Summarization
Transformers
Safetensors
Korean
English
gemma2
text-generation
text-generation-inference
Instructions to use dwhouse/gemma-2-2b-it-research-in-a-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwhouse/gemma-2-2b-it-research-in-a-flash with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="dwhouse/gemma-2-2b-it-research-in-a-flash")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("dwhouse/gemma-2-2b-it-research-in-a-flash") model = AutoModelForMultimodalLM.from_pretrained("dwhouse/gemma-2-2b-it-research-in-a-flash") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f205310531020c56f6bd11a5e4cd216d69f2c3f5231c814d217e44c45152283
- Size of remote file:
- 241 MB
- SHA256:
- d288b3c3ea03b3f052591b8720aa9ca1cf5d03080826daca5e3871223b9a16ea
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