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repliedto their post about 3 hours ago
First stuntd model is on the Hub :) pollix/stuntd-support-triage is three small heads on the Laya encoder that triage a support ticket in one request: category, urgency and needs_human. About 50 MB each, all three answers come back at a p50 of 71ms through the daemon. On 1,000 tickets they never saw, each head answers on its own when it's sure: category 99.9%, needs_human 92%, urgency 76%. A ticket only skips the big model when all three are sure, that's 72.7% of them, and all three are right on 97.1% of those. It's the support demo from the repo, so the tickets are generated and the teacher is a rule. The point is to show what a head looks like and how fast it is, then you train the same thing on your own traffic with your own LLM as the teacher. ``` hf download pollix/stuntd-support-triage --local-dir support-heads ``` Model: https://huggingface.co/pollix/stuntd-support-triage Everything in one place: https://huggingface.co/collections/pollix/stuntd-6abe0a33303828e10c72ab41 Code: https://github.com/bladedevoff/stuntd
reacted to theirpost with 🔥 about 4 hours ago
First stuntd model is on the Hub :) pollix/stuntd-support-triage is three small heads on the Laya encoder that triage a support ticket in one request: category, urgency and needs_human. About 50 MB each, all three answers come back at a p50 of 71ms through the daemon. On 1,000 tickets they never saw, each head answers on its own when it's sure: category 99.9%, needs_human 92%, urgency 76%. A ticket only skips the big model when all three are sure, that's 72.7% of them, and all three are right on 97.1% of those. It's the support demo from the repo, so the tickets are generated and the teacher is a rule. The point is to show what a head looks like and how fast it is, then you train the same thing on your own traffic with your own LLM as the teacher. ``` hf download pollix/stuntd-support-triage --local-dir support-heads ``` Model: https://huggingface.co/pollix/stuntd-support-triage Everything in one place: https://huggingface.co/collections/pollix/stuntd-6abe0a33303828e10c72ab41 Code: https://github.com/bladedevoff/stuntd
posted an update about 4 hours ago
First stuntd model is on the Hub :) pollix/stuntd-support-triage is three small heads on the Laya encoder that triage a support ticket in one request: category, urgency and needs_human. About 50 MB each, all three answers come back at a p50 of 71ms through the daemon. On 1,000 tickets they never saw, each head answers on its own when it's sure: category 99.9%, needs_human 92%, urgency 76%. A ticket only skips the big model when all three are sure, that's 72.7% of them, and all three are right on 97.1% of those. It's the support demo from the repo, so the tickets are generated and the teacher is a rule. The point is to show what a head looks like and how fast it is, then you train the same thing on your own traffic with your own LLM as the teacher. ``` hf download pollix/stuntd-support-triage --local-dir support-heads ``` Model: https://huggingface.co/pollix/stuntd-support-triage Everything in one place: https://huggingface.co/collections/pollix/stuntd-6abe0a33303828e10c72ab41 Code: https://github.com/bladedevoff/stuntd
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