Instructions to use Qwen/Qwen-72B-Chat-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen-72B-Chat-Int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen-72B-Chat-Int4", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-72B-Chat-Int4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Qwen/Qwen-72B-Chat-Int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen-72B-Chat-Int4" # 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-72B-Chat-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Qwen/Qwen-72B-Chat-Int4
- SGLang
How to use Qwen/Qwen-72B-Chat-Int4 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-72B-Chat-Int4" \ --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-72B-Chat-Int4", "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-72B-Chat-Int4" \ --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-72B-Chat-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Qwen/Qwen-72B-Chat-Int4 with Docker Model Runner:
docker model run hf.co/Qwen/Qwen-72B-Chat-Int4
update modeling_qwen.py
Browse files- modeling_qwen.py +1 -1
modeling_qwen.py
CHANGED
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@@ -520,7 +520,7 @@ class QWenAttention(nn.Module):
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if not self.use_cache_quantization and SUPPORT_TORCH2:
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if attention_mask is not None:
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-
attention_mask = attention_mask.expand(-1, -1,
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if causal_mask is not None:
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attention_mask = attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min)
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else:
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if not self.use_cache_quantization and SUPPORT_TORCH2:
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if attention_mask is not None:
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+
attention_mask = attention_mask.expand(-1, -1, query.size(2), -1)
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if causal_mask is not None:
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attention_mask = attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min)
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else:
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