator models and four problem-solving models.

Model	Type	MATH	Olympiad Bench	🤗 HuggingFace Download Link
ScaleQuest-DeepSeekMath-7B-QGen	question generator	-	-	link
ScaleQuest-Qwen2-Math-7B-QGen	question generator	-	-	link
Mistral-7B-ScaleQuest	problem solver	62.9	26.8	link
Llama3-8B-ScaleQuest	problem solver	64.4	25.3	link
DeepSeekMath-7B-ScaleQuest	problem solver	66.6	29.9	link
Qwen2-Math-7B-ScaleQuest	problem solver	73.4	38.5	link

Demo usage

Below is an example using ScaleQuest-Qwen2-Math-7B-QGen

from vllm import LLM, SamplingParams

model_name = "dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen"

pre_query_template = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n"
stop_tokens = ["<|im_start|>", "<|im_end|>", "<|endoftext|>"]

llm = LLM(
    model=model_name,
    tokenizer=model_name,
    tensor_parallel_size=1,
    max_model_len=4096,
    enable_prefix_caching=True,
    trust_remote_code=True,
    swap_space=16,
    gpu_memory_utilization=0.95,
)
sampling_params = SamplingParams(
    n=4,
    max_tokens=1024,
    temperature=1.0,
    top_p=0.99,
    stop=stop_tokens,
)

outputs = llm.generate(pre_query_template, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    for idx, generated_output in enumerate(output.outputs):
        generated_text = generated_output.text
        print(f"Sample {idx + 1}:")
        print(f"Prompt: {prompt!r}")
        print(f"Generated text: {generated_text!r}")
        print("-" * 50)

Citation

@article{ding2024unleashing,
    title={Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch}, 
    author={Ding, Yuyang and Shi, Xinyu and Liang, Xiaobo and Li, Juntao and Zhu, Qiaoming and Zhang, Min},
    journal={https://arxiv.org/abs/2410.18693}, 
    year={2024}
}