Instructions to use unsloth/GLM-4.7-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Ollama Modelfile to fix tool calling within GLM-4.7-Flash GGUF
pinned❤️ 5
4
#23 opened 3 months ago
by
CesarR70
DISABLE REPEAT PENALTY or set = 1.0
pinned👍 7
7
#13 opened 4 months ago
by
danielhanchen
Jan 21: All GLM-4.7-Flash quants reuploaded - much better outputs!
pinned🔥❤️ 7
29
#10 opened 4 months ago
by
danielhanchen
Liked bartowski model performance on my low-end videocard better
#33 opened 2 months ago
by
d9k
Install & run this model easily using llmpm
#32 opened 2 months ago
by
sarthak-saxena
Model degrades after ~64000 tokens
#30 opened 3 months ago
by
ceoofcapybaras
Localai model load fails w/GLM-4.7-Flash-IQ4_XS.gguf
#29 opened 3 months ago
by
SplashMatic
New uploads Feb 12th?
👍 1
2
#27 opened 3 months ago
by
jmagder
Still looping and getting stuck thinking after new update
4
#26 opened 3 months ago
by
xantrk
Tool calls destroy good reasoning + how to fix it
#25 opened 3 months ago
by
ffewfqefwsefwfe
GLM-4.7-Flash-Q4_K_S - in LMStudio 4.0.2 = garbage
#24 opened 3 months ago
by
K-Tul-Khu
HumanEval Benchmarks: GLM-4.7-Flash (UD Q4/Q5)
#22 opened 4 months ago
by
k5uu5
NEW: LLama.cpp: Using `ngram-mod` to Get 2x Speed Boost on Long-Chats/Agent!
1
#20 opened 4 months ago
by
PussyHut
Getting 110 tokens/sec on my RTX 3090, 24 GB VRam
👍🔥 4
#19 opened 4 months ago
by
bikkikumarsha
New flag: kv-unified. Performance report: 90 t/s on RTX 4090D 48GB
🔥 2
4
#18 opened 4 months ago
by
SlavikF
Anyone tried to challenge the model?
3
#16 opened 4 months ago
by
urtuuuu
Ollama Support
5
#15 opened 4 months ago
by
yqchen-sci
Infinite thinking (after </thinking> tag with updated quants
5
#14 opened 4 months ago
by
CoruNethron
new ggufs working as intended
🤗🔥 1
1
#12 opened 4 months ago
by
realrebelai
GGUF model with architecture deepseek2 is not supported yet.
2
#11 opened 4 months ago
by
T1-Faker1
How to run properly on KoboldCPP?
2
#8 opened 4 months ago
by deleted
Kind of broken.
14
#7 opened 4 months ago
by
ItzPingCat
Disable thinking?
4
#4 opened 4 months ago
by
JankyMudFart
Issue: GLM-4.7-Flash Q6_K - Completely unusable output (looping)
6
#3 opened 4 months ago
by
gannima
Jan 21: GGUFs all UPDATED!!!
🤗❤️ 5
40
#1 opened 4 months ago
by
danielhanchen