Instructions to use ctogaurav/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 ctogaurav/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 ctogaurav/GLM_OCR-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf ctogaurav/GLM_OCR-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ctogaurav/GLM_OCR-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf ctogaurav/GLM_OCR-GGUF: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 ctogaurav/GLM_OCR-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ctogaurav/GLM_OCR-GGUF: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 ctogaurav/GLM_OCR-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ctogaurav/GLM_OCR-GGUF:Q8_0

Use Docker

docker model run hf.co/ctogaurav/GLM_OCR-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use ctogaurav/GLM_OCR-GGUF with Ollama:
```
ollama run hf.co/ctogaurav/GLM_OCR-GGUF:Q8_0
```
Unsloth Desktop

How to use ctogaurav/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 ctogaurav/GLM_OCR-GGUF: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": "ctogaurav/GLM_OCR-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ctogaurav/GLM_OCR-GGUF with Docker Model Runner:
```
docker model run hf.co/ctogaurav/GLM_OCR-GGUF:Q8_0
```

Lemonade

How to use ctogaurav/GLM_OCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ctogaurav/GLM_OCR-GGUF:Q8_0

Run and chat with the model

lemonade run user.GLM_OCR-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use ctogaurav/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 ctogaurav/GLM_OCR-GGUF: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 ctogaurav/GLM_OCR-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ctogaurav/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 ctogaurav/GLM_OCR-GGUF: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 "ctogaurav/GLM_OCR-GGUF: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"

GLM-OCR GGUF Models (for LM Studio / Ollama / llama.cpp)

GGUF conversions of ctogaurav/GLM_OCR — fine-tuned LoRA adapters merged into zai-org/GLM-OCR (0.9B) and quantized for fast, local, private CPU/GPU inference.

📦 Available Versions & Files

Each model requires two files — the quantized language model and its vision projector (mmproj). Download both files into the same directory:

Version	Language Model GGUF	Multimodal Projector (`mmproj`)	Ollama Setup	Status
v5.0 (Latest SOTA)	`v5.0/GLM-OCR-v5.0-Q8_0.gguf` (~682 MB)	`v5.0/mmproj-GLM-OCR-v5.0-Q8_0.gguf` (~484 MB)	`v5.0/Modelfile`	Lowest CER (0.3377), 82.0% compile
v4.1	`v4.1/GLM-OCR-v4.1-Q8_0.gguf` (~683 MB)	`v4.1/mmproj-GLM-OCR-v4.1-Q8_0.gguf` (~485 MB)	`v4.1/Modelfile`	CER: 0.3816, 82.4% compile
v3.1	`v3.1/GLM-OCR-v3.1-Q8_0.gguf` (~683 MB)	`v3.1/mmproj-GLM-OCR-v3.1-Q8_0.gguf` (~485 MB)	`v3.1/Modelfile`	Highest compile rate (88.9%)

⚡ Quickstart: Running in LM Studio

Download GLM-OCR-v5.0-Q8_0.gguf and mmproj-GLM-OCR-v5.0-Q8_0.gguf into your LM Studio models directory:
```
~/.lmstudio/models/ctogaurav/GLM_OCR-GGUF/
```
LM Studio will automatically recognize the multimodal projector (mmproj) alongside the language model.
In the Chat tab, load GLM-OCR-v5.0-Q8_0 and drag-and-drop your math scan.

🦙 Quickstart: Running in Ollama

Download the files into a folder:

huggingface-cli download ctogaurav/GLM_OCR-GGUF --include "v5.0/*" --local-dir ./glm_v5
cd glm_v5/v5.0

Create and run the model in Ollama:

ollama create glm-ocr-v5.0 -f Modelfile
ollama run glm-ocr-v5.0 "OCR this handwritten math page. Convert ONLY the handwritten mathematical content into a complete, compilable LaTeX document. Output only LaTeX. path/to/page.png"

⚠️ Recommended Inference Settings (Crucial)

Setting	Value	Rationale
Context Length (`num_ctx`)	≥ 8192	The vision encoder consumes ~1,536 tokens. Contexts below 4,096 will fail to decode.
Temperature	`0` (Greedy)	Fine-tuned and benchmarked with deterministic greedy decoding.
Repeat Penalty	`1.0` (Disabled)	Matches benchmark conditions.
Max Tokens (`num_predict`)	`1024` - `2048`	Covers full-page dense mathematical derivations.

📊 Benchmark Scorecard (250 Held-Out Pages)

System	Mean CER ↓	Norm CER ↓	Math-F1 ↑	Compile Rate ↑	Training Hardware
Base GLM-OCR	0.5151	0.4910	0.7031	0.0%	—
GLM-OCR v3.1	0.3971	0.3753	0.8171	88.9%	RTX 3060
GLM-OCR v4.1	0.3816	0.4106	0.8272	82.4%	RTX 3060
GLM-OCR v5.0 (Ours, SOTA)	0.3377 🏆	0.3683 🏆	0.8358 🏆	82.0%	Hybrid: Local RTX 3060 (step 0–250) ➔ Cloud A100 (step 250–3945)

Full training methodology, benchmark logs, and dataset curation pipeline are available at:
👉 github.com/realgauravvyas/ocr2tex \n### 🖥️ Training Journey: Local RTX 3060 ➔ Step 250 Warm-Start Cloud Handoff

Phase 1 (Local RTX 3060 12GB): Steps 0–250 trained locally (batch size 1, grad accum 8, FP16, max_length=3584, max_image_tokens=1536). Under continuous 100% compute, the GPU reached 88°C thermal throttling at 57.05s/step (~62.5 hour ETA).
Phase 2 (Cloud A100 SXM4 80GB): Handed off from the step-250 checkpoint to Lightning AI Studio, accelerating the remaining training to 3.80s/step (15× speedup) and reaching convergence loss 0.0008 in 281 minutes.\n

Downloads last month: 369

GGUF

Model size

0.7B params

Architecture

glm4

Hardware compatibility

8-bit

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Model tree for ctogaurav/GLM_OCR-GGUF

Base model

zai-org/GLM-OCR

Quantized

(35)

this model