Instructions to use google/gemma-4-E2B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-E2B-it with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/gemma-4-E2B-it") model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-E2B-it") - Notebooks
- Google Colab
- Kaggle
Add IFStruct v1.0 evaluation result
#39
by SaylorTwift HF Staff - opened
.eval_results/google-gemma-4-E2B-it.yaml
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# IFStruct v1.0 evaluation result for google/gemma-4-E2B-it
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# Source blog: https://www.liquid.ai/blog/ifstruct-v1.0
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# Benchmark dataset (with eval.yaml): https://huggingface.co/datasets/LiquidAI/ifstruct-v1.0
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# GitHub: https://github.com/Liquid4All/ifstruct
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- dataset:
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id: LiquidAI/ifstruct-v1.0
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task_id: ifstruct_v1
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value: 64.85
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date: "2026-06-30"
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source:
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url: https://www.liquid.ai/blog/ifstruct-v1.0
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name: "Liquid AI — IFStruct v1.0 blog (gemma-4-E2B-it)"
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