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

Libraries

How to use osunlp/QUEST-35B-MT-Plus-SFT with Transformers:

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

pipe = pipeline("text-generation", model="osunlp/QUEST-35B-MT-Plus-SFT")
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-MT-Plus-SFT")
model = AutoModelForImageTextToText.from_pretrained("osunlp/QUEST-35B-MT-Plus-SFT")
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-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-35B-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-35B-MT-Plus-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/osunlp/QUEST-35B-MT-Plus-SFT

SGLang

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

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

QUEST-35B-MT+SFT

QUEST 35B-class MoE checkpoint after mid-training + SFT (Qwen3.5-35B-A3B base). Same training stage as QUEST-35B-MT; results correspond to the +MT column in the training-stage analysis.

Benchmark results

Benchmark	Metric	Score
BrowseComp	avg@3	45.5
Mind2Web 2	avg@3	29.9
HLE	avg@3	39.74
DeepResearch Bench	avg@3	39.72
BrowseComp-Plus	avg@3	58.6
WideSearch	Item F1 avg@4	62.5
GAIA	avg@3	83.17
LiveResearchBench	avg@3	65.47

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "osunlp/QUEST-35B-MT-Plus-SFT"
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.

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Safetensors

Model size

35B params

Tensor type

BF16

Collection including osunlp/QUEST-35B-MT-Plus-SFT

QUEST

Collection

13 items • Updated about 1 hour ago