Instructions to use prithivMLmods/ArmorOCR-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/ArmorOCR-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/ArmorOCR-GGUF")
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 AutoModel
model = AutoModel.from_pretrained("prithivMLmods/ArmorOCR-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/ArmorOCR-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/ArmorOCR-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/ArmorOCR-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/ArmorOCR-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/ArmorOCR-GGUF",
		"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/prithivMLmods/ArmorOCR-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/ArmorOCR-GGUF 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 "prithivMLmods/ArmorOCR-GGUF" \
    --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": "prithivMLmods/ArmorOCR-GGUF",
		"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 "prithivMLmods/ArmorOCR-GGUF" \
        --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": "prithivMLmods/ArmorOCR-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/ArmorOCR-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/ArmorOCR-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/ArmorOCR-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/ArmorOCR-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/ArmorOCR-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/ArmorOCR-GGUF to start chatting

How to use prithivMLmods/ArmorOCR-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/ArmorOCR-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/ArmorOCR-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/ArmorOCR-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/ArmorOCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/ArmorOCR-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ArmorOCR-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/ArmorOCR-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M

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 prithivMLmods/ArmorOCR-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/ArmorOCR-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/ArmorOCR-GGUF:Q4_K_M

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 "prithivMLmods/ArmorOCR-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

ArmorOCR-GGUF / README.md

prithivMLmods

Update README.md

ae3c4ce verified about 16 hours ago

preview code

Raw

History Blame Contribute Delete

4.05 kB

metadata

license: apache-2.0
base_model:
  - inclusionAI/ArmorOCR
tags:
  - text-generation-inference
  - llama-cpp
  - qwen3_vl
  - ocr
  - multimodal
  - vision-language
  - adversarial ocr
  - grounded ocr
language:
  - en
pipeline_tag: image-text-to-text
library_name: transformers

ArmorOCR-GGUF

ArmorOCR is a two-stage framework for grounded adversarial OCR perception from Ant Group's inclusionAI, built on Qwen3-VL-8B-Instruct and introduced in the paper "ArmorOCR: Grounded Adversarial Visual Perception via Observation-Transferred Self-Distillation." Its key design goal is robustness to adversarial or degraded visual conditions while enabling single-pass inference directly on the original image, without requiring any inference-time visual transformations (such as cropping, upscaling, or denoising) or external tool assistance. The model follows a structured reasoning-then-answer output format, wrapping its analysis in <analyze></analyze> tags and the final recognized text in <answer></answer> tags, and is loaded via the standard Qwen3VLForConditionalGeneration class in Transformers. Training details, the accompanying AdvSpot adversarial OCR benchmark, and evaluation scripts are hosted separately on the project's GitHub repository, and the model is released under Apache License 2.0, with usage additionally subject to the base Qwen3-VL-8B-Instruct model's license and acceptable-use policy.

Model Files

File Name	Quant Type	File Size	File Link
ArmorOCR.BF16.gguf	BF16	16.4 GB	Download
ArmorOCR.F16.gguf	F16	16.4 GB	Download
ArmorOCR.Q2_K.gguf	Q2_K	3.28 GB	Download
ArmorOCR.Q3_K_L.gguf	Q3_K_L	4.43 GB	Download
ArmorOCR.Q3_K_M.gguf	Q3_K_M	4.12 GB	Download
ArmorOCR.Q3_K_S.gguf	Q3_K_S	3.77 GB	Download
ArmorOCR.Q4_0.gguf	Q4_0	4.77 GB	Download
ArmorOCR.Q4_K_M.gguf	Q4_K_M	5.03 GB	Download
ArmorOCR.Q4_K_S.gguf	Q4_K_S	4.8 GB	Download
ArmorOCR.Q5_0.gguf	Q5_0	5.72 GB	Download
ArmorOCR.Q5_K_M.gguf	Q5_K_M	5.85 GB	Download
ArmorOCR.Q5_K_S.gguf	Q5_K_S	5.72 GB	Download
ArmorOCR.Q6_K.gguf	Q6_K	6.73 GB	Download
ArmorOCR.Q8_0.gguf	Q8_0	8.71 GB	Download
ArmorOCR.mmproj-bf16.gguf	mmproj-bf16	1.16 GB	Download
ArmorOCR.mmproj-f16.gguf	mmproj-f16	1.16 GB	Download
ArmorOCR.mmproj-q8_0.gguf	mmproj-q8_0	752 MB	Download

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp