A 339 KB linear probe on a frozen general-purpose backbone: 0.7590 on the official ChestX-ray14 split, against 0.7451 fine-tuned.
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RiverRider
AI & ML interests
Explainable AI
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updated a model 4 days ago
RiverRider/blackwindow-mlc-libs published a model 4 days ago
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RiverRider/srt-cxr14-pooled-probeOrganizations
Read-outs on frozen models you can actually run. Nothing here fine-tunes the host.
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SRT Omni Cross-Vendor Retrieval
🔀Search mixed-media index with text across AI models
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ChestX-ray14 frozen probe
🫁1339 KB linear probe beats a fine-tuned ResNet-50
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0.6B Reads 27B
🪟A 0.6B model in your tab searches a 27B model's index
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One Model, Two Jobs
🪟A 0.6B model in your tab searches a 27B model's index
A 339 KB linear probe on a frozen general-purpose backbone: 0.7590 on the official ChestX-ray14 split, against 0.7451 fine-tuned.
One company's model searches another's index at a rate indistinguishable from native: retention 0.988, 95% CI [0.955, 1.023].
Read-outs on frozen models you can actually run. Nothing here fine-tunes the host.
-
SRT Omni Cross-Vendor Retrieval
🔀Search mixed-media index with text across AI models
-
ChestX-ray14 frozen probe
🫁1339 KB linear probe beats a fine-tuned ResNet-50
-
0.6B Reads 27B
🪟A 0.6B model in your tab searches a 27B model's index
-
One Model, Two Jobs
🪟A 0.6B model in your tab searches a 27B model's index