Instructions to use stepfun-ai/Step-3.7-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stepfun-ai/Step-3.7-Flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="stepfun-ai/Step-3.7-Flash", 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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("stepfun-ai/Step-3.7-Flash", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use stepfun-ai/Step-3.7-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stepfun-ai/Step-3.7-Flash"
# 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/Step-3.7-Flash",
		"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/Step-3.7-Flash

SGLang

How to use stepfun-ai/Step-3.7-Flash 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/Step-3.7-Flash" \
    --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/Step-3.7-Flash",
		"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/Step-3.7-Flash" \
        --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/Step-3.7-Flash",
		"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/Step-3.7-Flash with Docker Model Runner:
```
docker model run hf.co/stepfun-ai/Step-3.7-Flash
```

hengm3467 commited on about 19 hours ago

Commit

63f1ea4

1 Parent(s): 5c5f97c

update Ecosystem bullet

Browse files

- broaden scope: "model development" -> "model development & customization"
- NVIDIA Megatron -> NVIDIA Nemo ecosystem
- add AutoModel alongside Megatron Core / Megatron Bridge
- mention NVIDIA NIM inference microservice

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -51,7 +51,7 @@ Step 3.7 Flash is built for live engineering tasks and secured a definitive seco
 ## 4. Availability, Deployment, and Ecosystem
 - Availability: Step 3.7 Flash is available on the StepFun Open Platform — [platform.stepfun.ai](https://platform.stepfun.ai) (Global) and [platform.stepfun.com](https://platform.stepfun.com) (China), OpenRouter, and NVIDIA NIM. StepFun is also partnering with DeepInfra, Fireworks AI, and Modal to expand availability soon.
 - Deployment: Step 3.7 Flash supports flexible deployment across cloud, data center, and local environments. For large-scale production and enterprise use cases, Step 3.7 Flash can be deployed on modern data center infrastructure. For local and workstation scenarios, it can also run on high-memory devices such as NVIDIA DGX Station, AMD Ryzen AI Max+ 395-based systems, and Mac Studio / Macbook Pro devices with at least 128GB unified memory.
-- Ecosystem: Step 3.7 Flash is supported across popular open-source infrastructure for both inference and model development. For inference and serving, developers can use vLLM, SGLang, Hugging Face Transformers, and llama.cpp. For model development workflows, StepFun model support has landed in the NVIDIA Megatron ecosystem, including Megatron Core and Megatron Bridge.
 ## 5. Examples

 ## 4. Availability, Deployment, and Ecosystem
 - Availability: Step 3.7 Flash is available on the StepFun Open Platform — [platform.stepfun.ai](https://platform.stepfun.ai) (Global) and [platform.stepfun.com](https://platform.stepfun.com) (China), OpenRouter, and NVIDIA NIM. StepFun is also partnering with DeepInfra, Fireworks AI, and Modal to expand availability soon.
 - Deployment: Step 3.7 Flash supports flexible deployment across cloud, data center, and local environments. For large-scale production and enterprise use cases, Step 3.7 Flash can be deployed on modern data center infrastructure. For local and workstation scenarios, it can also run on high-memory devices such as NVIDIA DGX Station, AMD Ryzen AI Max+ 395-based systems, and Mac Studio / Macbook Pro devices with at least 128GB unified memory.
+- Ecosystem: Step 3.7 Flash is supported across popular open-source infrastructure for both inference and model development. For inference and serving, developers can use vLLM, SGLang, Hugging Face Transformers, and llama.cpp. For model development & customization workflows, StepFun model support has landed in the NVIDIA Nemo ecosystem, including AutoModel, Megatron Core and Megatron Bridge. Step 3.7 Flash is also available as an NVIDIA NIM inference microservice for on-prem, cloud, or hybrid deployment.
 ## 5. Examples