Instructions to use google/gemma-4-E4B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-E4B-it with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/gemma-4-E4B-it") model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-E4B-it") - Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
Is there any quantization process done to this google/gemma-4-E4B-it model checkpoint?
#41
by yliu0777 - opened
Hi team,
I want to check and confirm if the model is a original bf16 model, or if there is any quantization processing done to it such as GPTQ weight quantization or quantization-aware training.
Hi @yliu0777
The google/gemma-4-E4B-it repository contains the original, unquantized instruction-tuned model checkpoint.If you are looking for official Quantization-Aware Training (QAT) variants, Google has provided dedicated checkpoints for those instead. You can find the specific QAT models here .
- google/gemma-4-E4B-it-qat-w4a16-ct
- google/gemma-4-E4B-it-qat-q4_0-gguf
- google/gemma-4-E4B-it-qat-q4_0-unquantized
For the complete list, check out the official Hugging Face collection: https://huggingface.co/collections/google/gemma-4-qat-q4-0
Thanks