Instructions to use EAF-Research/gemma2_2b_educational_code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EAF-Research/gemma2_2b_educational_code with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EAF-Research/gemma2_2b_educational_code", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use EAF-Research/gemma2_2b_educational_code 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 EAF-Research/gemma2_2b_educational_code 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 EAF-Research/gemma2_2b_educational_code to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for EAF-Research/gemma2_2b_educational_code to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="EAF-Research/gemma2_2b_educational_code", max_seq_length=2048, )
- Xet hash:
- c255ff9e9c4c7e1fa5e7e518da211df79c35756f5f689a73f26796321e9e6457
- Size of remote file:
- 166 MB
- SHA256:
- 3b5bc105bd3bfeea14b023e5176c524c5b839f30409c895813340da6dfe8e454
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