Instructions to use stepfun-ai/step3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stepfun-ai/step3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="stepfun-ai/step3", trust_remote_code=True)
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 AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("stepfun-ai/step3", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stepfun-ai/step3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stepfun-ai/step3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stepfun-ai/step3",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/stepfun-ai/step3

SGLang

How to use stepfun-ai/step3 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 "stepfun-ai/step3" \
    --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": "stepfun-ai/step3",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "stepfun-ai/step3" \
        --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": "stepfun-ai/step3",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use stepfun-ai/step3 with Docker Model Runner:
```
docker model run hf.co/stepfun-ai/step3
```

step3 / config.json

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	{
	"architectures": [
	"Step3VLForConditionalGeneration"
	],
	"auto_map": {
	"AutoConfig": "configuration_step3.Step3VLConfig",
	"AutoModelForCausalLM": "modeling_step3.Step3vForConditionalGeneration"
	},
	"model_type": "step3_vl",
	"im_end_token": "<im_end>",
	"im_patch_token": "<im_patch>",
	"im_start_token": "<im_start>",
	"image_token_len": 169,
	"patch_token_len": 81,
	"understand_projector_stride": 2,
	"projector_bias": false,
	"image_token_id": 128001,
	"bos_token_id": 0,
	"eos_token_id": 128805,
	"text_config": {
	"architectures": [
	"Step3TextForCausalLM"
	],
	"model_type": "step3_text",
	"hidden_size": 7168,
	"intermediate_size": 18432,
	"num_hidden_layers": 61,
	"max_seq_len": 65536,
	"max_position_embedding": 65536,
	"vocab_size": 128815,
	"torch_dtype": "bfloat16",
	"moe_layers_enum": "4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59",
	"num_attention_heads": 64,
	"num_attention_groups": 1,
	"head_dim": 256,
	"share_q_dim": 2048,
	"moe_num_experts": 48,
	"moe_top_k": 3,
	"moe_intermediate_size": 5120,
	"share_expert_dim": 5120,
	"norm_expert_weight": false,
	"rope_theta": 500000
	},
	"vision_config": {
	"hidden_size": 1792,
	"output_hidden_size": 4096,
	"image_size": 728,
	"intermediate_size": 15360,
	"num_attention_heads": 16,
	"num_hidden_layers": 63,
	"patch_size": 14
	}
	}