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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# 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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using bitsandbytes NF4 (4-bit). Tested on RTX 5090 (Blackwell, sm_120).
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# Gemma 4 E2B it — 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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using bitsandbytes NF4 (4-bit). Tested on RTX 5090 (Blackwell, sm_120).
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