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", dtype="auto")

llama-cpp-python

How to use unsloth/GLM-4.7-Flash-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-Flash-GGUF",
	filename="BF16/GLM-4.7-Flash-BF16-00001-of-00002.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-Flash-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-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-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

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 new

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

Pi new

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-server -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

Hermes Agent new

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-server -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

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

Issue: GLM-4.7-Flash Q6_K - Completely unusable output (looping)

by gannima - opened Jan 20

Discussion

gannima

Jan 20

Environment

Model: unsloth/GLM-4.7-Flash-GGUF (Q6_K, 23GB)
llama.cpp: build 7779 (commit 6df686bee)
Hardware: RTX 4090, 128 GB RAM

Issue

Model produces completely unusable output regardless of input:

Test	Input	Output
Chinese	"你好"	Version changelog
Chinese	"请介绍..."	Git repo structure
English	"hi"	`S` repeated 200x
English	"What is 2+2?"	Prompt analysis
Coding	"fibonacci..."	Git commit info
Math	"Solve x^2-4=0"	`S` repeated 200x

test output

Test 1: Chinese - "你好"

1.0.0 (2024-07-19)
- Initial release.
1.1.0 (2024-08-01)
- Add --no-verify flag to`
- Add --verify flag (default)
- Add --force flag
- Add --dry-run
- Add --verbose flag
- Add --debug flag
  ...

Test 2: Chinese - "请介绍一下你自己"

j: Simplified the logic for handling the --model argument. Now it's just a simple map of model names to their respective system prompts.
##Repo Structure: └── .
├── .github
S3
│ └── ...
├── .vscode
│ └── ...
├── src
│ ├── main
│ │ ├── java
│ │ │ └── com
│ │ │ └── github
│ │ │ └── catcode
│ │ │ └── openai4j
│ │ │ └── cli
│ │ │ └── Main.java

Test 3: English - "hi"

j
S
S
S
S
S
S
S
S
S
S
S
S
S
S
...
(repeated 200 times)

Test 4: English - "What is 2+2?"

j #1. Analyze the Request: * Input:#1. **` (This looks like a list item or a header, but the content following it is missing).
* Context: The user provided a very short prompt. It's possible they want me to complete the sentence, they pasted a snippet by mistake, or they are asking for a specific type of response based on a previous (unseen) context.
* Hypothesis 1 (Completion): The user wants me to continue the list or the sentence.

Test 5: Coding - "Write fibonacci..."

j: S3ClientBuilder#build returns a client that uses the default region provider chain (#447)
##Repo Structure: └── .
├── .github
S3ClientBuilderTest.java
├── README.md
├── gradle.properties
└── gradlew.bat
Additional changes:

Updated S3ClientBuilder to return a client that uses the DefaultRegionProviderChain instead of DefaultS3RegionProvider.

Test 6: Math - "Solve x^2-4=0"

j
S
S
S
S
S
S
S
...
(repeated 200 times)

Parameters Used

--temp 0.2 --top-p 0.95 --top-k 50 --min-p 0.01 --dry-multiplier 1.1 --fit on

Additional Info

- ✅ Verified --dry-multiplier exists in help
- ✅ Model loads without errors
- ✅ GPU is being used
- ❌ dry_multiplier has ZERO effect
- ❌ UD-Q4_K_XL has same issue
- ❌ Q6_K has same issue

Suggested Solutions

1. Test with higher dry-multiplier (2.0, 5.0, 10.0)?
2. Verify GGUF conversion for deepseek2 architecture?
3. Check if dry-multiplier works for deepseek2?

Impact: Model is 100% unusable. Can't use for any task. 🙏

Ref: https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF/discussions/1

gannima changed discussion status to closed Jan 20

shimmyshimmer

Unsloth AI org Jan 20

Hello @gannima did you manage to solve the issue?

gannima

Jan 20

Hello @gannima did you manage to solve the issue?

yes, the solution is on the https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF/discussions/1 , i just post my test output and fixing way in the latest posting

gannima changed discussion status to open Jan 20

gannima

Jan 20

@shimmyshimmer

danielhanchen

Unsloth AI org Jan 20

@shimmyshimmer

Thank you I got it we'll investigate

amaycom

Jan 20

Global GLM 4.7 flash problem from all - go to loop and writes crazy things . This is the first time I've seen a model glitch like this, regardless of quantization. LM studio ( with new glm 4.7 flash update )

danielhanchen

Unsloth AI org Jan 21

Jan 21 UPDATE: llama.cpp has fixed a bug which caused the model to loop and produce poor outputs. We have reconverted and reuploaded the model so outputs should be much much better now.

You can now use Z.ai's recommended parameters and get great results:

For general use-case: --temp 1.0 --top-p 0.95
For tool-calling: --temp 0.7 --top-p 1.0

If you can test and let us know if you get better results? Thanks so much!

CC: @amaycom @gannima

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment