Instructions to use AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-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 AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-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 AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

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 AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

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 AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF with Ollama:
```
ollama run hf.co/AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL
```
Unsloth Desktop

How to use AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

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": "AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF with Docker Model Runner:
```
docker model run hf.co/AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL
```

Lemonade

How to use AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.GLM-5.3-Flash-UNCENSORED-V2-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-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 AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

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 AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL

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 "AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF:UD-Q4_K_XL" \
  --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"

A better uncensored version of GLM-5.3-Flash than my previous version.

I merged MorinoNushi's lora with the fp16 model weights of GLM-5.3-Flash. Then I quantized the result using Unsloth's pipeline.

It allows faster inference time than loading the lora on top of the model, and it fixes some precision errors. For instance, loading the lora on top of the base UD-Q4_K_XL considerably reduced the reasoning budget for some reason. And now it doesn't happen anymore.

Downloads last month: 19

GGUF

Model size

321B params

Architecture

glm5next

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for AliceThirty/GLM-5.3-Flash-UNCENSORED-V2-GGUF

Base model

zai-org/GLM-5.3-Flash

Adapter

MorinoNushi/GLM-5.3-Flash-Heretic-Abliterated-LoRA-V2-GGUF

Quantized

(1)

this model