unsloth/MiniMax-M2.5-GGUF
Text Generation • 229B • Updated • 77.5k • 227
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unsloth/MiniMax-M2.7-GGUF:PPL and KLD graphs for each quant reacted to SeaWolf-AI's post with 🔥 about 15 hours ago
🧬 Darwin-27B-Opus: 86.9% on GPQA Diamond — World #5, Zero Training
We are excited to share Darwin-27B-Opus, a 27B model that achieved 86.9% on GPQA Diamond — ranking #5 globally on the HuggingFace leaderboard — without a single gradient update.
How? Darwin breeds pretrained models through evolutionary FFN crossbreeding. The father (Qwen3.5-27B) provides the reasoning architecture; the mother (Claude 4.6 Opus Reasoning Distilled) contributes structured chain-of-thought knowledge. CMA-ES automatically discovers optimal per-layer blending ratios — no human tuning required.
The result surpasses the original Qwen3.5-27B (85.5%), GLM-5.1 (744B, 86.2%), and Qwen3.5-122B (86.6%). A 27B model outperforming 744B — with zero training, zero data, one GPU, ~2 hours.
We also confirmed hybrid vigor on Korean benchmarks: Darwin-27B-KR (2nd generation offspring) surpassed both parents on CLIcK, winning 7 out of 11 categories. The evolutionary optimizer independently assigned 93% of FFN from the Korean-specialized mother while preserving 93% of attention from the reasoning-specialized father — autonomously validating our core principle: FFN carries knowledge, Attention carries reasoning.
📊 Public release: 10 days → 300+ community derivatives, 120K+ downloads.
🔗 Links:
Darwin-27B-Opus: https://huggingface.co/FINAL-Bench/Darwin-27B-Opus
article: https://huggingface.co/blog/FINAL-Bench/darwin-gpqa
Darwin Family Collection: https://huggingface.co/collections/FINAL-Bench/darwin-family
If foundation models are raw ore, Darwin is the forge. We are just getting started. 🔥 reacted to SeaWolf-AI's post with 🤯 about 15 hours ago
🧬 Darwin-27B-Opus: 86.9% on GPQA Diamond — World #5, Zero Training
We are excited to share Darwin-27B-Opus, a 27B model that achieved 86.9% on GPQA Diamond — ranking #5 globally on the HuggingFace leaderboard — without a single gradient update.
How? Darwin breeds pretrained models through evolutionary FFN crossbreeding. The father (Qwen3.5-27B) provides the reasoning architecture; the mother (Claude 4.6 Opus Reasoning Distilled) contributes structured chain-of-thought knowledge. CMA-ES automatically discovers optimal per-layer blending ratios — no human tuning required.
The result surpasses the original Qwen3.5-27B (85.5%), GLM-5.1 (744B, 86.2%), and Qwen3.5-122B (86.6%). A 27B model outperforming 744B — with zero training, zero data, one GPU, ~2 hours.
We also confirmed hybrid vigor on Korean benchmarks: Darwin-27B-KR (2nd generation offspring) surpassed both parents on CLIcK, winning 7 out of 11 categories. The evolutionary optimizer independently assigned 93% of FFN from the Korean-specialized mother while preserving 93% of attention from the reasoning-specialized father — autonomously validating our core principle: FFN carries knowledge, Attention carries reasoning.
📊 Public release: 10 days → 300+ community derivatives, 120K+ downloads.
🔗 Links:
Darwin-27B-Opus: https://huggingface.co/FINAL-Bench/Darwin-27B-Opus
article: https://huggingface.co/blog/FINAL-Bench/darwin-gpqa
Darwin Family Collection: https://huggingface.co/collections/FINAL-Bench/darwin-family
If foundation models are raw ore, Darwin is the forge. We are just getting started. 🔥Organizations
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