Model card: original fp32 / fp16 accuracy columns (stereo), table formatting

README.md

@@ -79,7 +79,7 @@ Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastfo
 
 ### Parity with the original code
 
-Every variant is checked against golden outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.02 and p99 <= 0.08 (absolute error of the output `logits`).
+Every variant is checked against reference outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.02 and p99 <= 0.08 (absolute error of the output `logits`).
 
 ## Usage
 
@@ -98,4 +98,4 @@ Install with `pip install fastforks` (see <https://github.com/kleinicke/fastfork
 
 `classification.load("squeezenet1_1.tv_in1k", mode="kernels")` runs our CUDA kernels (`cudnn_hacc`, `fvm_conv`, `fvm_extra`, `fvm_mb`, `fvm_r3`). It needs a prebuilt kernel for your GPU architecture (downloaded from <https://huggingface.co/fastforks/kernels> on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.
 
-<!-- staged from fastforks 03e6fdbc (2026-11-05) -->
+<!-- staged from fastforks badda9aa (2026-11-06) -->