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

Libraries

How to use stepfun-ai/Step-3.5-Flash-Base-Midtrain 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-Base-Midtrain", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("stepfun-ai/Step-3.5-Flash-Base-Midtrain", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

Randomize commited on Mar 2

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@@ -72,8 +72,8 @@ Performance of Step 3.5 Flash measured across **Reasoning**, **Coding**, and **A
   | MBPP | 3-shot | 79.2 | 79.4 | 81.0* | 81.6* | 74.6* | 75.6* | 89.0* |
   | HumanEval+ | 0-shot | 73.8 | 72.0 | 70.7 | - | 64.6† | 67.7† | - |
   | MBPP+ | 0-shot | 63.8 | 70.6 | 71.4 | - | 72.2† | 69.8† | - |
-  | MultiPL‑E HumanEval | 0-shot | - | 67.7 | 59.5 | - | 45.9† | 45.7† | 60.5 |
-  | MultiPL‑E MBPP | 0-shot | - | 58.0 | 56.7 | - | 52.5† | 50.6† | 58.8 |
   | Chinese |  |  |  |  |  |  |  |  |
   | C‑EVAL | 5-shot | 87.2 | 89.6 | 87.9 | 86.9 | 90.0† | 91.0† | 92.5 |
   | CMMLU | 5-shot | 86.9 | 88.9 | 87.4 | - | 88.8† | 88.9† | 90.9 |

   | MBPP | 3-shot | 79.2 | 79.4 | 81.0* | 81.6* | 74.6* | 75.6* | 89.0* |
   | HumanEval+ | 0-shot | 73.8 | 72.0 | 70.7 | - | 64.6† | 67.7† | - |
   | MBPP+ | 0-shot | 63.8 | 70.6 | 71.4 | - | 72.2† | 69.8† | - |
+  | MultiPL‑E HumanEval | 0-shot | 63.0 | 67.7 | 59.5 | - | 45.9† | 45.7† | 60.5 |
+  | MultiPL‑E MBPP | 0-shot | 47.9 | 58.0 | 56.7 | - | 52.5† | 50.6† | 58.8 |
   | Chinese |  |  |  |  |  |  |  |  |
   | C‑EVAL | 5-shot | 87.2 | 89.6 | 87.9 | 86.9 | 90.0† | 91.0† | 92.5 |
   | CMMLU | 5-shot | 86.9 | 88.9 | 87.4 | - | 88.8† | 88.9† | 90.9 |