Instructions to use osunlp/QUEST-35B-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osunlp/QUEST-35B-RL with Transformers:

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

pipe = pipeline("text-generation", model="osunlp/QUEST-35B-RL")
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-35B-RL")
model = AutoModelForImageTextToText.from_pretrained("osunlp/QUEST-35B-RL")
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-35B-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osunlp/QUEST-35B-RL"
# 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-35B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/osunlp/QUEST-35B-RL

SGLang

How to use osunlp/QUEST-35B-RL 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-35B-RL" \
    --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-35B-RL",
		"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-35B-RL" \
        --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-35B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use osunlp/QUEST-35B-RL with Docker Model Runner:
```
docker model run hf.co/osunlp/QUEST-35B-RL
```

hsaest commited on about 10 hours ago

Commit

48b2179

verified ·

1 Parent(s): 25518f0

Update QUEST family links and citation

Browse files

Files changed (1) hide show

README.md +15 -0

README.md CHANGED Viewed

@@ -24,6 +24,7 @@ QUEST **35B-class MoE** full model after **mid-training → SFT → RL** (Qwen3.
 | GAIA | avg@3 | 80.8 |
 | LiveResearchBench | avg@3 | 68.2 |
 ## QUEST Family
 | Type | Resources |
 | --- | --- |
 | 35B checkpoints | [RL](https://huggingface.co/osunlp/QUEST-35B-RL), [MT+SFT](https://huggingface.co/osunlp/QUEST-35B-MT-Plus-SFT), [MT](https://huggingface.co/osunlp/QUEST-35B-MT), [SFT](https://huggingface.co/osunlp/QUEST-35B-SFT) |
@@ -50,6 +51,20 @@ model = AutoModelForCausalLM.from_pretrained(
 Apply the model's chat template with `tokenizer.apply_chat_template(...)` before passing prompts.
 ## License
 Released under the **Apache License 2.0**.

 | GAIA | avg@3 | 80.8 |
 | LiveResearchBench | avg@3 | 68.2 |
 ## QUEST Family
 | Type | Resources |
 | --- | --- |
 | 35B checkpoints | [RL](https://huggingface.co/osunlp/QUEST-35B-RL), [MT+SFT](https://huggingface.co/osunlp/QUEST-35B-MT-Plus-SFT), [MT](https://huggingface.co/osunlp/QUEST-35B-MT), [SFT](https://huggingface.co/osunlp/QUEST-35B-SFT) |
 Apply the model's chat template with `tokenizer.apply_chat_template(...)` before passing prompts.
+## Citation
+If our paper or related resources prove valuable to your research, we kindly ask
+for a citation.
+```bibtex
+@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}
+}
+```
 ## License
 Released under the **Apache License 2.0**.