Instructions to use litert-community/Falcon-H1-Tiny-R-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/Falcon-H1-Tiny-R-0.6B 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/Falcon-H1-Tiny-R-0.6B \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/Falcon-H1-Tiny-R-0.6B 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
4 days ago
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litertlm_manifest.json: known_issues now carry the runtime notes added to the card on 2026-10-12/13 — one Engine per conversation (LiteRT-LM #3165)
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5 days ago
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Card: one Engine per conversation on LiteRT-LM (own report #3165), measured on litert-lm 0.18.0
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10 days ago
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manifest 0.1.3: capabilities.thinking.control = model (what the bundle's template does about thinking, derived from the template inside the bundle)
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26 days ago
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Chat template: accept the 0.18 content-parts form (string form unchanged); weights, tokenizer and executor metadata byte-identical
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Card: measured device / browser results (edge-compat, 2026-09)
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Card: compatibility line (edge-compat, 2026-09)
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manifest: Android measured rows (Galaxy S26 GPU + same-device CPU control; Pixel 8a where run) and an Android recommendation where the evidence supports one
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manifest: Android measured rows from the Galaxy S26 (GPU + same-device CPU control) and the Pixel 8a, with an Android recommendation where the pair supports one
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Card: base_model_relation: quantized (list under the base model's Quantizations)
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litertlm_manifest.json: manifest 0.1.2 — public build (evidence pointers stripped), channels mirrored
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litertlm_manifest.json (validated; measured rows with primary-log evidence)
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Falcon-H1-Tiny-R-0.6B int8 (FC-only dynamic; externalized float embedder; fp32act; thought channel)
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Falcon-H1-Tiny-R-0.6B LiteRT-LM card
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initial commit
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