Instructions to use Shamika2004/coco1-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Shamika2004/coco1-ocr 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 Shamika2004/coco1-ocr:Q8_0
# Run inference directly in the terminal:
llama cli -hf Shamika2004/coco1-ocr:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Shamika2004/coco1-ocr:Q8_0
# Run inference directly in the terminal:
llama cli -hf Shamika2004/coco1-ocr:Q8_0

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 Shamika2004/coco1-ocr:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Shamika2004/coco1-ocr:Q8_0

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 Shamika2004/coco1-ocr:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Shamika2004/coco1-ocr:Q8_0

Use Docker

docker model run hf.co/Shamika2004/coco1-ocr:Q8_0

LM Studio
Jan

vLLM

How to use Shamika2004/coco1-ocr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shamika2004/coco1-ocr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shamika2004/coco1-ocr",
		"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/Shamika2004/coco1-ocr:Q8_0

Ollama
How to use Shamika2004/coco1-ocr with Ollama:
```
ollama run hf.co/Shamika2004/coco1-ocr:Q8_0
```
Unsloth Desktop

How to use Shamika2004/coco1-ocr with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shamika2004/coco1-ocr:Q8_0

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": "Shamika2004/coco1-ocr:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Shamika2004/coco1-ocr with Docker Model Runner:
```
docker model run hf.co/Shamika2004/coco1-ocr:Q8_0
```

Lemonade

How to use Shamika2004/coco1-ocr with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Shamika2004/coco1-ocr:Q8_0

Run and chat with the model

lemonade run user.coco1-ocr-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Shamika2004/coco1-ocr with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shamika2004/coco1-ocr:Q8_0

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 Shamika2004/coco1-ocr:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shamika2004/coco1-ocr with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shamika2004/coco1-ocr:Q8_0

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 "Shamika2004/coco1-ocr:Q8_0" \
  --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"

coco1-ocr / README.md

Shamika2004

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	---
	base_model: Qwen/Qwen3-VL-4B-Instruct
	library_name: gguf
	pipeline_tag: image-text-to-text
	tags:
	- gguf
	- qwen3-vl
	- ocr
	- table-recognition
	- llama.cpp
	---

	# coco1-ocr (GGUF)

	Fine-tuned from [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) (LoRA, merged) for document understanding: table-to-HTML, checkbox/option transcription, handwritten line transcription, digit recognition, and layout detection.

	## Files

	\| File \| Purpose \|
	\|---\|---\|
	\| `coco1-ocr-Q8_0.gguf` \| Language model (Q8_0) \|
	\| `mmproj-coco1-ocr-f16.gguf` \| Vision projector (F16), required for image input \|

	## Usage (llama.cpp)

	```bash
	llama-mtmd-cli -m coco1-ocr-Q8_0.gguf --mmproj mmproj-coco1-ocr-f16.gguf \
	--image table.png \
	-p "Convert the table in the image to HTML. Use only <table>, <tr> and <td>, with rowspan/colspan attributes for merged cells. Keep empty cells as <td></td>."
	```

	## Prompts used in training

	- table: Convert the table in the image to HTML. Use only <table>, <tr> and <td>, with rowspan/colspan attributes for merged cells. Keep empty cells as <td></td>.
	- check_text: Transcribe the options shown. Put ☑ before the option that is marked or selected and ☐ before every other option.
	- hw_line: Transcribe the handwritten text.
	- digit: What digit is shown? Answer with the digit only.
	- layout: List every layout element with its category and bounding box [x0,y0,x1,y1] in 0-1000 relative coordinates, from top to bottom.

	## Notes

	Converted to GGUF from the merged fine-tuned weights. Use the same prompts as above for best results.