Instructions to use FastFlowLM/Gemma4-E2B-IT-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FastFlowLM/Gemma4-E2B-IT-NPU2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("FastFlowLM/Gemma4-E2B-IT-NPU2") model = AutoModelForMultimodalLM.from_pretrained("FastFlowLM/Gemma4-E2B-IT-NPU2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.q4nx from FastFlowLM/Gemma4-E2B-IT-NPU2: direct link, hf CLI and curl.
- Browser
- Download file 4.67 GB
-
https://huggingface.co/FastFlowLM/Gemma4-E2B-IT-NPU2/resolve/main/model.q4nx
- Command line
-
hf download hf://FastFlowLM/Gemma4-E2B-IT-NPU2/model.q4nx
-
curl -L -o model.q4nx https://huggingface.co/FastFlowLM/Gemma4-E2B-IT-NPU2/resolve/main/model.q4nx
4.67 GB
- Xet hash:
- bb04ca22fc4ff8ba66da6df265edb2e786d372819062ae864a07eb2ec3bcac01
- Size of remote file:
- 4.67 GB
- SHA256:
- aaca4bf5592804f0752c9f5fb7b4cce2f517fd869ef1d8181b209c8b6dbf2a87
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