Update README.md

README.md

@@ -22,7 +22,11 @@ base_model:
 
 This will be the real prototype, fingerprinting was the earlier thought and the full upcoming prototype is ready for train.
 
-The upcoming checkpoints will push after the process is successful, likely 1 hour per epoch for 5 epochs or so should be more than eneough.
+https://huggingface.co/AbstractPhil/geolip-axis-prototype
+
+The example code and prototype axis modulators are present there as they are, and they will be utilized throughout upcoming experiments.
+
+For CaptionBERT, upcoming checkpoints will push after the process is successful, likely 1 hour per epoch for 5 epochs or so should be more than eneough.
 
 This marks the first use of a new prototype object dubbed AnchorBank, which is designed specifically to house the necessary implications that the model is distilled with,
 while specifically aligning the expectation of those distillation valuations into the bank itself.
@@ -30,6 +34,11 @@ while specifically aligning the expectation of those distillation valuations int
 This allows the model to POTENTIALLY solve nth token lookup without a head, so a head will allow finetuning. If successful, the anchor bank will contain 
 all the knowledge the model requires to geomewtrically represent it's data into expanded structures - if the losses and training process is correctly aligned to the task.
 
+**HOPEFULLY** after this refit, the structure will be capable of predicting NIL head token prediction, if not I'll work with a different small LLM project and then
+determine the potential utility of direct integration of the two on a MOE pipeline instead of a full collective behavioral implication.
+
+If that goes well, the MOE can be adapted into collective behavior if the systems align correctly, but that's a different process.
+
 # GEOLIP CaptionBERT-8192-fingerprinted
 
 The next iteration will require an expanded fingerprinting axis-based relational bank, specifically to the alignment of the data and the teachers at training time.