Instructions to use mlboydaisuke/Falcon3-3B-Instruct-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use mlboydaisuke/Falcon3-3B-Instruct-LiteRT 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 litert-lm # 2. Download and run this model locally: # See: https://ai.google.dev/edge/litert-lm/cli litert-lm run \ --from-huggingface-repo=mlboydaisuke/Falcon3-3B-Instruct-LiteRT \ model.litertlm \ --prompt="Write me a poem"
- LiteRT
How to use mlboydaisuke/Falcon3-3B-Instruct-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Rebuild via stock litert-torch (recipe.json, no custom code); same blockwise-128 int4, GSM8K 77%
1e553b4 verified - Xet hash:
- 492aab63195bbf295f062995888019f756e7c64ed49dbf3cb4b314e22d544f03
- Size of remote file:
- 1.87 GB
- SHA256:
- 1a09ab6a560a3b0c23db1e299bf33975e669a47bc1ece24d6601114ff4cfd529
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