All tags
Topic: "multi-head-attention"
DeepSeek-V2 beats Mixtral 8x22B with >160 experts at HALF the cost
deepseek-v2 llama-3-120b llama-3-400b gpt-4 mistral phi claude gemini mai-1 med-gemini deepseek-ai mistral-ai microsoft openai scale-ai tesla nvidia google-deepmind mixture-of-experts multi-head-attention model-inference benchmarking overfitting robotics teleoperation open-source multimodality hallucination-detection fine-tuning medical-ai model-training erhartford maximelabonne bindureddy adcock_brett drjimfan clementdelangue omarsar0 rohanpaul_ai
DeepSeek V2 introduces a new state-of-the-art MoE model with 236B parameters and a novel Multi-Head Latent Attention mechanism, achieving faster inference and surpassing GPT-4 on AlignBench. Llama 3 120B shows strong creative writing skills, while Microsoft is reportedly developing a 500B parameter LLM called MAI-1. Research from Scale AI highlights overfitting issues in models like Mistral and Phi, whereas GPT-4, Claude, Gemini, and Llama maintain benchmark robustness. In robotics, Tesla Optimus advances with superior data collection and teleoperation, LeRobot marks a move toward open-source robotics AI, and Nvidia's DrEureka automates robot skill training. Multimodal LLM hallucinations are surveyed with new mitigation strategies, and Google's Med-Gemini achieves SOTA on medical benchmarks with fine-tuned multimodal models.