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LFM2.5-1.2B-CoreAI

+LICENSE TEXT
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README.md ADDED Viewed

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+---
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+base_model: LiquidAI/LFM2.5-1.2B-Instruct
+tags:
+  - coreai
+  - aimodel
+  - apple-silicon
+  - on-device
+  - lfm2
+  - hybrid
+pipeline_tag: text-generation
+---
+# LFM2.5-1.2B-Instruct — Apple Core AI (`.aimodel`)
+**LiquidAI's LFM2.5-1.2B-Instruct converted to Apple's Core AI** (the Core ML successor
+announced at WWDC26), ready to run on iOS 27 / macOS 27. A conv + full-attention hybrid
+(10 short-conv mixers + 6 GQA attention layers) riding Apple's **`coreai-pipelined` GPU
+engine** — the first non-Qwen architecture on that fast path, with zero custom kernels.
+> Requires the iOS 27 / macOS 27 beta (Core AI ships with the OS). Conversion code, knowledge
+> base, and the Swift runner: **[coreai-model-zoo](https://github.com/john-rocky/coreai-model-zoo)**.
+## Measured (greedy; single-step top-1 gated 16/16 vs the fp32 Hugging Face oracle)
+| Surface | Bundle | Prefill | Decode |
+|---|---|---:|---:|
+| **M4 Max**, release `llm-benchmark` | `gpu-pipelined/lfm2_5_1_2b_instruct_decode_int8lin/` (1.5 GB) | 253.3 tok/s | **253.3 tok/s** |
+| **iPhone 17 Pro**, one-shot runner | same bundle | 39.2–39.4 | **38.0–39.6 tok/s** |
+| iPhone 17 Pro, chat app (CoreAIChat LFM mode, 200-tok turn) | same bundle | 30.7 | **35.8 tok/s** |
+- ~39 tok/s on iPhone ≈ **~87% of the naive bandwidth ceiling** (~60 GB/s ÷ ~1.4 GB/token) —
+  effectively memory-bandwidth saturated. Cold GPU specialization 6.8 s, warm load 1.6 s; no
+  AOT compile needed.
+- iPhone greedy sequences are **24/24 token-identical to the M4 Max GPU** on both fixed
+  verification prompts.
+- For scale: our Qwen3.5-0.8B on the same engine does 204 tok/s on M4 Max — this 1.2B does
+  253.
+## What the bundle is
+One full **LanguageBundle** (`.aimodel` + `tokenizer/` + `metadata.json`): decode-only graph,
+`input_ids` static `[1,1]`, position_ids + KV seq dynamic (→ the engine factory selects
+`coreai-pipelined`: async non-blocking encode, on-GPU argmax sampling, on-device KV growth).
+Weights are **int8 linear per-block-32** (scale-multiply dequant — no LUT; k-means LUT
+gathers measure slower on this GPU delegate) with the embedding, depthwise convs, norms,
+lm_head, and the four attention projections kept high-precision. The attention projections
+are fp32 on purpose: under a dynamic-shape graph the delegate's fp16 attention-prologue
+matmuls lose ~1.3% relative accuracy, which LFM2.5's large q/k-norm gains amplify into wrong
+logits — fp32 there restores layer-level exactness (+126 MB). Full write-up:
+[`knowledge/pipelined-engine.md`](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/pipelined-engine.md).
+## Run it
+```bash
+git clone https://github.com/john-rocky/coreai-model-zoo
+git clone https://github.com/apple/coreai-models
+git -C coreai-models apply ../coreai-model-zoo/apps/coreai-shared-product.patch \
+                           ../coreai-model-zoo/apps/coreai-pipelined-extra-states.patch
+# (the extra-states patch lets the engine carry the conv state as a fixed-shape extra state)
+# download this bundle into coreai-models/exports/, then:
+cd coreai-models && swift build -c release
+COREAI_CHUNK_THRESHOLD=1 ./.build/release/llm-benchmark \
+    --model exports/lfm2_5_1_2b_instruct_decode_int8lin -p 128 -g 256 -n 3
+```
+Run contract (each of these matters):
+- `COREAI_CHUNK_THRESHOLD=1` **before engine creation** — prefill must run as pipelined S=1
+  steps (prompt tok/s ≈ decode tok/s).
+- **Never call `engine.warmup()`** on this S=1 bundle (it warms query length 256, which the
+  static `[1,1]` graph rejects). A 1-token generate after load is the warmup;
+  `llm-runner` needs `--warmup exact --warmup-length 1`.
+- Benchmark **Release** builds only (a Debug engine measures ~3× slow).
+On iPhone, the [CoreAIChat sample app](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/CoreAIChat)
+has an **LFM** picker mode that downloads this repo in-app and chats through this bundle.
+## Conversion
+Reproducible with
+[`conversion/export_lfm2_decode_pipelined.py`](https://github.com/john-rocky/coreai-model-zoo/blob/main/conversion/export_lfm2_decode_pipelined.py)
+(+ the `models/macos/lfm2.py` overlay) from the upstream HF checkpoint. Numerics are gated
+the strict way: a teacher-forced S=1 sweep over a 16-position oracle prompt (top-1 vs the
+fp32 HF reference at every position, 16/16 required) plus an oracle-cache-seeded decode
+step — not long-rollout eyeballing. Model card with the full method and the GPU-delegate
+findings: [`zoo/lfm2.5.md`](https://github.com/john-rocky/coreai-model-zoo/blob/main/zoo/lfm2.5.md).
+## License
+The model weights derive from
+[LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) and are
+redistributed under the **LFM Open License v1.0** (see [LICENSE](LICENSE)): Apache-style
+grants, but **Commercial Use is licensed only for entities below US$10M annual revenue**
+(qualified non-profits exempt for non-commercial/research use). The conversion code is
+BSD-3-Clause (zoo repo).