Instructions to use corsairnui/Unlimited-OCR-AMD-Strix-Halo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use corsairnui/Unlimited-OCR-AMD-Strix-Halo with Transformers:

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

pipe = pipeline("image-text-to-text", model="corsairnui/Unlimited-OCR-AMD-Strix-Halo", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("corsairnui/Unlimited-OCR-AMD-Strix-Halo", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use corsairnui/Unlimited-OCR-AMD-Strix-Halo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "corsairnui/Unlimited-OCR-AMD-Strix-Halo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "corsairnui/Unlimited-OCR-AMD-Strix-Halo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/corsairnui/Unlimited-OCR-AMD-Strix-Halo

SGLang

How to use corsairnui/Unlimited-OCR-AMD-Strix-Halo 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 "corsairnui/Unlimited-OCR-AMD-Strix-Halo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "corsairnui/Unlimited-OCR-AMD-Strix-Halo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "corsairnui/Unlimited-OCR-AMD-Strix-Halo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "corsairnui/Unlimited-OCR-AMD-Strix-Halo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use corsairnui/Unlimited-OCR-AMD-Strix-Halo with Docker Model Runner:
```
docker model run hf.co/corsairnui/Unlimited-OCR-AMD-Strix-Halo
```

Unlimited-OCR AMD Strix Halo

AMD Strix Halo / ROCm packaging for baidu/Unlimited-OCR.

Original model: baidu/Unlimited-OCR.

This repository keeps the Unlimited-OCR model files at the root and adds a small ROCm runner for Ryzen AI Max+ 395 / Radeon 8060S systems. The goal is simple: make Unlimited-OCR practical to run locally on AMD Strix Halo.

This is a community package. It is not an official Baidu release and is not an official Corsair release.

Included

Unlimited-OCR config, tokenizer, model code, assets, paper, license, wheel, and safetensors weight file.
scripts/run_ocr_rocm.py for image and PDF OCR.
scripts/rocm_container.sh for the ROCm PyTorch container.
scripts/setup_container_env.sh for OCR dependencies without replacing the ROCm PyTorch build.
scripts/smoke_test.sh for a local smoke test.
docs/AMD_VALIDATION.md for the validation snapshot from the tested machine.

Tested Machine

CPU/APU: AMD Ryzen AI Max+ 395 w/ Radeon 8060S
GPU target: gfx1151
Container: docker.io/rocm/pytorch:rocm7.2.1_ubuntu24.04_py3.12_pytorch_release_2.9.1
PyTorch observed locally: 2.9.1+rocm7.2.1.gitff65f5bc
HIP observed locally: 7.2.53211-e1a6bc5663
Transformers: 4.57.1

Setup

git clone https://huggingface.co/corsairnui/Unlimited-OCR-AMD-Strix-Halo
cd Unlimited-OCR-AMD-Strix-Halo
bash scripts/rocm_container.sh bash scripts/setup_container_env.sh

Verify device access:

bash scripts/rocm_container.sh bash -lc 'PYTHONPATH=$PWD/.deps:$PYTHONPATH python scripts/verify_rocm.py'

Run OCR

Single image:

bash scripts/rocm_container.sh bash -lc 'PYTHONPATH=$PWD/.deps:$PYTHONPATH python scripts/make_sample_image.py'
bash scripts/rocm_container.sh bash -lc 'PYTHONPATH=$PWD/.deps:$PYTHONPATH python scripts/run_ocr_rocm.py --image samples/strix_halo_ocr_sample.png --output-dir results/my_image --mode base --image-size 512 --max-length 2048'

PDF, practical profile:

bash scripts/rocm_container.sh bash -lc 'PYTHONPATH=$PWD/.deps:$PYTHONPATH python scripts/run_ocr_rocm.py --pdf document.pdf --pdf-strategy pages --quality-profile excellent --quality-target 0.80 --excellent-pdf-dpi 220 --excellent-image-size 1024 --output-dir results/my_pdf --image-size 512 --pdf-dpi 120 --max-length 4096 --retry-image-size 1024 --retry-pdf-dpi 160'

PDF, high-quality profile:

bash scripts/rocm_container.sh bash -lc 'PYTHONPATH=$PWD/.deps:$PYTHONPATH python scripts/run_ocr_rocm.py --pdf document.pdf --pdf-strategy pages --quality-profile high-quality --output-dir results/my_high_quality_pdf --image-size 1024 --pdf-dpi 300 --max-length 32768 --ngram-window 1024 --retry-image-size 1024 --retry-pdf-dpi 300'

Smoke test:

bash scripts/rocm_container.sh bash scripts/smoke_test.sh

Validation Snapshot

Core public PDF benchmark, excellent profile:

Document	Pages	Word F1 vs embedded PDF text
Unlimited-OCR paper	14/14	0.940
IRS W-4	5/5	0.978
Attention Is All You Need	15/15	0.849

High-quality representative subset:

Metric	Result
Completed cases	8/8
Mean word F1	0.886
Mean Unicode char F1	0.886
Throughput	1.53 pages/min
Hard-loop alerts	0
Structure detected	4 tables, 2 equations, 4 images, 2 captions
Non-Latin OCR characters	4495

Notes

This is a community AMD packaging of the upstream MIT-licensed Unlimited-OCR model. Runtime quality depends on document type, scan quality, PDF rendering settings, available memory, and ROCm/PyTorch versions.

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Safetensors

Model size

3B params

Tensor type

BF16

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Image-Text-to-Text

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