Instructions to use osunlp/QUEST-30B-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osunlp/QUEST-30B-RL with Transformers:

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

pipe = pipeline("text-generation", model="osunlp/QUEST-30B-RL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("osunlp/QUEST-30B-RL")
model = AutoModelForCausalLM.from_pretrained("osunlp/QUEST-30B-RL")
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 osunlp/QUEST-30B-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osunlp/QUEST-30B-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-30B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/osunlp/QUEST-30B-RL

SGLang

How to use osunlp/QUEST-30B-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-30B-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-30B-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-30B-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-30B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use osunlp/QUEST-30B-RL with Docker Model Runner:
```
docker model run hf.co/osunlp/QUEST-30B-RL
```

hsaest commited on about 8 hours ago

Commit

c803831

verified ·

1 Parent(s): bf9d923

Update QUEST family links and citation

Browse files

Files changed (1) hide show

README.md +28 -0

README.md CHANGED Viewed

@@ -24,6 +24,20 @@ QUEST **30B** full model after **mid-training → SFT → RL** (Qwen3-30B-A3B ba
 | GAIA | avg@3 | 69.0 |
 | LiveResearchBench | avg@3 | 74.1 |
 ## Quick start
 ```python
@@ -38,6 +52,20 @@ 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 | 69.0 |
 | LiveResearchBench | avg@3 | 74.1 |
+## 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) |
+| 30B checkpoints | [RL](https://huggingface.co/osunlp/QUEST-30B-RL), [MT+SFT](https://huggingface.co/osunlp/QUEST-30B-MT-Plus-SFT), [SFT](https://huggingface.co/osunlp/QUEST-30B-SFT) |
+| Smaller checkpoints | [9B](https://huggingface.co/osunlp/QUEST-9B), [4B](https://huggingface.co/osunlp/QUEST-4B), [2B](https://huggingface.co/osunlp/QUEST-2B) |
+| Training data | [RL data](https://huggingface.co/datasets/osunlp/QUEST-RL-Data), [SFT objective data](https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Objective), [SFT open-ended data](https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Open-ended) |
+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
 ```python
 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**.