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𝚐𝔪𝟾𝚡𝚡𝟾 pfp
𝚐𝔪𝟾𝚡𝚡𝟾
@gm8xx8
DeepSeek-V3 on HF Reasoning and Coding benchmarks: BigCodeBench-Hard: - 🥇 1st Overall - Complete: 40.5% - Instruct: 28.4% - Average: 34.5% > Comparisons: Gemini-Exp-1206 (34.1%), o1-2024-12-17 (32.8%) Aider Polyglot Leaderboard: -🥈 2nd Place - o1: 62% - DeepSeek-V3 Preview: 48% - Sonnet: 45%, Gemini-Exp-1206: 38%, o1-Mini: 33% SWE-Bench Lite: - Performance: 23.00% Key Architecture Upgrades (V3 vs. V2): > Vocab Size: 129,280 (↑ from 102,400) > Hidden Size: 7,168 (↑ from 4,096) > Layers: 61 (↑ from 30) > Attention Heads: 128 (↑ from 32) > Max Position Embeddings: 4,096 (↑ from 2,048) DeepSeek-V3 represents a significant scale-up from its predecessor, with a massive 685B parameters—substantially larger than the largest LLaMA model (405B). It sets new standards in reasoning and coding, with extensive architectural and performance upgrades across all benchmarks. don’t sleep on deepseek. https://huggingface.co/deepseek-ai/DeepSeek-V3-Base/tree/main
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Danny
@mad-scientist
Wow. It hurts me that hobbiests can't reasonably run the biggest open models at home, but to the next best thing - @venice-ai, when can I try this?
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