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
```

WinstonDeng commited on Feb 1

Commit

d67cbb1

verified ·

1 Parent(s): 33f52d4

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@@ -13,9 +13,9 @@ base_model:
   <h1 style="margin: 0; border-bottom: none;">Step-3.5-Flash</h1>
 </div>
-[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20HF-StepFun/STEP3p5-preview)](https://huggingface.co/stepfun-ai/step3p5_preview/tree/main)
 [![ModelScope](https://img.shields.io/badge/ModelScope-StepFun/STEP3p5-preview)](https://huggingface.co/stepfun-ai/step3p5_preview/tree/main)
-[![Paper](https://img.shields.io/badge/Paper-Arxiv-red)](https://huggingface.co/stepfun-ai/step3p5_preview/tree/main)
 [![License](https://img.shields.io/badge/License-Apache%202.0-green)]()
 </div>
@@ -51,7 +51,7 @@ Performance of Step 3.5 Flash measured across **Reasoning**, **Coding**, and **A
 | Est. decoding cost (@ 128K context, Hopper GPU**) | **1.0x** (100 tok/s, MTP-3, EP8) | 6.0x (33 tok/s, MTP-1, EP32) | 18.9x (33 tok/s, no MTP, EP32) | 18.9x (100 tok/s, MTP-3, EP8) | 3.9x (100 tok/s, MTP-3, EP8) | 1.2x (100 tok/s, MTP-3, EP8) |
 | **Agency** | | | | | | |
 | τ²-Bench | **88.2** | 80.3 | 74.3* / — | 87.4 | 80.2* | 80.3 |
-| BrowseComp | 50.7 | 51.4 | 41.5* / **60.6** | 52.0 | 47.4 | 45.4 |
 | BrowseComp (w/ Context Manager) | 69.0 | 67.6 | 60.2 / **74.9** | 67.5 | 62.0 | 58.3 |
 | BrowseComp-ZH | **66.9** | 65.0 | 62.3 / 62.3* | 66.6 | 47.8* | 51.2* |
 | BrowseComp-ZH (w/ Context Manager) | **73.7** | — | — / — | — | — | — |

   <h1 style="margin: 0; border-bottom: none;">Step-3.5-Flash</h1>
 </div>
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20HF-StepFun/STEP3p5-preview)](https://huggingface.co/stepfun-ai/Step-3.5-Flash-FP8)
 [![ModelScope](https://img.shields.io/badge/ModelScope-StepFun/STEP3p5-preview)](https://huggingface.co/stepfun-ai/step3p5_preview/tree/main)
+[![Paper](https://img.shields.io/badge/Paper-Arxiv-red)](https://huggingface.co/stepfun-ai/Step-3.5-Flash-FP8)
 [![License](https://img.shields.io/badge/License-Apache%202.0-green)]()
 </div>
 | Est. decoding cost (@ 128K context, Hopper GPU**) | **1.0x** (100 tok/s, MTP-3, EP8) | 6.0x (33 tok/s, MTP-1, EP32) | 18.9x (33 tok/s, no MTP, EP32) | 18.9x (100 tok/s, MTP-3, EP8) | 3.9x (100 tok/s, MTP-3, EP8) | 1.2x (100 tok/s, MTP-3, EP8) |
 | **Agency** | | | | | | |
 | τ²-Bench | **88.2** | 80.3 | 74.3* / — | 87.4 | 80.2* | 80.3 |
+| BrowseComp | 51.6 | 51.4 | 41.5* / **60.6** | 52.0 | 47.4 | 45.4 |
 | BrowseComp (w/ Context Manager) | 69.0 | 67.6 | 60.2 / **74.9** | 67.5 | 62.0 | 58.3 |
 | BrowseComp-ZH | **66.9** | 65.0 | 62.3 / 62.3* | 66.6 | 47.8* | 51.2* |
 | BrowseComp-ZH (w/ Context Manager) | **73.7** | — | — / — | — | — | — |