A 33.4M-parameter code-search encoder fine-tuned from bge-small-en-v1.5, the evidence behind its figures, and a Space that runs it in a browser.
Burton Lancaster PRO
RiverRider
AI & ML interests
Explainable AI
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liked a Space 3 days ago
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RiverRider/motherlode-code-small-en-v0.1 updated a collection 4 days ago
MotherlodeOrganizations
A 339 KB linear probe on a frozen general-purpose backbone: 0.7590 on the official ChestX-ray14 split, against 0.7451 fine-tuned.
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 33.4M-parameter code-search encoder fine-tuned from bge-small-en-v1.5, the evidence behind its figures, and a Space that runs it in a browser.
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