Image-Text-to-Text
LiteRT-LM
LiteRT
litertlm
on-device
edge
ocr
document-parsing
markdown
tables
formulas
vlm
hybrid
gated-deltanet
qwen3_5
Instructions to use mlboydaisuke/OvisOCR2-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use mlboydaisuke/OvisOCR2-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 -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=mlboydaisuke/OvisOCR2-LiteRT \ --prompt="Write me a poem"
- LiteRT
How to use mlboydaisuke/OvisOCR2-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
Add a known runtime issue: the stop token with a zero embedding from the second turn
Browse files
README.md
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@@ -129,3 +129,7 @@ picture-region `<img>` tag — exactly its document convention.
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Conversion scripts and a step-by-step reproduction:
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[hf-to-litertlm](https://github.com/john-rocky/hf-to-litertlm) (`qwen35vl_work/`).
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Conversion scripts and a step-by-step reproduction:
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[hf-to-litertlm](https://github.com/john-rocky/hf-to-litertlm) (`qwen35vl_work/`).
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## Known runtime issues
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From the second turn of a conversation on, LiteRT-LM puts the previous answer's stop token into the KV cache with a zero embedding, because this file holds the embedding table in its own section and the decoder takes embeddings, so later answers in a conversation can differ from the answer the model gives to the same messages in one prompt (read in the source of LiteRT-LM 0.16.0 to 0.18.1; measured with 0.18.1 on WeVisOne-VL-2B and Qwen2.5-Coder-1.5B-Instruct, not on this file). One question per conversation avoids it, as does a new conversation that starts with the earlier messages and the new question.
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