Image-Text-to-Text
Transformers
Safetensors
multilingual
minicpmv
feature-extraction
minicpm-v
vision
ocr
multi-image
video
custom_code
conversational
4-bit precision
bitsandbytes
Instructions to use openbmb/MiniCPM-V-2_6-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-V-2_6-int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="openbmb/MiniCPM-V-2_6-int4", 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-V-2_6-int4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use openbmb/MiniCPM-V-2_6-int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/MiniCPM-V-2_6-int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM-V-2_6-int4", "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/openbmb/MiniCPM-V-2_6-int4
- SGLang
How to use openbmb/MiniCPM-V-2_6-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 "openbmb/MiniCPM-V-2_6-int4" \ --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": "openbmb/MiniCPM-V-2_6-int4", "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 "openbmb/MiniCPM-V-2_6-int4" \ --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": "openbmb/MiniCPM-V-2_6-int4", "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 openbmb/MiniCPM-V-2_6-int4 with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM-V-2_6-int4
Download test.py from openbmb/MiniCPM-V-2_6-int4: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
-
https://huggingface.co/openbmb/MiniCPM-V-2_6-int4/resolve/main/test.py
- Command line
-
hf download hf://openbmb/MiniCPM-V-2_6-int4/test.py
-
curl -L -o test.py https://huggingface.co/openbmb/MiniCPM-V-2_6-int4/resolve/main/test.py
1.16 kB
| # test.py | |
| import torch | |
| from PIL import Image | |
| from transformers import AutoModel, AutoTokenizer, BitssAndBytesConfig | |
| model = AutoModel.from_pretrained('./', trust_remote_code=True, torch_dtype=torch.bfloat16, local_files_only=True) | |
| # model = model.to(device='cuda') | |
| tokenizer = AutoTokenizer.from_pretrained('./', trust_remote_code=True) | |
| model.eval() | |
| image = Image.open('/data1/caitianchi/code/MiniCPM-V-2_5/20240614-205027.jpeg').convert('RGB') | |
| question = '描述这张图?' | |
| msgs = [{'role': 'user', 'content': question}] | |
| res = model.chat( | |
| image=image, | |
| msgs=msgs, | |
| tokenizer=tokenizer, | |
| sampling=True, # if sampling=False, beam_search will be used by default | |
| temperature=0.7, | |
| # system_prompt='' # pass system_prompt if needed | |
| ) | |
| print(res) | |
| # ## if you want to use streaming, please make sure sampling=True and stream=True | |
| # ## the model.chat will return a generator | |
| # res = model.chat( | |
| # image=image, | |
| # msgs=msgs, | |
| # tokenizer=tokenizer, | |
| # sampling=True, | |
| # temperature=0.7, | |
| # stream=True | |
| # ) | |
| # generated_text = "" | |
| # for new_text in res: | |
| # generated_text += new_text | |
| # print(new_text, flush=True, end='') | |