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Llama 3.1 Leaks: big bumps to 8B, minor bumps to 70b, and SOTA OSS 405b model
llama-3-1-405b llama-3-8b llama-3-70b llama-3-1-8b gpt-4o gpt-4o-mini claude-3-5 qwen-2 meta-ai-fair openai alibaba multilinguality code-generation context-windows model-training synthetic-data benchmarking reasoning fine-tuning model-performance dataset-release swyx philschmid jjitsev lewtun teknium1 adcock_brett
Llama 3.1 leaks reveal a 405B dense model with 128k context length, trained on 39.3M GPU hours using H100-80GB GPUs, and fine-tuned with over 25M synthetic examples. The model shows significant benchmark improvements, especially for the 8B and 70B variants, with some evals suggesting the 70B outperforms GPT-4o. GPT-4o Mini launched as a cost-efficient variant with strong performance but some reasoning weaknesses. Synthetic datasets like NuminaMath enable models such as Alibaba Qwen 2 to surpass GPT-4o and Claude 3.5 in math competitions. Discussions include reasoning task benchmarks and dataset building for improved reasoning.
The Last Hurrah of Stable Diffusion?
llama-3-8b llama-3 qwen-2 gpt-4 gpt-4o stability-ai togethercompute model-architecture fine-tuning benchmarks dataset-release model-evaluation reasoning model-training retrieval-augmented-generation multimodality emad-mostaque rohanpaul_ai fchollet mikeknoop micahgoldblum teknium1 rasbt percyliang
Stability AI launched Stable Diffusion 3 Medium with models ranging from 450M to 8B parameters, featuring the MMDiT architecture and T5 text encoder for image text rendering. The community has shown mixed reactions following the departure of key researchers like Emad Mostaque. On AI models, Llama 3 8B Instruct shows strong evaluation correlation with GPT-4, while Qwen 2 Instruct surpasses Llama 3 on MMLU benchmarks. The Mixture of Agents (MoA) framework outperforms GPT-4o on AlpacaEval 2.0. Techniques like Spectrum and QLoRA enable efficient fine-tuning with less VRAM. Research on grokking reveals transformers can transition from memorization to generalization through extended training. Benchmark initiatives include the $1M ARC Prize Challenge for AGI progress and LiveBench, a live LLM benchmark to prevent dataset contamination. The Character Codex Dataset offers open data on over 15,000 characters for RAG and synthetic data. The MLX 0.2 tool enhances LLM experience on Apple Silicon Macs with improved UI and faster retrieval-augmented generation.
Not much happened today
gemini-1.5-flashmodel gemini-pro mixtral mamba-2 phi-3-medium phi-3-small gpt-3.5-turbo-0613 llama-3-8b llama-2-70b mistral-finetune twelve-labs livekit groq openai nea nvidia lmsys mistral-ai model-performance prompt-engineering data-curation ai-safety model-benchmarking model-optimization training sequence-models state-space-models daniel-kokotajlo rohanpaul_ai _arohan_ tri_dao _albertgu _philschmid sarahcat21 hamelhusain jachiam0 willdepue teknium1
Twelve Labs raised $50m in Series A funding co-led by NEA and NVIDIA's NVentures to advance multimodal AI. Livekit secured $22m in funding. Groq announced running at 800k tokens/second. OpenAI saw a resignation from Daniel Kokotajlo. Twitter users highlighted Gemini 1.5 FlashModel for high performance at low cost and Gemini Pro ranking #2 in Japanese language tasks. Mixtral models can run up to 8x faster on NVIDIA RTX GPUs using TensorRT-LLM. Mamba-2 model architecture introduces state space duality for larger states and faster training, outperforming previous models. Phi-3 Medium (14B) and Small (7B) models benchmark near GPT-3.5-Turbo-0613 and Llama 3 8B. Prompt engineering is emphasized for unlocking LLM capabilities. Data quality is critical for model performance, with upcoming masterclasses on data curation. Discussions on AI safety include a Frontier AI lab employee letter advocating whistleblower protections and debates on aligning AI to user intent versus broader humanity interests.