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

Libraries

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

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

pipe = pipeline("text-generation", model="unsloth/GLM-4.7-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-GGUF", dtype="auto")

llama-cpp-python

How to use unsloth/GLM-4.7-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/GLM-4.7-GGUF",
	filename="BF16/GLM-4.7-BF16-00001-of-00015.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use unsloth/GLM-4.7-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/GLM-4.7-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/GLM-4.7-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/GLM-4.7-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/GLM-4.7-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-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/GLM-4.7-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-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/GLM-4.7-GGUF:UD-Q4_K_XL

Use Docker

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

LM Studio
Jan

vLLM

How to use unsloth/GLM-4.7-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-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-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use unsloth/GLM-4.7-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-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-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-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-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio new

How to use unsloth/GLM-4.7-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-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-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-GGUF to start chatting

Pi new

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

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/GLM-4.7-GGUF with Hermes Agent:

Start the llama.cpp server

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

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

GLM 4.5, 4.6, 4.7 Quality of Life updates

pinned

by danielhanchen - opened Dec 27, 2025

Discussion

danielhanchen

Unsloth AI org Dec 27, 2025

We did a refresh of quants (quality of life updates) for GLM 4.5, 4.6 and 4.7

llama.cpp and other inference engines like LM Studio now support more features including but not limited to:

Non ascii decoding for tools (affects non English languages) For eg before the default (ensure_ascii=True) would cause "café" → "caf\u00e9", whilst now ensure_ascii=False would tokenize "café" → "café". I would re-download our quants if you use languages other than English.
Converts reasoning content parsing to original [0], [-1] from our changes of |first and |last. We used to change [0] to |first and [-1] to |last so we be compatible with LM Studio and llama-cli. With the upgrade of llama-cli to use llama-server, we can revert this. llama-server also didn't like |first, so we fixed it as well.

Also other changes:

(Ongoing process) Will add Ollama model files, so Ollama would function.
Added lot of tool calls in our calibration dataset - makes tool calling better, especially for smaller quants.
A bit more calibration data for GLM models., adding a teeny tiny bit more accurancy overall.

GGUFs which will be receive Quality of Life updates:
https://huggingface.co/unsloth/GLM-4.6-GGUF
https://huggingface.co/unsloth/GLM-4.5-GGUF
https://huggingface.co/unsloth/GLM-4.5-Air-GGUF
https://huggingface.co/unsloth/GLM-4.6V-GGUF
https://huggingface.co/unsloth/GLM-4.6V-Flash-GGUF
https://huggingface.co/unsloth/GLM-4.7-GGUF

danielhanchen pinned discussion Dec 27, 2025

TimothyRoo

Feb 6

•

edited Feb 6

Thank you for revisiting these models, much appreciated!

I was wondering if you can help explain (and maybe recommend) which 4bit quant would be the best for my use case. Mac Studio 512GB -> llama-server -> Roo Code

I've got the Q8_K_XL already downloaded, but that model + the MXFP4 version of Qwen-Coder-Next maxes out my memory.

My ideal is that I can have a good accuracy (and performant) quant of GLM4.7, 4.6V, and a few other models loaded all at the same time.

So with that preamble out of the way, I guess I have 3 questions (all for M3 Ultra - and size of the 4bit is not a consideration):

Which 4 bit is the best for Accuracy?
Which is the best for Speed?
Is there a model that offers that best of both for my Apple Silicon use case?
(Knowing that on your blog, you often say: "We use the UD-Q4_K_XL quant for the best size/accuracy balance")

IQ4_XS
Q4_K_S
IQ4_NL
Q4_0
Q4_1
Q4_K_M
Q4_K_XL
*Lets pretend there is also an MXFP4 so it saves me from asking the same question for another model 😀

TimothyRoo

Feb 6

•

edited Feb 9

@danielhanchen I do hope to get your answer - since commercial LLMs have been useless at answers to this question - ChatGPT, Sonnet, etc.

e.g. asking the same question about quants and MiniMax M2.1 - it says the XL ones are typos.
https://claude.ai/share/438fcc5d-ea95-48e3-9654-5a4ebfa3c3f3

TimothyRoo

Feb 10

@danielhanchen gentle nudge in case you missed my earlier messages 😀

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