Feature Extraction
Transformers
Safetensors
English
gemma
bnb-my-repo
unsloth
bnb
4-bit precision
bitsandbytes
Instructions to use lainlives/codegemma-7b-it-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lainlives/codegemma-7b-it-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lainlives/codegemma-7b-it-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lainlives/codegemma-7b-it-bnb-4bit") model = AutoModel.from_pretrained("lainlives/codegemma-7b-it-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use lainlives/codegemma-7b-it-bnb-4bit 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 lainlives/codegemma-7b-it-bnb-4bit 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 lainlives/codegemma-7b-it-bnb-4bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lainlives/codegemma-7b-it-bnb-4bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="lainlives/codegemma-7b-it-bnb-4bit", max_seq_length=2048, )
- Xet hash:
- 6a792613e55e161cab7c324701ec8b4e17cb3f0eaf6c90192449880621362ff3
- Size of remote file:
- 5.57 GB
- SHA256:
- cc47c52667ff3390e0310e71cc0950f587816279c7d10279127595bd50bf7980
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