Instructions to use FastFlowLM/Gemma4-E4B-IT-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FastFlowLM/Gemma4-E4B-IT-NPU2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("FastFlowLM/Gemma4-E4B-IT-NPU2") model = AutoModelForMultimodalLM.from_pretrained("FastFlowLM/Gemma4-E4B-IT-NPU2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from FastFlowLM/Gemma4-E4B-IT-NPU2: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
-
https://huggingface.co/FastFlowLM/Gemma4-E4B-IT-NPU2/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://FastFlowLM/Gemma4-E4B-IT-NPU2/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/FastFlowLM/Gemma4-E4B-IT-NPU2/resolve/main/tokenizer_config.json
2.14 kB
- Xet hash:
- c90c8ccb1f65ddd48c2f692f0d79ca739beda1ebf247754476a7085f24caece8
- Size of remote file:
- 2.14 kB
- SHA256:
- e592c513cc86edf9962b9a4072f034c20f3c1facfbb2f1c60de8391774c4786e
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