Instructions to use octopusmegalopod/some-glmocr-ggufs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use octopusmegalopod/some-glmocr-ggufs 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 octopusmegalopod/some-glmocr-ggufs:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf octopusmegalopod/some-glmocr-ggufs:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf octopusmegalopod/some-glmocr-ggufs:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf octopusmegalopod/some-glmocr-ggufs: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 octopusmegalopod/some-glmocr-ggufs:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf octopusmegalopod/some-glmocr-ggufs: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 octopusmegalopod/some-glmocr-ggufs:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf octopusmegalopod/some-glmocr-ggufs:Q4_K_M

Use Docker

docker model run hf.co/octopusmegalopod/some-glmocr-ggufs:Q4_K_M

LM Studio
Jan

vLLM

How to use octopusmegalopod/some-glmocr-ggufs with vLLM:

Install from pip and serve model

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

Ollama
How to use octopusmegalopod/some-glmocr-ggufs with Ollama:
```
ollama run hf.co/octopusmegalopod/some-glmocr-ggufs:Q4_K_M
```

Unsloth Studio

How to use octopusmegalopod/some-glmocr-ggufs 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 octopusmegalopod/some-glmocr-ggufs 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 octopusmegalopod/some-glmocr-ggufs to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for octopusmegalopod/some-glmocr-ggufs to start chatting

How to use octopusmegalopod/some-glmocr-ggufs with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf octopusmegalopod/some-glmocr-ggufs:Q4_K_M

Configure the model in Pi

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

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use octopusmegalopod/some-glmocr-ggufs with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf octopusmegalopod/some-glmocr-ggufs: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 "octopusmegalopod/some-glmocr-ggufs: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"

Docker Model Runner
How to use octopusmegalopod/some-glmocr-ggufs with Docker Model Runner:
```
docker model run hf.co/octopusmegalopod/some-glmocr-ggufs:Q4_K_M
```

Lemonade

How to use octopusmegalopod/some-glmocr-ggufs with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull octopusmegalopod/some-glmocr-ggufs:Q4_K_M

Run and chat with the model

lemonade run user.some-glmocr-ggufs-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use octopusmegalopod/some-glmocr-ggufs with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf octopusmegalopod/some-glmocr-ggufs: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 octopusmegalopod/some-glmocr-ggufs:Q4_K_M

Run Hermes

hermes

Atomic Chat

GLM-OCR converted for use with llama.cpp. As of b8094, it may crash in llama.cpp if flash-attn is on or certain backends are used (e.g: CPU).

~~Regrettably, does not seem to perform well for single CJK line texts out of the box. Or perhaps it expects the image in a specific resolution, due to its dependence on PaddleLayout.~~

GLM-OCR in llama.cpp behaves differently depending on the maximum context for some reason. Setting -c 2000 gives wrong outputs typically, for example. Whereas -c 9000 has produced much better results so far in my tests.

So far in my tests:

performs poorly on Korean horizontal text lines.
performs decently on Japanese horizontal text lines.
performs poorly on Japanese vertical text lines.
performs excellently on Chinese horizontal text lines.
relatively robust to quantization

Downloads last month: 186

GGUF

Model size

0.9B params

Architecture

glm4

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for octopusmegalopod/some-glmocr-ggufs

Base model

zai-org/GLM-OCR

Quantized

(30)

this model