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

Libraries

How to use osunlp/QUEST-30B-MT-Plus-SFT with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/osunlp/QUEST-30B-MT-Plus-SFT

SGLang

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

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

hsaest commited on 13 days ago

Commit

fef87d1

verified ·

1 Parent(s): a4daf55

README: soften benchmark note to 'not evaluated'

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README.md CHANGED Viewed

@@ -11,7 +11,7 @@ tags:
 QUEST **30B** checkpoint after **mid-training + SFT** (Qwen3-30B-A3B base, dense). This is an intermediate artifact in the QUEST 30B training pipeline (MT → SFT → RL).
-Benchmark results for this intermediate checkpoint are not separately reported on the project page. For the full pipeline results, see [QUEST-30B-RL](https://huggingface.co/osunlp/QUEST-30B-RL).
 ## Training stage

 QUEST **30B** checkpoint after **mid-training + SFT** (Qwen3-30B-A3B base, dense). This is an intermediate artifact in the QUEST 30B training pipeline (MT → SFT → RL).
+We did not run benchmark evaluations on this checkpoint. For full pipeline results, see [QUEST-30B-RL](https://huggingface.co/osunlp/QUEST-30B-RL).
 ## Training stage