Instructions to use BAAI/Emu2-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/Emu2-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BAAI/Emu2-Chat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("BAAI/Emu2-Chat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BAAI/Emu2-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BAAI/Emu2-Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BAAI/Emu2-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BAAI/Emu2-Chat
- SGLang
How to use BAAI/Emu2-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 "BAAI/Emu2-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": "BAAI/Emu2-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 "BAAI/Emu2-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": "BAAI/Emu2-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BAAI/Emu2-Chat with Docker Model Runner:
docker model run hf.co/BAAI/Emu2-Chat
Commit ·
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Parent(s): 94d0495
Update README.md
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README.md
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained(
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True).to('cuda').eval()
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained(
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True).to('cuda').eval()
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch
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tokenizer = AutoTokenizer.from_pretrained("
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with init_empty_weights():
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model = AutoModelForCausalLM.from_pretrained(
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True)
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch
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tokenizer = AutoTokenizer.from_pretrained("
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with init_empty_weights():
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model = AutoModelForCausalLM.from_pretrained(
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True)
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained(
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load_in_4bit=True,
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trust_remote_code=True,
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bnb_4bit_compute_dtype=torch.float16).eval()
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("BAAI/Emu2-Chat")
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model = AutoModelForCausalLM.from_pretrained(
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"BAAI/Emu2-Chat",
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True).to('cuda').eval()
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("BAAI/Emu2-Chat")
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model = AutoModelForCausalLM.from_pretrained(
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"BAAI/Emu2-Chat",
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True).to('cuda').eval()
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch
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tokenizer = AutoTokenizer.from_pretrained("BAAI/Emu2-Chat")
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with init_empty_weights():
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model = AutoModelForCausalLM.from_pretrained(
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"BAAI/Emu2-Chat",
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True)
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch
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tokenizer = AutoTokenizer.from_pretrained("BAAI/Emu2-Chat")
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with init_empty_weights():
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model = AutoModelForCausalLM.from_pretrained(
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"BAAI/Emu2-Chat",
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True)
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("BAAI/Emu2-Chat")
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model = AutoModelForCausalLM.from_pretrained(
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"BAAI/Emu2-Chat",
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load_in_4bit=True,
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trust_remote_code=True,
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bnb_4bit_compute_dtype=torch.float16).eval()
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