Image-Text-to-Text
Transformers
Safetensors
English
minicpmo
feature-extraction
minicpm
omni
multimodal
audio
vision
tts
quantized
int4
INT4
w4a16
4-bit precision
compressed-tensors
vllm
text-generation
conversational
ptq
AutoRound (blocked — see KNOWN-FAILURES)
llmcompressor
sglang
88plug
post-training-quantization
vlm
image
custom_code
Instructions to use 88plug/MiniCPM-o-4.5-W4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 88plug/MiniCPM-o-4.5-W4A16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="88plug/MiniCPM-o-4.5-W4A16", 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("88plug/MiniCPM-o-4.5-W4A16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 88plug/MiniCPM-o-4.5-W4A16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "88plug/MiniCPM-o-4.5-W4A16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "88plug/MiniCPM-o-4.5-W4A16", "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/88plug/MiniCPM-o-4.5-W4A16
- SGLang
How to use 88plug/MiniCPM-o-4.5-W4A16 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 "88plug/MiniCPM-o-4.5-W4A16" \ --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": "88plug/MiniCPM-o-4.5-W4A16", "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 "88plug/MiniCPM-o-4.5-W4A16" \ --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": "88plug/MiniCPM-o-4.5-W4A16", "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 88plug/MiniCPM-o-4.5-W4A16 with Docker Model Runner:
docker model run hf.co/88plug/MiniCPM-o-4.5-W4A16
File size: 2,481 Bytes
1311940 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | {
"audio_processor": {
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"AutoProcessor": "processing_minicpmo.MiniCPMOProcessor"
},
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"feature_size": 80,
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"im_end": "</image>",
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"image_processor_type": "MiniCPMVImageProcessor",
"log_floor_db": -10.0,
"max_slice_nums": 9,
"n_fft": 400,
"n_samples": 480000,
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"padding_side": "right",
"padding_value": 0.0,
"patch_size": 14,
"return_attention_mask": false,
"sampling_rate": 16000,
"scale_resolution": 448,
"slice_end": "</slice>",
"slice_mode": true,
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"unk": "<unk>",
"use_image_id": true,
"version": 4.5
},
"auto_map": {
"AutoProcessor": "processing_minicpmo.MiniCPMOProcessor"
},
"image_processor": {
"audio_pool_step": 5,
"auto_map": {
"AutoFeatureExtractor": "processing_minicpmo.MiniCPMAAudioProcessor",
"AutoImageProcessor": "processing_minicpmo.MiniCPMVImageProcessor",
"AutoProcessor": "processing_minicpmo.MiniCPMOProcessor"
},
"im_end": "</image>",
"im_end_token": "</image>",
"im_id_end": "</image_id>",
"im_id_start": "<image_id>",
"im_start": "<image>",
"im_start_token": "<image>",
"image_feature_size": 64,
"image_processor_type": "MiniCPMVImageProcessor",
"max_slice_nums": 9,
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"patch_size": 14,
"scale_resolution": 448,
"slice_end": "</slice>",
"slice_end_token": "</slice>",
"slice_mode": true,
"slice_start": "<slice>",
"slice_start_token": "<slice>",
"std": [
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],
"unk": "<unk>",
"unk_token": "<unk>",
"use_image_id": true,
"version": 4.5
},
"processor_class": "MiniCPMOProcessor"
}
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