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 ·
88d6dd3
1
Parent(s): 0b3eb69
Update visual.py
Browse files
visual.py
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@@ -125,7 +125,7 @@ class Resampler(nn.Module):
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self.ln_q = norm_layer(embed_dim)
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self.ln_kv = norm_layer(embed_dim)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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@@ -189,7 +189,7 @@ class VisualAttention(nn.Module):
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# query/key/value: [sq, b, h]
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sq, b, _ = query.size()
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assert query
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sk = sq
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mixed_x_layer = self.in_proj(query)
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self.ln_q = norm_layer(embed_dim)
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self.ln_kv = norm_layer(embed_dim)
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# self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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# query/key/value: [sq, b, h]
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sq, b, _ = query.size()
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assert torch.allclose(query, key), 'Only Support Self-Attention Currently'
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sk = sq
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mixed_x_layer = self.in_proj(query)
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