Image-Text-to-Text
Transformers
Safetensors
English
Chinese
ernie4_5_moe_vl
ERNIE4.5
conversational
custom_code
Instructions to use baidu/ERNIE-4.5-VL-28B-A3B-PT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baidu/ERNIE-4.5-VL-28B-A3B-PT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="baidu/ERNIE-4.5-VL-28B-A3B-PT", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("baidu/ERNIE-4.5-VL-28B-A3B-PT", trust_remote_code=True, dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use baidu/ERNIE-4.5-VL-28B-A3B-PT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "baidu/ERNIE-4.5-VL-28B-A3B-PT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baidu/ERNIE-4.5-VL-28B-A3B-PT", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/baidu/ERNIE-4.5-VL-28B-A3B-PT
- SGLang
How to use baidu/ERNIE-4.5-VL-28B-A3B-PT 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 "baidu/ERNIE-4.5-VL-28B-A3B-PT" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baidu/ERNIE-4.5-VL-28B-A3B-PT", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "baidu/ERNIE-4.5-VL-28B-A3B-PT" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baidu/ERNIE-4.5-VL-28B-A3B-PT", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use baidu/ERNIE-4.5-VL-28B-A3B-PT with Docker Model Runner:
docker model run hf.co/baidu/ERNIE-4.5-VL-28B-A3B-PT
| { | |
| "chat_template": "\n{%- set image_count = namespace(value=0) -%}\n{%- set video_count = namespace(value=0) -%}\n{{- '<|begin_of_sentence|>' }}\n{%- for message in messages -%}\n {%- if message.role in ['system', 'user'] -%}\n {%- if message.role == 'user' -%}\n {{- 'User: ' -}}\n {%- endif -%}\n {%- if message.content is string -%}\n {{- message.content -}}\n {%- else -%}\n {%- for content_item in message.content -%}\n {%- if content_item.type == 'text' -%}\n {{- content_item.text -}}\n {%- elif content_item.type in ['image_url', 'image'] -%}\n {%- set image_count.value = image_count.value + 1 -%}\n Picture {{ image_count.value }}:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>\n {%- elif content_item.type in ['video_url', 'video'] -%}\n {%- set video_count.value = video_count.value + 1 -%}\n Video {{ video_count.value }}:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n {%- if message.role == 'system' -%}\n {{- '\n' -}}\n {%- endif -%}\n {%- elif message.role == 'assistant' -%}\n {%- macro extract_text_content(content_field) -%}\n {%- if content_field is string -%}\n {{- content_field -}}\n {%- elif content_field is iterable and content_field is not string -%}\n {%- set ns = namespace(text_parts=[]) -%}\n {%- set text_parts = [] -%}\n {%- for item in content_field -%}\n {%- if item.type == 'text' -%}\n {%- set ns.text_parts = ns.text_parts + [item.text] -%}\n {%- endif -%}\n {%- endfor -%}\n {{- ns.text_parts | join('') -}}\n {%- else -%}\n {{- '' -}}\n {%- endif -%}\n {%- endmacro -%}\n {%- set reasoning_content = extract_text_content(message.reasoning_content) -%}\n {%- set content = extract_text_content(message.content) -%}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\n') %}\n {%- endif %}\n {%- if reasoning_content %}\n {{- '\n' + 'Assistant: ' + '<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}\n {%- else %}\n {{- '\n' + 'Assistant: ' + content }}\n {%- endif %}\n {{- '<|end_of_sentence|>' }}\n {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt is not defined or add_generation_prompt is true %}\n {{- '\nAssistant: ' -}}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\n\n</think>\n\n' }}\n {%- endif %}\n {%- if enable_thinking is not defined or enable_thinking is true %}\n {{- '<think>' }}\n {%- endif %}\n{%- endif %}\n" | |
| } |