Instructions to use 1024m/EMNLP-NLLP-NLI-GEMMA2-27B-LoRA-004 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1024m/EMNLP-NLLP-NLI-GEMMA2-27B-LoRA-004 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("1024m/EMNLP-NLLP-NLI-GEMMA2-27B-LoRA-004", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use 1024m/EMNLP-NLLP-NLI-GEMMA2-27B-LoRA-004 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/EMNLP-NLLP-NLI-GEMMA2-27B-LoRA-004 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/EMNLP-NLLP-NLI-GEMMA2-27B-LoRA-004 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for 1024m/EMNLP-NLLP-NLI-GEMMA2-27B-LoRA-004 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="1024m/EMNLP-NLLP-NLI-GEMMA2-27B-LoRA-004", max_seq_length=2048, )
- Xet hash:
- c6061530ed2b7a5aaf0d0c566e691426e0478d9b30aeef850a189b84754cefce
- Size of remote file:
- 457 MB
- SHA256:
- 2b4a02b8fc99f94c7e2fff397b3f7455a14eedad7e81c1235412fba35aa860ac
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