an1-core

the intended content tokens. This suggests the probe is relying on global sentence summaries (standard embeddings in causal structures) rather than the novel 'meaning fields' claimed in the paper. --- I dont have time to look at more of this and I just looked at how the eval is made, but please dont waste peoples times when you dont even know what you are evaluating.

eeze a pretrained model, train a classifier on its features). The full flow (as reported by CC) is "Text → [Full Transformer] → activations → [Tiny Head] → prediction" Basically, there's no code to train a real "student" model that would run without the teacher. === The repo/paper say that there's a mythical "commercial version" that has all the goodies: (repo) > This reference implementation (an1-core) does not include the FPU, AN4, or other proprietary optimization components covered by these patents. It provides only the core scientific demonstration of the meaning fields phenomenon. (paper) > Production deployment: Optimized implementations (AN1-Turbo) with learned layer selection, adaptive loss scheduling, and CUDA-accelerated inference available under commercial license. But right now we only have the code in the repo. === In the paper they show that the student model (30M params) gets ~82% on SST-2 (labels-only). But what what they don't show is that DistilBERT (>5 year old model) already achieves 91% on the same dataset despite only having 66M params. Another weird tidbit from the paper - in the section where they show the economic impact, they claim that LLaMA 70B runs at 2 tok/s at batch size=1 on an H200. In reality that number is at least a magnitude bigger even without quantization, like 20-40 tok/s. With quantization it can easily be above 100 tok/s.

ace.