Instructions to use litert-community/LFM2.5-Encoder-350M-PII-Detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/LFM2.5-Encoder-350M-PII-Detector 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
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Card: base_model_relation: quantized (list under the base model's Quantizations)
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Add measured Galaxy S26 NPU/GPU section
Verified signatureNumbers from the S26 classic sweep. Primary logs: litertlm-convert/community_accel_work/s2_npu_sweep/results.jsonl (on-device JIT) and portal_work/bench_all.txt + bench_redo.txt (the 2026-08-23 AOT sweep); every row states which of the two produced it. Rows carry device, SoC, runtime, N and thermal state, and join to this repo by exact .tflite file name. Rows taken outside the sweep's thermal window are not reported.120f82b
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Card update: rank-4 re-export GPU results (full mobile delegation; measured on-device)
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Rank-4 repeat_kv re-export: full mobile-GPU delegation (bitwise-identical CPU outputs)
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Rank-4 repeat_kv re-export: full mobile-GPU delegation (bitwise-identical CPU outputs)
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Card: add the measured Pixel 8a rows (GPU vs CPU) that the Android sweep produced
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Card: measured Performance table (M4 Max + iPhone 17 Pro), verified runnable usage script, accuracy note
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LFM2.5-Encoder-350M-PII-Detector LiteRT: int8 (iPhone-verified bit-exact) + fp16, task-level parity verified
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initial commit
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