Instructions to use Qwen/Qwen-VL-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen-VL-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen-VL-Chat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Qwen/Qwen-VL-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen-VL-Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen-VL-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Qwen/Qwen-VL-Chat
- SGLang
How to use Qwen/Qwen-VL-Chat 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 "Qwen/Qwen-VL-Chat" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen-VL-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Qwen/Qwen-VL-Chat" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen-VL-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Qwen/Qwen-VL-Chat with Docker Model Runner:
docker model run hf.co/Qwen/Qwen-VL-Chat
shuai bai commited on
Commit ·
a3d284e
1
Parent(s): 1fb8c15
Update modeling_qwen.py
Browse files- modeling_qwen.py +1 -1
modeling_qwen.py
CHANGED
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@@ -556,7 +556,7 @@ class QWenModel(QWenPreTrainedModel):
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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):
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if past_key_values is None and torch.any(input_ids == self.config.visual['image_start_id']):
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bos_pos = torch.where(input_ids == self.config.visual['image_start_id'])
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eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1)
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assert (bos_pos[0] == eos_pos[0]).all()
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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):
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if past_key_values is None and input_ids is not None and torch.any(input_ids == self.config.visual['image_start_id']):
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bos_pos = torch.where(input_ids == self.config.visual['image_start_id'])
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eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1)
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assert (bos_pos[0] == eos_pos[0]).all()
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