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CoquelicotsBot
Coquelicots
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A small gift for anyone building or studying foundation models. Most "open" models hand you the weights and stop there. With Aether-7B-5Attn we wanted to hand over the whole thing โ so you can actually learn from it, reproduce it, and build on it: the data recipe, the training code, every hyperparameter, the complete logs, and the intermediate checkpoints. All Apache-2.0, reproducible byte-for-byte. What you can do with it: ๐ Rebuild it from scratch, or fork the recipe for your own model ๐ฌ Study a real heterogeneous-attention MoE โ 49 layers place 5 attention mechanisms on a 7ร7 Latin square, arranged as a clean, attributable ablation ๐ Trace training dynamics across the released checkpoints (110k / 115k / 162k) It's a modest 6.59B model, and an honest one โ the limitations (no KV-cache in this build, small scale) are written right in the card. We're not claiming it's special. If any piece of it saves you time or teaches you something, that's exactly what we hoped for. ๐ค ๐ Full write-up โ [blog] ยท https://huggingface.co/blog/FINAL-Bench/opensource-llm ๐ฆ 5 Attention Base ยท https://huggingface.co/FINAL-Bench/Aether-7B-5Attn ๐ฏ 5 Attention Instruct ยท https://huggingface.co/FINAL-Bench/Aether-7B-5Attn-it ๐ 5 Attention Live demo ยท https://huggingface.co/spaces/FINAL-Bench/Aether-Sovereign-AI ๐ฆ 7 Attention Base ยท https://huggingface.co/FINAL-Bench/Aether-7B-7Attn-base ๐ฆ 11 Attention Base ยท https://huggingface.co/FINAL-Bench/Aether-6B-11Attn-base ๐งฌ Collection ยท https://huggingface.co/collections/FINAL-Bench/aether-foundation-model #opensource #LLM #MoE #reproducibility #Apache2
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Coquelicots/JVC-DATASET12K_JSONL
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Dec 2, 2024
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