Instructions to use emwesoft/GLM-5.3-DFlash2-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 emwesoft/GLM-5.3-DFlash2-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 emwesoft/GLM-5.3-DFlash2-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf emwesoft/GLM-5.3-DFlash2-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf emwesoft/GLM-5.3-DFlash2-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf emwesoft/GLM-5.3-DFlash2-GGUF:BF16

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 emwesoft/GLM-5.3-DFlash2-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf emwesoft/GLM-5.3-DFlash2-GGUF:BF16

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 emwesoft/GLM-5.3-DFlash2-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf emwesoft/GLM-5.3-DFlash2-GGUF:BF16

Use Docker

docker model run hf.co/emwesoft/GLM-5.3-DFlash2-GGUF:BF16

LM Studio
Jan
Ollama
How to use emwesoft/GLM-5.3-DFlash2-GGUF with Ollama:
```
ollama run hf.co/emwesoft/GLM-5.3-DFlash2-GGUF:BF16
```
Unsloth Desktop

How to use emwesoft/GLM-5.3-DFlash2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emwesoft/GLM-5.3-DFlash2-GGUF:BF16

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": "emwesoft/GLM-5.3-DFlash2-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use emwesoft/GLM-5.3-DFlash2-GGUF with Docker Model Runner:
```
docker model run hf.co/emwesoft/GLM-5.3-DFlash2-GGUF:BF16
```

Lemonade

How to use emwesoft/GLM-5.3-DFlash2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull emwesoft/GLM-5.3-DFlash2-GGUF:BF16

Run and chat with the model

lemonade run user.GLM-5.3-DFlash2-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use emwesoft/GLM-5.3-DFlash2-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 emwesoft/GLM-5.3-DFlash2-GGUF:BF16

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 emwesoft/GLM-5.3-DFlash2-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use emwesoft/GLM-5.3-DFlash2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emwesoft/GLM-5.3-DFlash2-GGUF:BF16

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 "emwesoft/GLM-5.3-DFlash2-GGUF:BF16" \
  --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-5.3 DFlash2 drafters (GGUF)

GGUF conversions of incoai/GLM-5.3-DFlash2 — the DFlash2 block-diffusion drafter for GLM-5.3 753B (zai-org/GLM-5.3).

File	Size
`GLM-5.3-DFlash2-BF16.gguf`	4.59 GiB — lossless from source
`GLM-5.3-DFlash2-Q8_0.gguf`	2.44 GiB

-md GLM-5.3-DFlash2-Q8_0.gguf --spec-type draft-dflash --spec-draft-n-max 7

block_size is 8, so 7 is the ceiling — llama.cpp clamps anything higher. Lower values can win when the target's experts are partly CPU-resident, since each verification step is then more expensive; sweep it for your setup.

Works with any GLM-5.3 GGUF — DFlash consumes the target's hidden states (hidden_size 6144, layers [6,20,34,48,62,76]), not its quantisation. Not compatible with GLM-5.3-Flash (hidden 4096).

Downloads last month: 211

GGUF

Model size

2B params

Architecture

dflash

Hardware compatibility

8-bit

16-bit

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Model tree for emwesoft/GLM-5.3-DFlash2-GGUF

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