Add precision notes

README.md

@@ -34,3 +34,19 @@ git push
 git tag v1.0.0 -m 'Model release description'
 git push origin tag v1.0.0
 ```
+
+## Precision
+
+For static embeddings and cosine similarity, precision isn't as important. For an end
+to end to test in Firefox on some vectors here was the cosine similarity for the same
+mean pooled result. Note that the vector math happens in the f32 space, but storage
+for the embeddings is in a lower precision.
+
+f32 vs f16: cosine similarity = 1.00000000<br/>
+ → They are essentially identical in direction.
+
+f32 vs f8: cosine similarity = 0.99956375<br/>
+ → Very close, only tiny quantization effects.
+
+Note that this was done on the `torch.float8_e4m3fn`, while `torch.float8_e5m2` generally
+has more loss.