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🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning Both code and weights are available under the MIT license on HuggingFace. 👉 Key details: • Trained from scratch (not a finetune) on proprietary data and infrastructure • Mixture-of-Experts (MoE) architecture Models: 🧠 GigaChat-3.1 Ultra • 702B MoE model for high-performance environments • Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks • Supports FP8 training and MTP ⚡️ GigaChat-3.1 Lightning • 10B model (1.8B active parameters) • Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks • Efficient local inference • Up to 256k context Engineering highlights: • Custom metric to detect and reduce generation loops • DPO training moved to native FP8 • Improvements in post-training pipeline • Identified and fixed a critical issue affecting evaluation quality 🌍 Trained on 14 languages (optimized for English and Russian) Use cases: • chatbots • AI assistants • copilots • internal ML systems Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.

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