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Person: "bryan-catanzaro"
Nemotron-4-340B: NVIDIA's new large open models, built on syndata, great for syndata
nemotron-4-340b mixtral llama-3 gemini-1.5 gpt-4o mamba-2-hybrid-8b samba-3.8b-instruct dolphin-2.9.3 faro-yi-9b-dpo nvidia hugging-face mistral-ai llamaindex cohere gemini mistral synthetic-data model-alignment reward-models fine-tuning long-context model-scaling inference-speed mixture-of-agents open-source-models model-training instruction-following context-windows philipp-schmid bryan-catanzaro oleksii-kuchaiev rohanpaul_ai cognitivecompai _philschmid 01ai_yi
NVIDIA has scaled up its Nemotron-4 model from 15B to a massive 340B dense model, trained on 9T tokens, achieving performance comparable to GPT-4. The model alignment process uses over 98% synthetic data, with only about 20K human-annotated samples for fine-tuning and reward model training. The synthetic data generation pipeline is open-sourced, including synthetic prompts and preference data generation. The base and instruct versions outperform Mixtral and Llama 3, while the reward model ranks better than Gemini 1.5, Cohere, and GPT-4o. Other notable models include Mamba-2-Hybrid 8B, which is up to 8x faster than Transformers and excels on long-context tasks, Samba-3.8B-instruct for infinite context length with linear complexity, Dolphin-2.9.3 tiny models optimized for low-resource devices, and Faro Yi 9B DPO with a 200K context window running efficiently on 16GB VRAM. The Mixture-of-Agents technique boosts open-source LLMs beyond GPT-4 Omni on AlpacaEval 2.0.
Hybrid SSM/Transformers > Pure SSMs/Pure Transformers
mamba-2-hybrid gpt-4 qwen-72b table-llava-7b nvidia lamini-ai sakana-ai luma-labs mixture-of-experts benchmarking fine-tuning multimodality text-to-video model-performance memory-optimization preference-optimization video-understanding multimodal-tables bryan-catanzaro bindureddy ylecun ctnzr corbtt realsharonzhou andrew-n-carr karpathy _akhaliq omarsar0
NVIDIA's Bryan Catanzaro highlights a new paper on Mamba models, showing that mixing Mamba and Transformer blocks outperforms either alone, with optimal attention below 20%. Mixture-of-Agents (MoA) architecture improves LLM generation quality, scoring 65.1% on AlpacaEval 2.0 versus GPT-4 Omni's 57.5%. The LiveBench AI benchmark evaluates reasoning, coding, writing, and data analysis. A hybrid Mamba-2-Hybrid model with 7% attention surpasses a Transformer on MMLU accuracy, jumping from 50% to 53.6%. GPT-4 performs better at temperature=1. Qwen 72B leads open-source models on LiveBench AI. LaminiAI Memory Tuning achieves 95% accuracy on a SQL agent task, improving over instruction fine-tuning. Sakana AI Lab uses evolutionary strategies for preference optimization. Luma Labs Dream Machine demonstrates advanced text-to-video generation. The MMWorld benchmark evaluates multimodal video understanding, and Table-LLaVa 7B competes with GPT-4V on multimodal table tasks.