Any-to-Any
Transformers
Safetensors
English
gemma4
image-text-to-text
gemma
gemma-4
quantized
int4
bitsandbytes
4-bit precision
Instructions to use dahus/gemma-4-e2b-it-q4-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dahus/gemma-4-e2b-it-q4-nf4 with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("dahus/gemma-4-e2b-it-q4-nf4") model = AutoModelForMultimodalLM.from_pretrained("dahus/gemma-4-e2b-it-q4-nf4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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base_model: google/gemma-4-e2b-it
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pipeline_tag: any-to-any
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library_name: transformers
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tags:
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- gemma
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- gemma4
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- quantized
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- int8
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- bitsandbytes
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license:
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---
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# Gemma 4 E2B — NF4 Quantization (bitsandbytes)
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Quantized version of [google/gemma-4-e2b-it](https://huggingface.co/google/gemma-4-e2b-it)
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language:
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- en
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base_model: google/gemma-4-e2b-it
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pipeline_tag: any-to-any
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library_name: transformers
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tags:
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- gemma
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- gemma4
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- gemma-4
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- quantized
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- int8
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- bitsandbytes
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license: apache-2.0
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---
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# Gemma 4 E2B — NF4 Quantization (bitsandbytes)
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Quantized version of [google/gemma-4-e2b-it](https://huggingface.co/google/gemma-4-e2b-it)
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