Instructions to use Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-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 Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-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 Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF", max_seq_length=2048, )
Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF
This model was converted to GGUF format from Tavernari/git-commit-message-splitter-Qwen3-4B using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF --hf-file git-commit-message-splitter-qwen3-4b-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF --hf-file git-commit-message-splitter-qwen3-4b-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF --hf-file git-commit-message-splitter-qwen3-4b-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Tavernari/git-commit-message-splitter-Qwen3-4B-Q4_K_M-GGUF --hf-file git-commit-message-splitter-qwen3-4b-q4_k_m.gguf -c 2048
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