Instructions to use FastFlowLM/Qwen3.5-4B-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FastFlowLM/Qwen3.5-4B-NPU2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="FastFlowLM/Qwen3.5-4B-NPU2") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FastFlowLM/Qwen3.5-4B-NPU2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FastFlowLM/Qwen3.5-4B-NPU2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FastFlowLM/Qwen3.5-4B-NPU2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FastFlowLM/Qwen3.5-4B-NPU2", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/FastFlowLM/Qwen3.5-4B-NPU2
- SGLang
How to use FastFlowLM/Qwen3.5-4B-NPU2 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 "FastFlowLM/Qwen3.5-4B-NPU2" \ --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": "FastFlowLM/Qwen3.5-4B-NPU2", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "FastFlowLM/Qwen3.5-4B-NPU2" \ --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": "FastFlowLM/Qwen3.5-4B-NPU2", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use FastFlowLM/Qwen3.5-4B-NPU2 with Docker Model Runner:
docker model run hf.co/FastFlowLM/Qwen3.5-4B-NPU2
feat: update config
Browse files- chat_template.jinja +1 -5
- config.json +1 -1
chat_template.jinja
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{%- endif %}
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{%- endif %}
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{%- set reasoning_content = reasoning_content|trim %}
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{
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- endif %}
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{%- endif %}
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{%- set reasoning_content = reasoning_content|trim %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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config.json
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"VISION_MM_TILE_K": 512,
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"VISION_MM_TILE_N": 64
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}
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}
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"VISION_MM_TILE_K": 512,
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"VISION_MM_TILE_N": 64
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}
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