Instructions to use giannisan/GLM-5.2-ds4-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 giannisan/GLM-5.2-ds4-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 giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ
# Run inference directly in the terminal:
llama cli -hf giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ
# Run inference directly in the terminal:
llama cli -hf giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

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 giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ
# Run inference directly in the terminal:
./llama-cli -hf giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

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 giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ
# Run inference directly in the terminal:
./build/bin/llama-cli -hf giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

Use Docker

docker model run hf.co/giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

LM Studio
Jan

Ollama

How to use giannisan/GLM-5.2-ds4-gguf with Ollama:

ollama run hf.co/giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

Unsloth Desktop

How to use giannisan/GLM-5.2-ds4-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

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": "giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use giannisan/GLM-5.2-ds4-gguf with Docker Model Runner:
```
docker model run hf.co/giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ
```

Lemonade

How to use giannisan/GLM-5.2-ds4-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

Run and chat with the model

lemonade run user.GLM-5.2-ds4-gguf-UD-IQ2_XXS_ROUTEDIQ

List all available models

lemonade list

Hermes Agent

How to use giannisan/GLM-5.2-ds4-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 giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

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 giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use giannisan/GLM-5.2-ds4-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ

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 "giannisan/GLM-5.2-ds4-gguf:UD-IQ2_XXS_ROUTEDIQ" \
  --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"

giannisan commited on Jul 7

Commit

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@@ -11,8 +11,12 @@ tags:
 # GLM-5.2 GGUF for ds4 (SSD streaming, CUDA)
-This is a mixed-precision GGUF of [GLM-5.2](https://huggingface.co/zai-org/GLM-5.2) (743B MoE)
-assembled for the [ds4](https://github.com/antirez/ds4) inference engine, specifically the
 [glm-local branch](https://github.com/giannisanni/neutronstar/tree/glm-local) which adds the CUDA port,
 SSD expert streaming optimizations, and the first MTP speculative-decoding implementation for
 GLM 5.2 on any backend.

 # GLM-5.2 GGUF for ds4 (SSD streaming, CUDA)
+This is a mirror of the official ds4 GGUF of [GLM-5.2](https://huggingface.co/zai-org/GLM-5.2)
+(743B MoE) built by antirez and published at
+[antirez/GLM-5.2-GGUF](https://huggingface.co/antirez/GLM-5.2-GGUF) (bit-identical file, same
+sha256). Credit for the quantization is his; this repo re-documents it with the full
+per-tensor recipe below and pairs it with the CUDA/SSD-streaming usage notes. It is the file
+used by the [ds4](https://github.com/antirez/ds4) inference engine, specifically the
 [glm-local branch](https://github.com/giannisanni/neutronstar/tree/glm-local) which adds the CUDA port,
 SSD expert streaming optimizations, and the first MTP speculative-decoding implementation for
 GLM 5.2 on any backend.