Instructions to use QuantPasture/GLM-4.7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantPasture/GLM-4.7-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantPasture/GLM-4.7-GGUF", filename="GLM-4.7-IQ2_M/GLM-4.7-IQ2_M-00001-of-00004.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantPasture/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 QuantPasture/GLM-4.7-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantPasture/GLM-4.7-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantPasture/GLM-4.7-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantPasture/GLM-4.7-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 QuantPasture/GLM-4.7-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantPasture/GLM-4.7-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 QuantPasture/GLM-4.7-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantPasture/GLM-4.7-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantPasture/GLM-4.7-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantPasture/GLM-4.7-GGUF with Ollama:
ollama run hf.co/QuantPasture/GLM-4.7-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantPasture/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 QuantPasture/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 QuantPasture/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 QuantPasture/GLM-4.7-GGUF to start chatting
- Pi
How to use QuantPasture/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 QuantPasture/GLM-4.7-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": "QuantPasture/GLM-4.7-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use QuantPasture/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 QuantPasture/GLM-4.7-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 QuantPasture/GLM-4.7-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use QuantPasture/GLM-4.7-GGUF with Docker Model Runner:
docker model run hf.co/QuantPasture/GLM-4.7-GGUF:Q4_K_M
- Lemonade
How to use QuantPasture/GLM-4.7-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantPasture/GLM-4.7-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.GLM-4.7-GGUF-Q4_K_M
List all available models
lemonade list
IQ2_M performs surprisingly nice
Use case: fooling around with writing / RP.
- When thinking is disabled, it's too focused in the moment (addressing most recent input, not catching the nuances of what happened before).
? Sometimes (rarely) it mixes up words / concepts: perplexity is noticeable, although treatable with re-generations.
+ Enabling thinking fights the 1st issue and generally makes the model get its shit together, even recalling some unexpected details (i.e. character lore not present in profile or lorebook).
Overall, IQ2_M is a little short of being impressive.
Update.
So, about that part -- Sometimes (rarely) it mixes up words / concepts -- either I'm getting crazy or raising the temperature eliminates this issue completely.
Anyway, scoring it as truly impressive now, at least in RP. I still wish there was an in-between variant (like 3bpw, no larger than 130 - 135GB), since IQ4_XS is just too big.
For the ~3bpw range, I'd suggest @ubergarm 's quants: https://huggingface.co/ubergarm/GLM-4.7-GGUF
He's got an smol-IQ2_KS 99.237 GiB (2.379 BPW) and IQ2_KL 129.279 GiB (3.099 BPW) that might be suitable.