Instructions to use osunlp/QUEST-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osunlp/QUEST-4B with Transformers:

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

pipe = pipeline("text-generation", model="osunlp/QUEST-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("osunlp/QUEST-4B")
model = AutoModelForImageTextToText.from_pretrained("osunlp/QUEST-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use osunlp/QUEST-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osunlp/QUEST-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": "osunlp/QUEST-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/osunlp/QUEST-4B

SGLang

How to use osunlp/QUEST-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 "osunlp/QUEST-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": "osunlp/QUEST-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 "osunlp/QUEST-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": "osunlp/QUEST-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use osunlp/QUEST-4B with Docker Model Runner:
```
docker model run hf.co/osunlp/QUEST-4B
```

QUEST-4B / README.md

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Update QUEST family links and citation

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metadata

library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - quest
  - text-generation

QUEST-4B

QUEST 4B SFT model — general-purpose deep research agent (Qwen3.5 family, dense).

Benchmark results

Benchmark	Metric	Score
BrowseComp	avg@3	40.0
Mind2Web 2	avg@3	24.3
HLE	avg@3	36.2
DeepResearch Bench	avg@3	22.0
BrowseComp-Plus	avg@3	52.1
WideSearch	Item F1 avg@4	55.0
GAIA	avg@3	77.7
LiveResearchBench	avg@3	62.1

QUEST Family

Type	Resources
35B checkpoints	RL, MT+SFT, MT, SFT
30B checkpoints	RL, MT+SFT, SFT
Smaller checkpoints	9B, 4B, 2B
Training data	RL data, SFT objective data, SFT open-ended data, Mid-training data

Model selection note: if you only need to evaluate objective tasks and do not need open-ended task evaluation, we recommend the MT+SFT checkpoints because they perform better on reasoning-heavy objective benchmarks. For a more comprehensive evaluation across both objective and open-ended tasks, we recommend the RL checkpoints.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "osunlp/QUEST-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", torch_dtype="auto",
)

Apply the model's chat template with tokenizer.apply_chat_template(...) before passing prompts.

License

Released under the Apache License 2.0.

Citation

If our paper or related resources prove valuable to your research, we kindly ask for a citation.

@misc{xie2026quest,
  title={QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks},
  author={Xie, Jian and Lin, Tianhe and Wang, Zilu and Ning, Yuting and Yao, Yuekun and Xue, Tianci and Zhang, Zhehao and Li, Zhongyang and Zhang, Kai and Wu, Yufan and Chen, Shijie and Gou, Boyu and Han, Mingzhe and Wang, Yifei and Lee, Vint and Wei, Xinpeng and Wang, Xiangjun and Su, Yu and Sun, Huan},
  journal={arXiv preprint arXiv:2605.24218},
  year={2026}
}