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

Commit

45eebdc

verified ·

1 Parent(s): e9b865b

Update config.json

Browse files

Files changed (1) hide show

config.json +10 -6

config.json CHANGED Viewed

@@ -1,8 +1,12 @@
 {
   "architectures": [
-    "Step4ForCausalLM"
   ],
-  "model_type": "step4",
   "hidden_size": 4096,
   "intermediate_size": 11264,
   "num_hidden_layers": 45,
@@ -10,9 +14,8 @@
   "max_position_embedding": 131072,
   "vocab_size": 128896,
   "torch_dtype": "bfloat16",
-  "use_qk_norm": false,
   "moe_layers_enum": "3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44",
-  "use_mfa": false,
   "num_attention_heads": 64,
   "num_attention_groups": 8,
   "num_nextn_predict_layers": 1,
@@ -285,5 +288,6 @@
     0.0,
     16,
     0.0
-  ]
-}

 {
   "architectures": [
+    "Step3p5ForCausalLM"
   ],
+  "model_type": "step3p5",
+  "auto_map": {
+    "AutoConfig": "configuration_step3p5.Step3p5Config",
+    "AutoModelForCausalLM": "modeling_step3p5.Step3p5ForCausalLM"
+  },
   "hidden_size": 4096,
   "intermediate_size": 11264,
   "num_hidden_layers": 45,
   "max_position_embedding": 131072,
   "vocab_size": 128896,
   "torch_dtype": "bfloat16",
+  "use_qk_norm": true,
   "moe_layers_enum": "3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44",
   "num_attention_heads": 64,
   "num_attention_groups": 8,
   "num_nextn_predict_layers": 1,
     0.0,
     16,
     0.0
+  ],
+  "zero_centered": true
+}