Update model card

README.md

@@ -85,7 +85,7 @@ them locally with `build/jepa-quantize vjepa2-ac-vitg-f16.gguf out.gguf q6_k -t
 
 ## Measured
 
-Every figure below is read from a committed artifact of jepa.cpp [`d53ab48`](https://github.com/aselimc/jepa.cpp/commit/d53ab48) by `scripts/hf_publish.py` — [parity](https://aselimc.github.io/jepa.cpp/parity/), [quantization](https://aselimc.github.io/jepa.cpp/quantization/), [accuracy](https://aselimc.github.io/jepa.cpp/accuracy/), [performance](https://aselimc.github.io/jepa.cpp/performance/) and `tests/results/*.json`.
+Every figure below is read from a committed artifact of jepa.cpp [`00bfd4e`](https://github.com/aselimc/jepa.cpp/commit/00bfd4e) by `scripts/hf_publish.py` — [parity](https://aselimc.github.io/jepa.cpp/parity/), [quantization](https://aselimc.github.io/jepa.cpp/quantization/), [accuracy](https://aselimc.github.io/jepa.cpp/accuracy/), [performance](https://aselimc.github.io/jepa.cpp/performance/) and `tests/results/*.json`.
 
 **Speed** — the encoder graph at f16 on 32 threads (AMD Ryzen Threadripper PRO 7995WX 96-Cores): 2228 ms per 16-frame clip; 15747 ms per 64-frame clip. The same shape on NVIDIA RTX 4500 Ada Generation: 27.8 ms. Peak RSS at f16: 3092 MiB.
 
@@ -97,9 +97,11 @@ cosine 1.0000000 against Meta's own world-model dump, and K candidates batched o
 axis are bit-identical to K sequential rollouts, and `jepa_ac_plan` reproduces Meta's own CEM
 planner to 3e-08 on its own recorded random draws. Full tables in [parity](https://aselimc.github.io/jepa.cpp/parity/).
 
-**`-q4_k.gguf` self-reports `general.file_type = q4_0`, and that is correct.** K-quants need
-`ne[0] % 256 == 0` and this family's width is 1408 = 5.5 x 256, so every `[*, 1408]`-row matrix
-falls back to q4_0; the field is the most common stored type. `jepa-info` lists the real mix.
+**On `-q4_k.gguf`:** K-quants need `ne[0] % 256 == 0`, and the encoder's width is 1408 = 5.5 x 256,
+so every `[*, 1408]`-row matrix falls back to q4_0. In THIS bundle the 24 predictor blocks are
+1024-wide and keep the K-quant, so the mix is 121 q4_0 against 137 q4_K and the file reports
+`general.file_type = 12 (q4_k)`. (The encoder-only `vjepa2-vitg-fpc64-256-q4_k.gguf` goes the other
+way: 158 q4_0 / 52 q4_K, reported as q4_0.) `jepa-info` lists the real mix.
 
 ## Source, licence and attribution
 
@@ -117,7 +119,7 @@ The licence travels inside every GGUF as `general.license` and the origin as `ge
 
 ## Conversion
 
-Produced by jepa.cpp [`d53ab48`](https://github.com/aselimc/jepa.cpp/commit/d53ab48):
+Produced by jepa.cpp [`00bfd4e`](https://github.com/aselimc/jepa.cpp/commit/00bfd4e):
 
 ```bash
 scripts/download_models.sh --convert vjepa2-ac