Instructions to use 4ntoine/LocoOperator-4B-LiteRTLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use 4ntoine/LocoOperator-4B-LiteRTLM 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=4ntoine/LocoOperator-4B-LiteRTLM \ model.litertlm \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
| base_model: | |
| - LocoreMind/LocoOperator-4B | |
| tags: | |
| - litert-lm | |
| - litertlm | |
| The model is converted from the original LocoreMind/LocoOperator-4B using: | |
| ``` | |
| litert-torch export_hf \ | |
| --model=LocoreMind/LocoOperator-4B \ | |
| --output_dir="./dynamic_wi8_afp32" \ | |
| --quantization_recipe="dynamic_wi8_afp32" \ | |
| --bundle_litert_lm=true | |
| ``` |