card: model.onnx + INT8 ONNX are bundled (fix not-bundled note)

README.md

@@ -68,10 +68,10 @@ This model trades long-context capability for raw throughput on short code units
   passage embeddings, unlike the teacher whose prefix is query-only). Mean-pool → **L2-normalize**.
 - For smaller indexes, truncate to **256** or **512** dims (MRL) before normalizing.
 
-The daemon runs the bundled engines directly (this repo is its CDN). The embedding recipe below is
-illustrative — `model.onnx` is **not bundled** here; it shows how an engine maps text → vector
-(tokenize with the bundled `sentencepiece.bpe.model`, run, the pooled `[B,768]` is already produced,
-then L2-normalize):
+The daemon runs the bundled engines directly (this repo is its CDN), but the FP32 `model.onnx` is
+**also bundled** for standalone use. The recipe below runs it with `onnxruntime`: tokenize with the
+bundled `sentencepiece.bpe.model`, run, and the pooled `[B,768]` is already produced — just
+L2-normalize:
 
 ```python
 import onnxruntime as ort, sentencepiece as spm, numpy as np
@@ -102,8 +102,9 @@ hardware. No compilation on the user's machine.
 - **OpenVINO** `*.xml` + `*.bin` — Intel **CPU / iGPU / NPU**, per bucket.
 - **Metal** `*_tvm_metal.*` — Apple Silicon (macOS), per bucket.
 - **Tokenizer** — `sentencepiece.bpe.model` (the model's SentencePiece; specials baked at
-  pad=0 / unk=1 / bos=2 / eos=3, byte-fallback) + `tokenizer_config.json`. The daemon loads the SP
-  directly; the FP32 `model.onnx` source is not bundled here (this repo is the daemon's engine CDN).
+  pad=0 / unk=1 / bos=2 / eos=3, byte-fallback) + `tokenizer_config.json`. The daemon loads the SP directly.
+- **ONNX source** — `model.onnx` (+ `model.onnx.data`) FP32 and `model_int8qdt.onnx` (INT8 W8A16) — for
+  standalone `onnxruntime` / optimum use, and the source the engines are compiled from.
 
 ## Evaluation — in-scope CoIR (sub-CoIR)