Instructions to use binedge/dots.mocr-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use binedge/dots.mocr-FP8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="binedge/dots.mocr-FP8", 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("binedge/dots.mocr-FP8", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use binedge/dots.mocr-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "binedge/dots.mocr-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "binedge/dots.mocr-FP8",
		"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/binedge/dots.mocr-FP8

SGLang

How to use binedge/dots.mocr-FP8 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 "binedge/dots.mocr-FP8" \
    --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": "binedge/dots.mocr-FP8",
		"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 "binedge/dots.mocr-FP8" \
        --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": "binedge/dots.mocr-FP8",
		"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 binedge/dots.mocr-FP8 with Docker Model Runner:
```
docker model run hf.co/binedge/dots.mocr-FP8
```

dots.mocr-FP8 / README.md

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metadata

license: apache-2.0
base_model: rednote-hilab/dots.mocr
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - fp8
  - compressed-tensors
  - llm-compressor
  - quantized
  - multimodal

dots.mocr-FP8

FP8-quantized version of rednote-hilab/dots.mocr.

This model was quantized with llm-compressor using FP8 dynamic activation quantization for the text backbone. The custom vision tower was intentionally excluded from quantization and kept in BF16.

Quantization details

Base model: rednote-hilab/dots.mocr
Quantization tool: llm-compressor
Saved format: compressed-tensors
Quantization scheme: FP8_DYNAMIC
Targets: Linear
Ignored modules:
- lm_head
- .*vision_tower.*

Quantization recipe

from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(
    targets="Linear",
    scheme="FP8_DYNAMIC",
    ignore=[
        "lm_head",
        "re:.*vision_tower.*",
    ],
)

oneshot(model=model, recipe=recipe)

model.save_pretrained("binedge/dots.mocr-FP8", save_compressed=True)
processor.save_pretrained("binedge/dots.mocr-FP8")