Instructions to use unsloth/GLM-4-32B-0414-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/GLM-4-32B-0414-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/GLM-4-32B-0414-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/GLM-4-32B-0414-GGUF") model = AutoModelForMultimodalLM.from_pretrained("unsloth/GLM-4-32B-0414-GGUF") - llama-cpp-python
How to use unsloth/GLM-4-32B-0414-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/GLM-4-32B-0414-GGUF", filename="BF16/GLM-4-32B-0414-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 Settings
- llama.cpp
How to use unsloth/GLM-4-32B-0414-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-4-32B-0414-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/GLM-4-32B-0414-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-4-32B-0414-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/GLM-4-32B-0414-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-4-32B-0414-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/GLM-4-32B-0414-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-4-32B-0414-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/GLM-4-32B-0414-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/GLM-4-32B-0414-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/GLM-4-32B-0414-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/GLM-4-32B-0414-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-4-32B-0414-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/GLM-4-32B-0414-GGUF:UD-Q4_K_XL
- SGLang
How to use unsloth/GLM-4-32B-0414-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-4-32B-0414-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-4-32B-0414-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-4-32B-0414-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-4-32B-0414-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use unsloth/GLM-4-32B-0414-GGUF with Ollama:
ollama run hf.co/unsloth/GLM-4-32B-0414-GGUF:UD-Q4_K_XL
- Unsloth Studio
How to use unsloth/GLM-4-32B-0414-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-4-32B-0414-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-4-32B-0414-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-4-32B-0414-GGUF to start chatting
- Pi
How to use unsloth/GLM-4-32B-0414-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-4-32B-0414-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-4-32B-0414-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/GLM-4-32B-0414-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-4-32B-0414-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-4-32B-0414-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use unsloth/GLM-4-32B-0414-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/GLM-4-32B-0414-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/GLM-4-32B-0414-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/GLM-4-32B-0414-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.GLM-4-32B-0414-GGUF-UD-Q4_K_XL
List all available models
lemonade list
Final GLM-4-32B-0414-GGUF fixes!
Hey guys we reuploaded the quants with more fixes. Hopefully it's the final fixes! Please use --jinja
If you don't use
--jinja, which applies the chat template, then you will get gibberish!
Results should be much better so let us know!:
./llama.cpp/llama-cli -hf unsloth/GLM-4-32B-0414-GGUF:Q4_K_XL -ngl 99 --jinja
Thank you!
what does --jinja do ?
--jinja applies the chat template - if you don't, you will get gibberish
--jinja applies the chat template - if you don't, you will get gibberish
Is it different for other models, like qwen? I never used --jinja
Yes always use --jinja! LM Studio turns it on by default. I think llama.cpp also does it by default now, but unsure
do you know if koboldcpp uses it by default? you can't use the argument in kobold ("unrecognized arguments: --jinja"). However, it derives from llamacpp
KoboldCpp has its own system, but it is compatible out of the box with this model. When loading it should mention its detected as GLM4.
Do note, we had to do a couple of changes in the ways EOS was handled as well as fixing tokenization multiple versions ago. If this model works poorly for you make sure to update to the latest KoboldCpp.
Likewise I never got confirmation llamacpp was 100% stable with this model on Vulkan, so Vulkan may behave odd.
Theres some complexities with this models way of doing BOS in general, that part is handled internally by KoboldCpp when GLM is detected.
Update: I remembered the why of it all, this model has that odd [gMASK] thing in the jinja. KoboldCpp has its own GLM4 code handle this automatically in the background. Even if you don't use it for instruct it should automatically just work.
do you know if koboldcpp uses it by default? you can't use the argument in kobold ("unrecognized arguments: --jinja"). However, it derives from llamacpp
@doc-acula see above ^