Instructions to use WilburDev/gemma3-1b-it-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use WilburDev/gemma3-1b-it-int4 with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM) # and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter). # For platform-specific integration guides, please refer to the official developer website: # https://ai.google.dev/edge/litert-lm # To try LiteRT-LM, the easiest way is to use our CLI tool. # 1. Install the LiteRT-LM CLI tool: pip install -U litert-lm # 2. Download and run this model locally: # See: https://ai.google.dev/edge/litert-lm/cli # A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run # (or pass its name right after the repo id). litert-lm run \ --from-huggingface-repo=WilburDev/gemma3-1b-it-int4 \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from WilburDev/gemma3-1b-it-int4: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/WilburDev/gemma3-1b-it-int4/resolve/main/tokenizer.model
- Command line
-
hf download hf://WilburDev/gemma3-1b-it-int4/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/WilburDev/gemma3-1b-it-int4/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- a81fa217b67ef4a1992b48a47651c27a2a19df419eafd1aad9c0bbd5ff49bde3
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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