Instructions to use BAAI/Emu3-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/Emu3-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BAAI/Emu3-Chat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("BAAI/Emu3-Chat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BAAI/Emu3-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BAAI/Emu3-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/Emu3-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BAAI/Emu3-Chat
- SGLang
How to use BAAI/Emu3-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/Emu3-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/Emu3-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/Emu3-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/Emu3-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BAAI/Emu3-Chat with Docker Model Runner:
docker model run hf.co/BAAI/Emu3-Chat
| # coding=utf-8 | |
| # Copyright 2024 The Emu team, BAAI and The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ Logits Processor Helper class for Emu3. """ | |
| import torch | |
| class Emu3PrefixConstrainedLogitsHelper: | |
| def __init__( | |
| self, | |
| height, | |
| width, | |
| img_token, | |
| eoi_token, | |
| eos_token, | |
| eol_token, | |
| eof_token, | |
| pad_token, | |
| visual_tokens, | |
| ): | |
| self.height = height | |
| self.width = width | |
| self.img_token = img_token | |
| self.eoi_token = eoi_token | |
| self.eos_token = eos_token | |
| self.eol_token = eol_token | |
| self.eof_token = eof_token | |
| self.pad_token = pad_token | |
| self.visual_tokens = visual_tokens | |
| self.offset_cache = {} | |
| def __call__(self, batch_id, input_ids): | |
| if batch_id not in self.offset_cache: | |
| position = torch.nonzero(input_ids == self.img_token, as_tuple=True)[0][0] | |
| self.offset_cache[batch_id] = position | |
| offset = input_ids.shape[0] - self.offset_cache[batch_id] | |
| if offset % (self.width + 1) == 0: | |
| return (self.eol_token, ) | |
| elif offset == (self.width + 1) * self.height + 1: | |
| return (self.eof_token, ) | |
| elif offset == (self.width + 1) * self.height + 2: | |
| return (self.eoi_token, ) | |
| elif offset == (self.width + 1) * self.height + 3: | |
| return (self.eos_token, ) | |
| elif offset > (self.width + 1) * self.height + 3: | |
| return (self.pad_token, ) | |
| else: | |
| return self.visual_tokens | |