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 / README.md

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	---
	license: apache-2.0
	base_model: infly/Infinity-Parser2-Pro
	base_model_relation: quantized
	library_name: transformers
	tags:
	- mlx
	- int8
	- ocr
	- document-parsing
	- document-understanding
	- infinity-parser2
	- mere-run
	---

	# Infinity-Parser2-Pro Int8

	This repository contains a MLX affine int8 quantization of
	[`infly/Infinity-Parser2-Pro`](https://huggingface.co/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`:

	```bash
	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`.