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Jason Brashear PRO

jbrashear

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posted an update about 8 hours ago

I publish three decision shapes in Jeb: choice, noul and score. The distinction matters before you wire an answer into an action. Choice gives a probability for each named option. Noul gives the probability that a statement is true. Score gives a probability for each level of an ordered scale, plus the expected level. The support-ticket example on jebadiah.ai shows a frustration score of 2.29 on a 0 to 3 scale. That's an expected level between labels, not a fourth label or a claim that the answer is 229 percent certain. That example was recorded on September 26, 2026. The standalone server exposes POST /v1/systemone. The decision scripts read answer-label probabilities; a chat reply doesn't expose those. Your application still has to decide what to do with the result. Which shape fits the first decision you want to make locally? https://jebadiah.ai/ PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.

posted an update 11 days ago

I publish a "same answer" count for each official Jeb GGUF build. It means the top option matched the full bf16 weights. It doesn't mean every probability was identical, or that the answer was correct. On the published 260-question sample, Q8_0 agreement was 260/260 for 27B, 257/260 for 9B v2 and 256/260 for 4B v2. A made-up example: Billing at 0.81 and Billing at 0.78 agree on the pick. A routing rule that acts above 0.80 would behave differently. That's why the cards report probability differences too. The records are under eval/agreement-*.json in the official repos. These numbers apply to those files, not independent quantizers' builds. If you're choosing a build for a threshold-based decision, I'd compare probabilities near the threshold on your own examples too. https://huggingface.co/frontier-infra/jebadiah-27b-GGUF https://huggingface.co/frontier-infra/jebadiah-9b-v2-GGUF https://huggingface.co/frontier-infra/jebadiah-4b-v2-GGUF PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.

posted an update 17 days ago

Jebadiah 27B v2.1 with the T1e inference recipe scores Full 64.4 on Jev Decision Index 0.4.1: rank 25 overall and tie rank 6, above the Jev 1.14 reference at 60.24. Board data: October 22, 2026. The smaller 9B v2.1 with T1e scores Full 50.9, rank 80 overall and tie rank 18, with a board median latency of 125.1 ms versus 303.2 ms for the 27B. These are board reproduction timings on one RTX PRO 6000, not a promise for another setup. The 27B is a LoRA fine-tune of Qwen3.8-27B. These are benchmark scores on the board's scale, not accuracy percentages or a guarantee for every runtime or quantized build. The public index is 60.46. Private new-domain tasks map to 72.52 on the public scale. The full result combines private same-skill tests and private new-domain tasks at 60/40. The 0.9-point tie band defines groups; the overall position is 25. Announcement and recipe background (earlier October 20 snapshot): https://titaniumcomputing.com/insights/jebadiah-decision-index-0-4/ Board: https://huggingface.co/spaces/multimodalart/jev-decision-index The board describes these as the same published v2.1 weights with a new inference recipe. Its lab check reproduced the submitted decisions with 100.00% agreement for the 9B and 99.90% for the 27B, each on 831 rows. That is reproduction agreement, not accuracy. PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.

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I publish three decision shapes in Jeb: choice, noul and score. The distinction matters before you wire an answer into an action.

Choice gives a probability for each named option. Noul gives the probability that a statement is true. Score gives a probability for each level of an ordered scale, plus the expected level.

The support-ticket example on jebadiah.ai shows a frustration score of 2.29 on a 0 to 3 scale. That's an expected level between labels, not a fourth label or a claim that the answer is 229 percent certain. That example was recorded on September 26, 2026.

The standalone server exposes POST /v1/systemone. The decision scripts read answer-label probabilities; a chat reply doesn't expose those. Your application still has to decide what to do with the result.

Which shape fits the first decision you want to make locally?

https://jebadiah.ai/

PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.