Image-Text-to-Text
LiteRT-LM
LiteRT
LiteRT
litertlm
on-device
vision-language
object-detection
referring-expression-comprehension
object-counting
point-grounding
ocr
Instructions to use litert-community/WeVisOne-VL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/WeVisOne-VL-2B 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=litert-community/WeVisOne-VL-2B \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/WeVisOne-VL-2B 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
Commit History
Today
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README: Masks — the cutout/ folder and the Android cut-out sample (LiteRT-Models wevisone/)
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cutout/: the graphs, token table and tokenizer that the Android cut-out sample loads with LiteRT CompiledModel (vision encoder, adapter, int8 and fp16 language models, matte decoder)
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litertlm_manifest.json: parameters_b 2.44 (three significant digits)
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WeVisOne-VL-2B for LiteRT-LM: float16 fast_vlm bundle at 512 x 512, card, manifest
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initial commit
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