clip-experts-32 - training the second surge collective
The entire outcome should be under 800 megs space.
I've given purpose to my 32 shunt experts; entirely devoted to encoder delta projection for the vit-l-14 - CLIP_L and BERT encoders.
With each expert, will come a heavily planned and synthetic caption set of 1 billion captions along with it.
They are to be interconnected and regulated with a resonant core - trained entirely on timestep normalization using the frozen layer surge paradigm - targeting the experts as distilled responses.
These experts will be trained independently ahead of time and frozen during the collective convergence.
The shunt is capable of both in-place inverse and in-place anchored delta control. It's highly flexible and very difficult to snap when trained to expert form.
Each of these experts will require a synthetic dataset to converge with the CLIP_L. It cannot be an end-all dataset like say feeding LAION into them. That would never work.
Every single expert needs it's own knowledge that the others do not share. Each of them need it's own peaks and it's own faults - it's own strengths and it's own shatter-points.
That means they are more akin to... manifested idea, rather than simply creating something that is intended to be perfect.
Their strength, is through their imperfection. Their own ability to identify which is the best suited for this task, and which is not. Which is best capable at modifying this prompt piece, and which is entirely incapable at understanding it.
They will learn all of this through shared bonded learning as their training sequence trains the beatrix convergence directly on top of them.
This is going to be the first large-scale showcase of the Surge paradigm, and the first truly powerful outcome that I'll share with the public.
1. Classifier — Tag-symbolic literal description (current dataset)
2. Metaphorist — Poetic/metaphorical abstraction
3. Relationalist — Spatial and object relationship grounding
4. Contrarian — Contrastive and anti-alignment phrasing
5. Aesthetician — Subjective beauty and emotional tone labeling
6. Narrativist — Caption as scene-from-story; progression implied
7. Director — Cinematic framing and camera language
8. Moodcaster — Psychological state and affective presence
9. Negativist — Absence-based or occlusion-aware captioning
10. Essencian — Compressed core-meaning phrasing
11. Remembrancer — Caption as recalled (blurry, biased, human)
12. Hyperrealist — Over-descriptive detail for fidelity learning
13. Sensorist — Sensory translation: texture, sound, pressure
14. Hallucinator — Failure-mode simulation, illusion, miscaptioning
15. Perspective Agent — Caption from character-specific viewpoint
16. Echo Reviser — Paraphraser for synthetic tag-based captions
17. Objectifier — Caption that reduces to object taxonomies only
18. Environmentalist — Caption driven by background/environment context
19. Chronographer — Time-aware captions; frame differences
20. Vignette — Caption as fragment from a larger invisible scene
21. Interlocutor — Caption as if answering a question about the image
22. Dialogist — Caption as a quote from the subject or observer
23. Minimalist — Caption reduced to a single evocative word
24. Analogist — Caption by comparing to something else
25. Labelsmith — Industrial/product-style naming systems and text display
26. Mythweaver — Caption as archetype, legend, or symbolic figure
27. Jester — Caption in humor, irony, or satire mode
28. Distortionist — Caption of warped, adversarial, or glitched data
29. Perspective Blender — Mixed viewpoints in one caption (e.g., 1st + 3rd)
30. Foregrounder — Only foreground content described
31. Backgrounder — Caption describes only the setting
32. Synthesist — Integrator of multiple styles from all others