Instructions to use JosineyJr/generate-conventional-commit-messages with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use JosineyJr/generate-conventional-commit-messages 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 JosineyJr/generate-conventional-commit-messages 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 JosineyJr/generate-conventional-commit-messages to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for JosineyJr/generate-conventional-commit-messages to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="JosineyJr/generate-conventional-commit-messages", max_seq_length=2048, )
Update README.md
Browse files
README.md
CHANGED
|
@@ -6,6 +6,7 @@ library_name: unsloth
|
|
| 6 |
tags:
|
| 7 |
- code
|
| 8 |
pipeline_tag: text2text-generation
|
|
|
|
| 9 |
---
|
| 10 |
# About the project
|
| 11 |
CommitWizard is a project that uses pre-trained language models to help automate the generation of commit messages based on code changes. It employs 4-bit quantization to optimize memory usage while maintaining model efficiency and accuracy.
|
|
|
|
| 6 |
tags:
|
| 7 |
- code
|
| 8 |
pipeline_tag: text2text-generation
|
| 9 |
+
base_model: meta-llama/Meta-Llama-Guard-2-8B
|
| 10 |
---
|
| 11 |
# About the project
|
| 12 |
CommitWizard is a project that uses pre-trained language models to help automate the generation of commit messages based on code changes. It employs 4-bit quantization to optimize memory usage while maintaining model efficiency and accuracy.
|