Instructions to use ModularityAI/gemma-2b-datascience-it-adapters-raft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModularityAI/gemma-2b-datascience-it-adapters-raft with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ModularityAI/gemma-2b-datascience-it-adapters-raft", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use ModularityAI/gemma-2b-datascience-it-adapters-raft 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 ModularityAI/gemma-2b-datascience-it-adapters-raft 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 ModularityAI/gemma-2b-datascience-it-adapters-raft to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ModularityAI/gemma-2b-datascience-it-adapters-raft to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ModularityAI/gemma-2b-datascience-it-adapters-raft", max_seq_length=2048, )
- Xet hash:
- 50df89e148ba1a119b07500a46bbd261dfc2578096b56e1d039cf47d38fa6da5
- Size of remote file:
- 4.98 kB
- SHA256:
- f0a8025b2cafdd54e28f59c3df86d474016144c905fd537522a153cd17cc3035
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