Instructions to use bartowski/THUDM_GLM-4-32B-0414-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/THUDM_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="bartowski/THUDM_GLM-4-32B-0414-GGUF", filename="THUDM_GLM-4-32B-0414-IQ2_M.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 bartowski/THUDM_GLM-4-32B-0414-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 bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
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 bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
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 bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/THUDM_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 "bartowski/THUDM_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": "bartowski/THUDM_GLM-4-32B-0414-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
- Ollama
How to use bartowski/THUDM_GLM-4-32B-0414-GGUF with Ollama:
ollama run hf.co/bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
- Unsloth Studio
How to use bartowski/THUDM_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 bartowski/THUDM_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 bartowski/THUDM_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 bartowski/THUDM_GLM-4-32B-0414-GGUF to start chatting
- Pi
How to use bartowski/THUDM_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 serve -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
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": "bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/THUDM_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 serve -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
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 bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use bartowski/THUDM_GLM-4-32B-0414-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
- Lemonade
How to use bartowski/THUDM_GLM-4-32B-0414-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.THUDM_GLM-4-32B-0414-GGUF-Q4_K_M
List all available models
lemonade list
Broken results
Tried Q4_K_L on llama.cpp b5133 server and it's unusable due to extreme repetition issues. Something seems clearly broken.
I can confirm that I'm experiencing this as well on the IQ4_XS quant. I tried the Q_4_K_M quant it was broken as well.
This has been opened as an issue for llama.cpp on github:
Shoot.. hopefully is a small fix, ideally not the quant! But if it is I'll remake it promptly!
Just as a note, see https://www.reddit.com/r/LocalLLaMA/comments/1jzn9wj/comment/mn7iv7f
By using these arguments: --flash-attn -ctk q4_0 -ctv q4_0 --ctx-size 16384 --override-kv tokenizer.ggml.eos_token_id=int:151336 --override-kv glm4.rope.dimension_count=int:64 --jinja I was able to make the IQ4_XS quant work well for me on the lastest build of llama.cpp
Just as a note, see https://www.reddit.com/r/LocalLLaMA/comments/1jzn9wj/comment/mn7iv7f
By using these arguments:
--flash-attn -ctk q4_0 -ctv q4_0 --ctx-size 16384 --override-kv tokenizer.ggml.eos_token_id=int:151336 --override-kv glm4.rope.dimension_count=int:64 --jinjaI was able to make the IQ4_XS quant work well for me on the lastest build of llama.cpp
Thanks for your info, but why did you set kv cache to q4? Won't the default fp16 get better accuracy?
Yes, you can use fp16 for better accuracy, I use q4 because I have 16GB VRAM and want to fit as much of the model as possible onto the GPU
Shoot.. hopefully is a small fix, ideally not the quant! But if it is I'll remake it promptly!
Hi, would like to notify that pull 13021 has been merged and official release b5173 contains it. Quant re-creation is required.
Yup, I've started to remake now