Instructions to use litert-community/Falcon-H1-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/Falcon-H1-3B-Instruct 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-H1-3B-Instruct \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/Falcon-H1-3B-Instruct 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
9 days ago
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Add a known runtime issue: a preface with tools and no messages fails to render the chat template
Verified signatureAdds the lines under Known runtime issues; nothing else in the card changes.8f51599
21 days ago
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Card: tool calls on litert-lm 0.18.0 — re-packed 2026-10-14 (llm_model_type qwen2p5, tool-result rendering and a JSON tool-call example in the chat template; metadata only); GPU executor defaults note
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litertlm_manifest.json: Falcon-H1-3B-Instruct_int8.litertlm re-packed 2026-10-14 (llm_model_type qwen2p5, tool-result rendering and a JSON tool-call example in the chat template): new sha256 and LlmMetadata section size, the tool-call known_issues sentence and the GPU executor defaults sentence
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Re-pack Falcon-H1-3B-Instruct_int8.litertlm: llm_model_type qwen2p5, tool-result rendering and a JSON tool-call example in the chat template (metadata only; tool calls parse and tool results reach the model on litert-lm 0.18.0)
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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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22 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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27 days ago
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manifest 0.1.3: capabilities.thinking.control = never (what the bundle's template does about thinking, derived from the template inside the bundle)
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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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Add litertlm_manifest.json (deployment manifest 0.1.2: variant selection, verified backends, Android/Apple recommendations, measured rows with conditions)
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Card: base_model_relation: quantized (list under the base model's Quantizations)
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Add measured Raspberry Pi 5 (CPU) section
Verified signatureNumbers from the 2026-09 Raspberry Pi 5 LLM sweep: litert-lm benchmark 0.16.1, CPU backend, 4 threads, 256 prefill + 256 decode tokens, compile cache disabled, 3 invocations per file with cooldown, thermal throttle state verified 0x0 on every run, and a real-generation gate on the same backend before any number was recorded. Median + spread, never single-shot.8e47d0c
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Add measured Galaxy S26 GPU backend section
Verified signatureFunctional verdict from the S26 GPU gate. Primary log: litertlm-convert/community_accel_work/s4_gpu_gate/results.jsonl. Delegation counts and peak RSS are read from that run's own log; throughput is deliberately absent, because an Android GPU speed is not quotable without a CPU row from the same phone and that control has not been run.fa7018f
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Card: state where GPU execution is verified, and scope the Android decode note
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Falcon-H1-3B int8 (fp32act, full prefill ladder)
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Falcon-H1-3B LiteRT-LM card
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initial commit
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