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    "command": "python convert_lfm25_vl3b.py ../src_models/lfm25-vl-450m out_vl450m_int8_dir && python quantize_vl.py out_vl450m_int8_dir/model.litertlm LFM2.5-VL-450M_int8.litertlm --recipe none  # export-time dynamic int8 is the convert default (no --fp); the quantize_vl pass adds ExecutorMetadata + the externalized-embedder int8 recipe (RESULTS.md)",
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            "VERBOSE: Replacing 459 out of 543 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 104 partitions for subgraph 1 (prefill_512).",
            "VERBOSE: Replacing 459 out of 543 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 104 partitions for subgraph 2 (prefill_256).",
            "VERBOSE: Replacing 459 out of 543 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 104 partitions for subgraph 3 (prefill_128).",
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            "VERBOSE: Replacing 543 out of 543 node(s) with delegate (LITERT_CL) node, yielding 1 partitions for subgraph 2 (prefill_256).",
            "VERBOSE: Replacing 543 out of 543 node(s) with delegate (LITERT_CL) node, yielding 1 partitions for subgraph 3 (prefill_128).",
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            "VERBOSE: Replacing 543 out of 543 node(s) with delegate (LITERT_CL) node, yielding 1 partitions for subgraph 1 (prefill_512).",
            "VERBOSE: Replacing 543 out of 543 node(s) with delegate (LITERT_CL) node, yielding 1 partitions for subgraph 2 (prefill_256).",
            "VERBOSE: Replacing 543 out of 543 node(s) with delegate (LITERT_CL) node, yielding 1 partitions for subgraph 3 (prefill_128).",
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  "model": {
    "family": "lfm2_vl",
    "id": "lfm25-vl-450m-int8",
    "license": "lfm-open-license-v1.0",
    "source_url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M",
    "task": "image-text-to-text"
  },
  "pitfalls": [
    "int4 is the better variant of this model on our gates — this int8 scores 6/8 text and 3/5 image vs the int4's 8/8 and 4/5; on Pixel 8a CPU int8 prefills faster than int4 but decodes ~40% slower — pick by whether prompts or outputs dominate (HF card; lfm25vl_work/RESULTS.md gate table, measured).",
    "Fine-grained shape questions can miss on-device at this scale across all quant levels including an unquantized probe, while the torch reference answers them; conversion exonerated by experiment (vision tflite cosine 1.0000, token-identical streams). Treat as a fast small VLM for coarse visual tasks and large-text OCR; the 3B answers all fixtures (RESULTS.md; HF card wording).",
    "Same VLM-bundle mechanics as the siblings: transformers==5.14.1 pin; llm_model_type { lfm2 {} } with bare <image> template; externalized-embedder int8 pass; pack/unpack post-processing (single-tflite tools drop vision sections) (REPRODUCE.md).",
    "iPhone GPU blocked (LiteRT-LM#3129) — iOS runs CPU; Android OpenCL delegates the text graph fully (543/543, zero rejections).",
    "Android v0.16.0 litert_lm_main has no image flag (--max_num_images only) — image input is API-only on Android for now, so device rows are text-graph numbers (RESULTS.md)."
  ],
  "schema_version": "1.2"
}
