Instructions to use 1024m/CHIPSAL-C-GEMMA2-001-27B-A32bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1024m/CHIPSAL-C-GEMMA2-001-27B-A32bit with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("1024m/CHIPSAL-C-GEMMA2-001-27B-A32bit", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use 1024m/CHIPSAL-C-GEMMA2-001-27B-A32bit 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 1024m/CHIPSAL-C-GEMMA2-001-27B-A32bit 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 1024m/CHIPSAL-C-GEMMA2-001-27B-A32bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for 1024m/CHIPSAL-C-GEMMA2-001-27B-A32bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="1024m/CHIPSAL-C-GEMMA2-001-27B-A32bit", max_seq_length=2048, )
- Xet hash:
- 7f515dd3e4c433e748d30137a0e27d3ac12be887b618d53c1b7fae47d7783d6b
- Size of remote file:
- 457 MB
- SHA256:
- 5ca0bce9991eb041da3dee10e2f6a70309de0dbe6c181dc68cd291f60d124bd1
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