AI Fingerprinting Visually

Alright. So I did some digging. I found no visual representation of a models capability, just metrics on benchmarks, that don’t really say much in practice. So I am working on a means to check the functionality if a model by tensor scanning and weight evaluation. This stems off the RMT studies of 2024, using Alpha values to map to a band on each layer type the model has. I have data from QWEN2.5 4b bf16, q8km, and q4km ggufs. I had to make the scripts myself, as weightwatch doesn’t have any data on quantized values and doesn’t support their data sets. So I am digging into that data myself and trying to make a heat map per layer of the models usage potential. What this does: Runs the weights first, gets a map of their values to stage each tensor value against it. Then runs every tensor, mapping the utilization of the values stored directly the weights. It returns alpha per layer and block of the models tumble through inference. It doesn’t spin the model up and ask it questions. It directly scans the matrices that make the model, against the weights that drive its inference. So far, quantization hasn’t affected the Alpha value of the model. It is low parameter, so I need to run it against a high parametered model to check integrity of what I am seeing on the smaller models. But the only deviation was in block 31 (the last block). Is this something people would be interested in as an open source project? I am doing it mostly to make my models work better in my custom framework and to find the way verbosity is determined in the lower parameter models. If you are interested, have you had a low parametered model work really well without verbosity? Have you run lower parametered models as agents? If so, which ones? I would like to fingerprint them and get a good detailed map with descriptive use data tied in.