Instructions to use zenosai/MonkeyOCRv2-S-Parsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zenosai/MonkeyOCRv2-S-Parsing with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="zenosai/MonkeyOCRv2-S-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-S-Parsing", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zenosai/MonkeyOCRv2-S-Parsing with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zenosai/MonkeyOCRv2-S-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-S-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-S-Parsing

SGLang

How to use zenosai/MonkeyOCRv2-S-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-S-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-S-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-S-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-S-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-S-Parsing with Docker Model Runner:
```
docker model run hf.co/zenosai/MonkeyOCRv2-S-Parsing
```

MonkeyOCRv2-S-Parsing / config.json

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	{
	"architectures": [
	"MonkeyOCRv2ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_monkeyocrv2.MonkeyOCRv2Config",
	"AutoModelForCausalLM": "modeling_monkeyocrv2.MonkeyOCRv2ForCausalLM"
	},
	"dtype": "bfloat16",
	"eos_token_id": 151645,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 1024,
	"image_token_id": 151655,
	"initializer_range": 0.02,
	"intermediate_size": 3072,
	"layer_types": [
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention"
	],
	"max_position_embeddings": 40960,
	"max_window_layers": 28,
	"model_type": "monkeyocrv2",
	"num_attention_heads": 16,
	"num_hidden_layers": 28,
	"num_key_value_heads": 8,
	"pad_token_id": 151643,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 1000000,
	"sliding_window": null,
	"tie_word_embeddings": true,
	"transformers_version": "4.57.1",
	"use_cache": false,
	"use_sliding_window": false,
	"video_token_id": 151656,
	"vision_config": {
	"attn_implementation": "flash_attention_2",
	"dtype": "bfloat16",
	"embed_dim": 384,
	"gradient_checkpointing": false,
	"hidden_size": 1024,
	"init_merger_std": 0.02,
	"initializer_range": 0.02,
	"intermediate_size": 1536,
	"is_causal": false,
	"model_type": "monkeyocr_vit",
	"num_attention_heads": 6,
	"num_channels": 3,
	"num_hidden_layers": 12,
	"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
	}