Instructions to use stepfun-ai/Step-3.5-Flash-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stepfun-ai/Step-3.5-Flash-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="stepfun-ai/Step-3.5-Flash-FP8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stepfun-ai/Step-3.5-Flash-FP8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("stepfun-ai/Step-3.5-Flash-FP8", trust_remote_code=True, device_map="auto")
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 Settings

vLLM

How to use stepfun-ai/Step-3.5-Flash-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stepfun-ai/Step-3.5-Flash-FP8"
# 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-3.5-Flash-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/stepfun-ai/Step-3.5-Flash-FP8

SGLang

How to use stepfun-ai/Step-3.5-Flash-FP8 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-3.5-Flash-FP8" \
    --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-3.5-Flash-FP8",
		"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 "stepfun-ai/Step-3.5-Flash-FP8" \
        --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-3.5-Flash-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use stepfun-ai/Step-3.5-Flash-FP8 with Docker Model Runner:
```
docker model run hf.co/stepfun-ai/Step-3.5-Flash-FP8
```

Tingdan commited on Feb 2

Commit

a3d66b5

verified ·

1 Parent(s): 683fb74

Update README.md

Browse files

update sglang deploy cmd

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README.md +3 -7

README.md CHANGED Viewed

@@ -220,12 +220,10 @@ pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git"
 ```
 2. Launch the server.
 ```bash
-  - For bf16 model
-SGLANG_ENABLE_SPEC_V2=1
-python3 -m sglang.launch_server \
-  --model-path <MODEL_PATH_OR_HF_ID> \
   --served-model-name step3p5-flash \
   --tp-size 8 \
   --tool-call-parser step3p5 \
@@ -240,9 +238,7 @@ python3 -m sglang.launch_server \
 ```
   - For fp8 model
 ```bash
-SGLANG_ENABLE_SPEC_V2=1
-python3 -m sglang.launch_server \
-  --model-path <MODEL_PATH_OR_HF_ID> \
   --served-model-name step3p5-flash \
   --tp-size 8 \
   --ep-size 8 \

 ```
 2. Launch the server.
+  - For bf16 model
 ```bash
+sglang serve --model-path <MODEL_PATH_OR_HF_ID> \
   --served-model-name step3p5-flash \
   --tp-size 8 \
   --tool-call-parser step3p5 \
 ```
   - For fp8 model
 ```bash
+sglang serve --model-path <MODEL_PATH_OR_HF_ID> \
   --served-model-name step3p5-flash \
   --tp-size 8 \
   --ep-size 8 \