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:
- 8d8be77c163f81676c4c872c156a5d3714fa8b95c4eab9ec1564d97a4bdac767
- Size of remote file:
- 4.99 GB
- SHA256:
- fddb4ad10234e2bb6232db0c030fcf546cc29c631229b7f260463e864031272c
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