Image-Text-to-Text
Transformers
Safetensors
monkeyocrv2
text-generation
conversational
custom_code
Eval Results
Instructions to use zenosai/MonkeyOCRv2-B-Parsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenosai/MonkeyOCRv2-B-Parsing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="zenosai/MonkeyOCRv2-B-Parsing", 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 AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("zenosai/MonkeyOCRv2-B-Parsing", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zenosai/MonkeyOCRv2-B-Parsing with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zenosai/MonkeyOCRv2-B-Parsing" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zenosai/MonkeyOCRv2-B-Parsing", "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/zenosai/MonkeyOCRv2-B-Parsing
- SGLang
How to use zenosai/MonkeyOCRv2-B-Parsing 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 "zenosai/MonkeyOCRv2-B-Parsing" \ --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": "zenosai/MonkeyOCRv2-B-Parsing", "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 "zenosai/MonkeyOCRv2-B-Parsing" \ --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": "zenosai/MonkeyOCRv2-B-Parsing", "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 zenosai/MonkeyOCRv2-B-Parsing with Docker Model Runner:
docker model run hf.co/zenosai/MonkeyOCRv2-B-Parsing
File size: 2,186 Bytes
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"architectures": [
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"model_type": "monkeyocrv2",
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"vision_config": {
"attn_implementation": "flash_attention_2",
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"embed_dim": 768,
"gradient_checkpointing": false,
"hidden_size": 1024,
"init_merger_std": 0.02,
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"is_causal": false,
"model_type": "monkeyocr_vit",
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"num_channels": 3,
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"pad_token_id": 151643,
"patch_size": 14,
"post_norm": true,
"rms_norm_eps": 1e-05,
"spatial_merge_size": 2,
"temporal_patch_size": 1,
"use_bias": false
},
"vocab_size": 151936
}
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