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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use axteam/GLM-OCR-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 axteam/GLM-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf axteam/GLM-OCR-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 axteam/GLM-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf axteam/GLM-OCR-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf axteam/GLM-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf axteam/GLM-OCR-GGUF:Q4_K_M

Use Docker

docker model run hf.co/axteam/GLM-OCR-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use axteam/GLM-OCR-GGUF with Ollama:
```
ollama run hf.co/axteam/GLM-OCR-GGUF:Q4_K_M
```
Unsloth Desktop

How to use axteam/GLM-OCR-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf axteam/GLM-OCR-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": "axteam/GLM-OCR-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use axteam/GLM-OCR-GGUF with Docker Model Runner:
```
docker model run hf.co/axteam/GLM-OCR-GGUF:Q4_K_M
```

Lemonade

How to use axteam/GLM-OCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull axteam/GLM-OCR-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-OCR-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use axteam/GLM-OCR-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 axteam/GLM-OCR-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 axteam/GLM-OCR-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use axteam/GLM-OCR-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf axteam/GLM-OCR-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 "axteam/GLM-OCR-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"

GLM-OCR · GGUF (llama.cpp)

GGUF quantizations of zai-org/GLM-OCR for llama.cpp / Ollama / LM Studio. Produced by axteam.

File	Size
`GLM-OCR-Q4_K_M.gguf`	0.5 GB
`GLM-OCR-Q8_0.gguf`	1.0 GB

Q8_0 — 8-bit, highest GGUF quality, closest to the original.
Q4_K_M — 4-bit (k-quant medium), ~half the size, small quality trade-off.

Converted with llama.cpp convert_hf_to_gguf.py (recognized as glm4 (text backbone)); the original chat_template is embedded. The vision encoder is not included in these GGUF files — this is the text-backbone LLM only, so it serves text generation, not the full OCR/image pipeline. For end-to-end OCR use the original model or the compressed-tensors builds with vLLM.

Usage

llama-cli -hf axteam/GLM-OCR-GGUF

한국어 요약

zai-org/GLM-OCR 의 GGUF 양자화(llama.cpp/Ollama/LM Studio). 작성: axteam.

Q8_0(8비트, 최고품질) · Q4_K_M(4비트, 절반 크기).
llama.cpp convert_hf_to_gguf.py 로 변환(glm4 (text backbone) 인식), chat_template 내장.
⚠️ 비전 인코더 미포함 — 텍스트 백본 LLM 전용입니다. 전체 OCR 파이프라인은 원본 또는 vLLM용 compressed-tensors 빌드를 쓰세요.

Downloads last month: 90

GGUF

Model size

0.9B params

Architecture

glm4

Hardware compatibility

4-bit

8-bit

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