Instructions to use stepfun-ai/Step-3.5-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stepfun-ai/Step-3.5-Flash 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", 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", trust_remote_code=True, dtype="auto") - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use stepfun-ai/Step-3.5-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-3.5-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-3.5-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/stepfun-ai/Step-3.5-Flash
- SGLang
How to use stepfun-ai/Step-3.5-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-3.5-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-3.5-Flash", "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" \ --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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use stepfun-ai/Step-3.5-Flash with Docker Model Runner:
docker model run hf.co/stepfun-ai/Step-3.5-Flash
NVFP4
This is great, any chance of a NVFP4 quant for Blackwell GPU’s?
We are discussing this with NVIDIA
Please dont forget to make nvfp4 mlx quant for apple users. Thank you
Awesome
I've been trying every which way I can think of to quantize this to NVFP4 and so far no amount of hacking on llm-compressor or ModelOpt results in a good quant. The 3D MoE layers are not cooperating.
Please dont forget to make nvfp4 mlx quant for apple users. Thank you
You can use the int4 version for now, just in case you did not notice there is an int4 version
I've been trying every which way I can think of to quantize this to NVFP4 and so far no amount of hacking on llm-compressor or ModelOpt results in a good quant. The 3D MoE layers are not cooperating.
Ya, no luck here. I got a model to compile, but the output was garbage.
Please dont forget to make nvfp4 mlx quant for apple users. Thank you
You can use the int4 version for now, just in case you did not notice there is an int4 version
The int4 works great, however it's in gguf format and llama.cpp performance is terrible in prompt-processing and batching request. I would like to use it in VLLM but there is no way to load the gguf into it as it's not yet supported, and from the available quants, not a single one works on vllm or sglang for Ampere archs. There is a int4 gptq model out there but it produces garbage output on my system.