Card: add author line under the rank badges
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README.md
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<p align="center"><a href="https://huggingface.co/spaces/Hanno-Labs/decision-bench-leaderboard"><img src="assets/proof-decisionbench.png" width="760" alt="DecisionBench (eng, v1): 1 bosun-v3.1-1.7b 87.29, 2 bosun-v3.1-0.6b 83.20, 3 imajev-4b 79.65, ahead of glm-5.3-flash, jev-1.13, deepseek-v4.1-flash, gpt-5.6-luna"></a></p>
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<p align="center"><sub>JevBench v1.4.2.2, scored 27 Sep 2026 (<a href="https://benchmarkheaven.com/jev-models">board</a>). Image JevBench v0.1.3, released 28 Sep 2026: #1 of 49, 76.39, ahead of Jev-Omni (<a href="https://benchmarkheaven.com/image-jev-bench">board</a>). DecisionBench (eng, v1): #3 of 56 models, 79.65, ahead of GLM-5.3 Flash (320B), Jev 1.13, DeepSeek V4.1 Flash (552B) and GPT-5.6 Luna; the two above are the benchmark team's own models (<a href="https://huggingface.co/spaces/Hanno-Labs/decision-bench-leaderboard">leaderboard</a>).</sub></p>
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<p align="center"><img src="assets/request-listing.png" class="block dark:hidden" alt="imajev-4b checks a listing against its photo: listing.color says red, the photo shows beige shoes; the model names listing.color at 0.999 and the app holds the listing"><img src="assets/request-listing-dark.png" class="hidden dark:block" alt="imajev-4b checks a listing against its photo: listing.color says red, the photo shows beige shoes; the model names listing.color at 0.999 and the app holds the listing"></p>
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imajev-4b is the recommended default: 83.9% on ImajevBench against the 9B's 82.1% at under half the size, and the only size trained in the phase-3 hard-data stage.
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<p align="center"><a href="https://huggingface.co/spaces/Hanno-Labs/decision-bench-leaderboard"><img src="assets/proof-decisionbench.png" width="760" alt="DecisionBench (eng, v1): 1 bosun-v3.1-1.7b 87.29, 2 bosun-v3.1-0.6b 83.20, 3 imajev-4b 79.65, ahead of glm-5.3-flash, jev-1.13, deepseek-v4.1-flash, gpt-5.6-luna"></a></p>
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<p align="center"><sub>JevBench v1.4.2.2, scored 27 Sep 2026 (<a href="https://benchmarkheaven.com/jev-models">board</a>). Image JevBench v0.1.3, released 28 Sep 2026: #1 of 49, 76.39, ahead of Jev-Omni (<a href="https://benchmarkheaven.com/image-jev-bench">board</a>). DecisionBench (eng, v1): #3 of 56 models, 79.65, ahead of GLM-5.3 Flash (320B), Jev 1.13, DeepSeek V4.1 Flash (552B) and GPT-5.6 Luna; the two above are the benchmark team's own models (<a href="https://huggingface.co/spaces/Hanno-Labs/decision-bench-leaderboard">leaderboard</a>).</sub></p>
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<p align="center">Built by <b>Mohit Garg</b>. I build decision systems like this for operations, support and back-office teams:<br>
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refunds, returns, ticket routing, document and photo checks. <a href="https://mohitgarg.ai"><b>Work with me → mohitgarg.ai</b></a></p>
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<p align="center"><img src="assets/request-listing.png" class="block dark:hidden" alt="imajev-4b checks a listing against its photo: listing.color says red, the photo shows beige shoes; the model names listing.color at 0.999 and the app holds the listing"><img src="assets/request-listing-dark.png" class="hidden dark:block" alt="imajev-4b checks a listing against its photo: listing.color says red, the photo shows beige shoes; the model names listing.color at 0.999 and the app holds the listing"></p>
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imajev-4b is the recommended default: 83.9% on ImajevBench against the 9B's 82.1% at under half the size, and the only size trained in the phase-3 hard-data stage.
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