Update README.md

README.md

@@ -5,6 +5,34 @@ base_model:
 pipeline_tag: zero-shot-classification
 ---
 
+# Updated - Spark works.
+
+max-vit-goliath-spark is essentially a 300k param vit that can handle nearly identical accuracy as the larger model with a shockingly robust utility of the features.
+
+```PYTHON
+'pentachora_spark': PentachoraConfig(
+    dim=64, depth=5, heads=4, mlp_ratio=4.0,
+    preserve_structure_until_layer=2,
+    dropout_rate=0.0, drop_path_rate=0.0
+),
+```
+
+64 dim vocabulary effectively trying to carry the entire vit.
+It's using a particularly effective geometric attention.
+
+The output produces effective image feature representations in geomeric format.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630cf55b15433862cfc9556f/DvJBf3cP6p2zj6P_wc7HH.png)
+
+
+```
+Final Results:
+Best Validation Accuracy: 54.15%
+Final Train Loss: 2.1262
+Final Val Loss: 3.6396
+```
+
+# Original post
 Currently it's only a pickled early version at about ~50% accuracy.
 
 This one is a 12 layer 8 head variation of max-vit-goliath that trained on geometric vocab with cifar100 using a specialized 5d format. It's WORKING - somewhat, but it's definitely nothing to phone home about yet.