Step3 VL 10B

Step3 VL 10B from stepfun-ai. Source-based hardware guidance from its published configuration.

EstimatedRepository opened Jan 13, 2026Source checked 9/25/2026Version: 5026053b

LOCALRENTED GPUOPEN WEIGHTS

At a glance

Parameters: 10.17B
Architecture: step_robotics
License: apache-2.0
Software: vLLM, Transformers

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Ways to run it

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

vllm serve --model stepfun-ai/Step3-VL-10B -tp 1 --reasoning-parser deepseek_r1 --enable-auto-tool-choice --tool-call-parser hermes --trust-remote-code

Official instructions

Transformers · See official guide

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

from transformers import AutoProcessor, AutoModelForCausalLM


key_mapping = {
    "^vision_model": "model.vision_model",
    r"^model(?!\.(language_model|vision_model))": "model.language_model",
    "vit_large_projector": "model.vit_large_projector",
}

model_path = "stepfun-ai/Step3-VL-10B"

processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
            {"type": "text", "text": "What's in this picture?"}
        ]
    },
]

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype="auto",
    key_mapping=key_mapping).eval()


inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt"
).to(model.device)


generate_ids = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
decoded = processor.decode(generate_ids[0, inputs["input_ids"].shape[-1] :], skip_special_tokens=True)

print(decoded)

Official instructions

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