Instructions to use unsloth/GLM-4.7-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/GLM-4.7-Flash-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="unsloth/GLM-4.7-Flash-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("unsloth/GLM-4.7-Flash-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unsloth/GLM-4.7-Flash-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/GLM-4.7-Flash-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/GLM-4.7-Flash-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/GLM-4.7-Flash-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/GLM-4.7-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/GLM-4.7-Flash-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/GLM-4.7-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

SGLang

How to use unsloth/GLM-4.7-Flash-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "unsloth/GLM-4.7-Flash-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/GLM-4.7-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "unsloth/GLM-4.7-Flash-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/GLM-4.7-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use unsloth/GLM-4.7-Flash-GGUF with Ollama:
```
ollama run hf.co/unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/GLM-4.7-Flash-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/GLM-4.7-Flash-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/GLM-4.7-Flash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/GLM-4.7-Flash-GGUF to start chatting

How to use unsloth/GLM-4.7-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use unsloth/GLM-4.7-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/GLM-4.7-Flash-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 "unsloth/GLM-4.7-Flash-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"

Docker Model Runner
How to use unsloth/GLM-4.7-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/GLM-4.7-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.GLM-4.7-Flash-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use unsloth/GLM-4.7-Flash-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 unsloth/GLM-4.7-Flash-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

Anyone tried to challenge the model?

#16

by urtuuuu - opened Jan 25

Discussion

urtuuuu

Jan 25

•

edited Jan 25

I decided to try and create a chess game in html and so far it's not easy at all. The model seems very stupid at times. I'm doing everything step by step because it seems impossible in one go (btw gemini 3 pro did it with just one promt, even the bot opponent is working).
But this model...first it created just the chess board and nothing else as i promted, but the dark and light squares were swapped, not correct. So i told it to swap the squares, light with dark. And it failed! It's an easy fix, i just did it myself then... just these two lines of code
--board-light: #b58863; /* Light Wood /
--board-dark: #594034; / Dark Wood */
swap the colors and done, but this model overthinking like crazy, and even broke the whole board.
Now i'm trying to add the chess pieces, trying... man this model just doesn't know chess it seems...

Maybe anyone willing to try this too? 😀 and tell us if it's possible at all.

I'm using llama cli, UD-Q4_K_XL.gguf and settings seems to work well --multiline-input --jinja --temp 0.7 --top-p 1.0 --min-p 0.01 . But this task seems impossible.

urtuuuu changed discussion title from Anyone tried to challenge to model? to Anyone tried to challenge the model? Jan 25

kalota

Jan 28

"gemini 3 pro did it with just one promt"

BingoBird

Feb 23

Your observation is correct that GLM-4.7-lash is not competitive with Gemini Pro.

Personally, I dont rely on small models to get code right, but they can suggest code that I might choose to use after modification.

urtuuuu

Feb 23

•

edited Feb 23

Actually, i managed to create a chess game against bot in html using this model. It just takes time...step-by-step, only using llama.cpp. So this model impressed me. I don't mind spending some time improving code and fixing bugs :) At least it works well

Later i created texas holdem poker against 3 bots, it's not bad, just some gameplay bugs that need to be fixed.

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