Leaderboard: add serin-0.8b to the fitted table (trained on train)

#51
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  1. README.md +10 -9
README.md CHANGED
@@ -142,14 +142,15 @@ zero-shot and their scores are not comparable with the table above.
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  | 23 | [Seb-9B](https://huggingface.co/ironbcc/seb-9b) (Qwen3.5-9B, v1) ([#13](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/13)) | general, then fitted on `train`, one question per request | 0.754 | 0.349 | 0.131 | 0.031 | – |
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  | 24 | [tasksource-decider-nano](https://huggingface.co/tasksource/tasksource-decider-nano) (149M cross-encoder, `v5`) ([#45](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/45)) | general; `train` is 0.5% of its training mix | 0.7515 | 0.1125 | 0.0621 | 0.125 | 37 ms (A30, shared) |
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  | 25 | [ProKope-421M-8K](https://huggingface.co/ProKope-AI/ProKope-421M-8K) (421M, 8K context) | specialist | 0.7185 | – | 0.385 | – | 43 ms |
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- | 26 | [gemma3-270m-typed-decisions](https://huggingface.co/rony/gemma3-270m-typed-decisions) (Gemma 3 270M, full fine-tune) | general, fine-tuned on `train` | 0.702 | 0.156 | 0.091 | 0.128 | 68 ms |
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- | 27 | SAP RPT 1.6 via the SAP Decisions API (`/api/systemone`), [evaluation](https://huggingface.co/datasets/tomgutt/rpt-1.6-typed-decisions-evaluation) ([#49](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/49)) | in-context: 300 provider-held `train` examples per workflow, no weight updates; closed API | 0.672 | 0.646 | 0.151 | 0.019 | 656 ms (p50, API round trip) |
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- | 28 | [Bekko System One v0 400M](https://huggingface.co/hotchpotch/bekko-system-one-v0-400m), scored by us | trained on `train` and other data | 0.668 | 0.214 | 0.113 | 0.143 | 808 ms |
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- | 29 | [vega-08b-public-intents](https://huggingface.co/nandakishorm/vega-08b-public-intents) (0.8B) ([#37](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/37)) | specialist (bundled typed-decisions adapter active) | 0.663 | 0.435 | 0.191 | 0.074 | – |
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- | 30 | ModernBERT-base (149M), ours | specialist, fitted per workflow | 0.646 | 0.223 | 0.119 | 0.179 | 349 ms |
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- | 31 | MiniLM-L6 (22M), ours | specialist, fitted per workflow | 0.587 | 0.262 | 0.143 | 0.108 | 22 ms |
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- | 32 | [Bekko System One v0 68M](https://huggingface.co/hotchpotch/bekko-system-one-v0-68m), scored by us | trained on `train` and other data | 0.537 | 0.293 | 0.160 | 0.116 | 176 ms |
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- | 33 | [Bekko System One v0 17M](https://huggingface.co/hotchpotch/bekko-system-one-v0-17m), scored by us | trained on `train` and other data | 0.483 | 0.344 | 0.203 | 0.136 | 33 ms |
 
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  **Quyet-1.0-Large, served by Decision Models, has the highest accuracy among zero-shot models (0.791). [meraGPT Decider 1](https://meragpt.com/models/state-decider-1?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions) is second on accuracy (0.768) and has the lowest KL (0.096) and Brier (0.052) of any zero-shot model.** It leads Jev on every question type (`noul` 0.840 vs
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  0.775, `choice` 0.733 vs 0.720, `score` 0.739 vs 0.696), its distributions sit far
@@ -190,7 +191,7 @@ hf jobs run --flavor a100-large --timeout 2h \\
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  - **Scored by us:** meraGPT Decider 1, TypeSafe Jev 1.13.0, Liquid AI d1, Baseten Mammoth 1 and Elephant 1, Featherless Simple Jev, the three Jeff models, the three rows served by Decision Models ([#38](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/38)), the Bekko and Adaptive Classifier rows, and the Prior and Uniform references.
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  - **Scored by Hugging Face:** the rows linked to [#37](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/37), run on Hugging Face Jobs.
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  - **Reported by an outside evaluator:** SAP RPT 1.6 ([#49](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/49)), a self-run, unofficial evaluation of SAP's public Decisions API. The API answers from 300 `train` examples per workflow that SAP holds on its side, with no weight updates; those examples are SAP's description and could not be checked.
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- - **Reported by the model's creator:** every other row, in its own discussion.
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  ## Reading a score
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  | 23 | [Seb-9B](https://huggingface.co/ironbcc/seb-9b) (Qwen3.5-9B, v1) ([#13](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/13)) | general, then fitted on `train`, one question per request | 0.754 | 0.349 | 0.131 | 0.031 | – |
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  | 24 | [tasksource-decider-nano](https://huggingface.co/tasksource/tasksource-decider-nano) (149M cross-encoder, `v5`) ([#45](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/45)) | general; `train` is 0.5% of its training mix | 0.7515 | 0.1125 | 0.0621 | 0.125 | 37 ms (A30, shared) |
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  | 25 | [ProKope-421M-8K](https://huggingface.co/ProKope-AI/ProKope-421M-8K) (421M, 8K context) | specialist | 0.7185 | – | 0.385 | – | 43 ms |
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+ | 26 | [serin-0.8b](https://huggingface.co/lambdasec/serin-0.8b) (Qwen3.5-0.8B, 0.8B) | general, then trained on `train` | 0.7165 | 0.9577 | 0.1502 | 0.1122 | – |
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+ | 27 | [gemma3-270m-typed-decisions](https://huggingface.co/rony/gemma3-270m-typed-decisions) (Gemma 3 270M, full fine-tune) | general, fine-tuned on `train` | 0.702 | 0.156 | 0.091 | 0.128 | 68 ms |
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+ | 28 | SAP RPT 1.6 via the SAP Decisions API (`/api/systemone`), [evaluation](https://huggingface.co/datasets/tomgutt/rpt-1.6-typed-decisions-evaluation) ([#49](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/49)) | in-context: 300 provider-held `train` examples per workflow, no weight updates; closed API | 0.672 | 0.646 | 0.151 | 0.019 | 656 ms (p50, API round trip) |
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+ | 29 | [Bekko System One v0 400M](https://huggingface.co/hotchpotch/bekko-system-one-v0-400m), scored by us | trained on `train` and other data | 0.668 | 0.214 | 0.113 | 0.143 | 808 ms |
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+ | 30 | [vega-08b-public-intents](https://huggingface.co/nandakishorm/vega-08b-public-intents) (0.8B) ([#37](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/37)) | specialist (bundled typed-decisions adapter active) | 0.663 | 0.435 | 0.191 | 0.074 | – |
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+ | 31 | ModernBERT-base (149M), ours | specialist, fitted per workflow | 0.646 | 0.223 | 0.119 | 0.179 | 349 ms |
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+ | 32 | MiniLM-L6 (22M), ours | specialist, fitted per workflow | 0.587 | 0.262 | 0.143 | 0.108 | 22 ms |
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+ | 33 | [Bekko System One v0 68M](https://huggingface.co/hotchpotch/bekko-system-one-v0-68m), scored by us | trained on `train` and other data | 0.537 | 0.293 | 0.160 | 0.116 | 176 ms |
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+ | 34 | [Bekko System One v0 17M](https://huggingface.co/hotchpotch/bekko-system-one-v0-17m), scored by us | trained on `train` and other data | 0.483 | 0.344 | 0.203 | 0.136 | 33 ms |
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  **Quyet-1.0-Large, served by Decision Models, has the highest accuracy among zero-shot models (0.791). [meraGPT Decider 1](https://meragpt.com/models/state-decider-1?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions) is second on accuracy (0.768) and has the lowest KL (0.096) and Brier (0.052) of any zero-shot model.** It leads Jev on every question type (`noul` 0.840 vs
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  0.775, `choice` 0.733 vs 0.720, `score` 0.739 vs 0.696), its distributions sit far
 
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  - **Scored by us:** meraGPT Decider 1, TypeSafe Jev 1.13.0, Liquid AI d1, Baseten Mammoth 1 and Elephant 1, Featherless Simple Jev, the three Jeff models, the three rows served by Decision Models ([#38](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/38)), the Bekko and Adaptive Classifier rows, and the Prior and Uniform references.
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  - **Scored by Hugging Face:** the rows linked to [#37](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/37), run on Hugging Face Jobs.
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  - **Reported by an outside evaluator:** SAP RPT 1.6 ([#49](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/49)), a self-run, unofficial evaluation of SAP's public Decisions API. The API answers from 300 `train` examples per workflow that SAP holds on its side, with no weight updates; those examples are SAP's description and could not be checked.
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+ - **Reported by the model's creator:** every other row, in its own discussion. [serin-0.8b](https://huggingface.co/lambdasec/serin-0.8b) (lambdasec) has no discussion thread; it is a self-run evaluation with the benchmark's own scorer.
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  ## Reading a score
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