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 5 hours ago

Commit

5f32273

verified ·

1 Parent(s): ccbc79a

Update QUEST family links and citation

Browse files

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README.md CHANGED Viewed

@@ -31,7 +31,7 @@ QUEST **30B** full model after **mid-training → SFT → RL** (Qwen3-30B-A3B ba
 | 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), [Mid-training data](https://huggingface.co/datasets/osunlp/QUEST-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

 | 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), [Mid-training data](https://huggingface.co/datasets/osunlp/QUEST-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