Instructions to use jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF", filename="GLM-4.5-DRAFT-0.6B-128k-Q4_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0 # Run inference directly in the terminal: llama-cli -hf jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0 # Run inference directly in the terminal: llama-cli -hf jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0
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 jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0
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 jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0
Use Docker
docker model run hf.co/jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0
- LM Studio
- Jan
- Ollama
How to use jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF with Ollama:
ollama run hf.co/jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0
- Unsloth Studio
How to use jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-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 jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-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 jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF with Docker Model Runner:
docker model run hf.co/jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0
- Lemonade
How to use jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jukofyork/GLM-4.5-DRAFT-0.6B-v3.0-GGUF:Q4_0
Run and chat with the model
lemonade run user.GLM-4.5-DRAFT-0.6B-v3.0-GGUF-Q4_0
List all available models
lemonade list
Hello, I want to know if the draft model will reduce the model generation quality?
In my understanding, the acceptance of the draft model depends on the similarity of the probability distribution, generally set to 0.8~0.95, will this not lead to a decline in model performance?
In my understanding, the acceptance of the draft model depends on the similarity of the probability distribution, generally set to 0.8~0.95, will this not lead to a decline in model performance?
It won't make the main model choose different tokens, but it can end up worse performance in terms of tokens/s if the draft model doesn't predict the main model very well. It also requires you to use top_k = 1 or temperature = 0 to work properly.
I did read in another thread that this particular draft doesn't work that well for the GLM-4.5-Air model; likely because it only has ~10B active parameters, and the potential gain in tokens/s relies on the draft model being much smaller than the main model (the full GLM-4.5 model has 30B+ active parameters, so likely to work much better for it).