Add a precisions table

README.md

@@ -42,11 +42,23 @@ to end to test in Firefox on some vectors here was the cosine similarity for the
 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.
+> 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.
+
+Precision also affects download size. For instance with larger
+[minishlab/potion-multilingual-128M/](models/minishlab/potion-multilingual-128M/README.md)
+model. The `fp32` is 228M compressed, while only 51M for `fp8_e4m3`, which has competetive
+quantization values.
+
+| precision     | dimensions | size    |
+| ------------- | ---------- | ------- |
+| fp32          | 128        | 228M    |
+| fp16          | 128        | 114M    |
+| **fp8_e4m3**  | 128        | **51M** |
+| fp8_e5m2      | 128        |  44M    |