Instructions to use Sawfwair/Infinity-Parser2-Pro-Int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sawfwair/Infinity-Parser2-Pro-Int8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sawfwair/Infinity-Parser2-Pro-Int8")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Sawfwair/Infinity-Parser2-Pro-Int8")
model = AutoModelForMultimodalLM.from_pretrained("Sawfwair/Infinity-Parser2-Pro-Int8")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use Sawfwair/Infinity-Parser2-Pro-Int8 with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("Sawfwair/Infinity-Parser2-Pro-Int8")
config = load_config("Sawfwair/Infinity-Parser2-Pro-Int8")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use Sawfwair/Infinity-Parser2-Pro-Int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sawfwair/Infinity-Parser2-Pro-Int8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sawfwair/Infinity-Parser2-Pro-Int8",
		"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/Sawfwair/Infinity-Parser2-Pro-Int8

SGLang

How to use Sawfwair/Infinity-Parser2-Pro-Int8 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 "Sawfwair/Infinity-Parser2-Pro-Int8" \
    --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": "Sawfwair/Infinity-Parser2-Pro-Int8",
		"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 "Sawfwair/Infinity-Parser2-Pro-Int8" \
        --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": "Sawfwair/Infinity-Parser2-Pro-Int8",
		"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"
						}
					}
				]
			}
		]
	}'

How to use Sawfwair/Infinity-Parser2-Pro-Int8 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Sawfwair/Infinity-Parser2-Pro-Int8"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Sawfwair/Infinity-Parser2-Pro-Int8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Sawfwair/Infinity-Parser2-Pro-Int8 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Sawfwair/Infinity-Parser2-Pro-Int8"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Sawfwair/Infinity-Parser2-Pro-Int8

Run Hermes

hermes

OpenClaw new

How to use Sawfwair/Infinity-Parser2-Pro-Int8 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Sawfwair/Infinity-Parser2-Pro-Int8"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Sawfwair/Infinity-Parser2-Pro-Int8" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Sawfwair/Infinity-Parser2-Pro-Int8 with Docker Model Runner:
```
docker model run hf.co/Sawfwair/Infinity-Parser2-Pro-Int8
```

Infinity-Parser2-Pro Int8

This repository contains a MLX affine int8 quantization of infly/Infinity-Parser2-Pro prepared for native mere.run Q35 OCR evaluation.

Changes From The Base Model

Quantized eligible language-model linear weights to int8.
Used group size 64 with MLX affine quantization metadata.
Split Infinity-Parser2-Pro fused expert tensors into the native switch_mlp layout expected by mere.run.
Preserved vision tower and tokenizer sidecar files required by the native Qwen-family OCR runtime.
Added mererun_model.json metadata for managed local installation.

Intended Use

Use this model as the quality-focused native Infinity-Parser2 Pro OCR option in mere.run:

mere.run model pull vision-ocr-infinity-pro-int8
mere.run vision ocr ./page.png \
  --backend infinity \
  --infinity-model vision-ocr-infinity-pro-int8 \
  --infinity-task doc2md \
  --temperature 0

mere.run keeps LightOnOCR as the default OCR backend because it is smaller and more predictable across the local smoke set. This quantized Pro model is for document types where Pro's layout and parsing quality justify higher latency and memory use.

Local Evaluation Notes

On local samples, this int8 model improved over Infinity-Parser2-Flash on some metadata-heavy article layouts, while LightOnOCR remained stronger on the tested default-OCR mix. Treat this as an eval target rather than a universal default.

License

The base model is licensed under Apache-2.0. This quantized derivative is distributed under Apache-2.0 as well. See LICENSE and NOTICE.

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Safetensors

Model size

10B params

Tensor type

BF16

U32

MLX

Hardware compatibility

8-bit

Model tree for Sawfwair/Infinity-Parser2-Pro-Int8

Base model

infly/Infinity-Parser2-Pro

Quantized

(3)

this model