license: other
tags:
- executorch
- xnnpack
- pte
- on-device
- text-generation
base_model:
- LiquidAI/LFM2.5-1.2B-Instruct
LFM2.5-1.2B-Instruct — ExecuTorch XNNPACK 8da4w
lfm2_5_1_2b_xnnpack_8da4w.pte (741 MB)
- Source: LiquidAI/LFM2.5-1.2B-Instruct (hybrid conv/attention)
- License: LFM Open License v1.0
- Quantization: 8da4w (8-bit dynamic activation / 4-bit weight) + 8-bit embedding
(
embedding_quantize: "8,0"; cuts 1143 MB → 741 MB vs the fp32-embedding v1) - Export: executorch 1.4.0
export_llm, dynamic shape, max_seq_length 2048, XNNPACK extended_ops - Config:
llm_params/lfm2_5_1_2b_xnnpack_8da4w_e8.yaml
Verification (2026-08-13)
Mac gate (greedy via native.py, chat template): correct 2-sentence Rayleigh-scattering
answer, 170.8 tok/s on M-series Mac (reference only). v1 (fp32 embedding) passed 3/3
(Paris / Japanese / haiku) with identical quant settings otherwise.
iPhone 17 Pro / iOS 27 (ETBench, XNNPACK CPU, default threads), re-measured 2026-08-14 on this 8-bit-embedding build:
| metric | value |
|---|---|
| load | 0.6 s |
| ttft (short prompt) | 0.05-0.06 s |
| decode | 65-86 tok/s (86 short answer, 65 at 128 tokens) |
Outputs correct (Paris; coherent 128-token story). The earlier 1143 MB fp32-embedding build loaded in 1.6 s and decoded 55-81 tok/s, so quantizing the embedding table cut both the file and the load time without costing throughput.
Usage note — chat template is required. This is an instruct model: raw untemplated
text makes it emit <|im_end|> immediately (looks like broken generation but is not).
Always wrap prompts as
<|startoftext|><|im_start|>user\n...<|im_end|>\n<|im_start|>assistant\n, eos ids [7].
More models in this format: ExecuTorch Model Zoo — 31 models, each with the recipe that produced it.
Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.