Instructions to use engindemir/gemma2_9b_tr-dparsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use engindemir/gemma2_9b_tr-dparsing with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("engindemir/gemma2_9b_tr-dparsing", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use engindemir/gemma2_9b_tr-dparsing 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 engindemir/gemma2_9b_tr-dparsing 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 engindemir/gemma2_9b_tr-dparsing to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for engindemir/gemma2_9b_tr-dparsing to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="engindemir/gemma2_9b_tr-dparsing", max_seq_length=2048, )
- Xet hash:
- afa78551f6fdaff5671645d2394d4478e28255203cd7456448b0da82ae0a3ba8
- Size of remote file:
- 34.4 MB
- SHA256:
- 5f7eee611703c5ce5d1eee32d9cdcfe465647b8aff0c1dfb3bed7ad7dbb05060
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