All tags
Model: "command-r7b"
Meta Apollo - Video Understanding up to 1 hour, SOTA Open Weights
apollo-1b apollo-3b apollo-7b veo-2 imagen-3 llama-3-70b llama-3b command-r7b llama-1b llama-8b chatgpt meta-ai-fair hugging-face google-deepmind openai figure-ai klarna cohere notion video-understanding scaling-consistency benchmarking temporal-ocr egocentric-perception spatial-perception reasoning video-generation physics-simulation voice-features map-integration language-expansion test-time-compute-scaling humanoid-robots ai-integration search-optimization self-recognition self-preference-bias akhaliq _lewtun clementdelangue adcock_brett rohanpaul_ai swyx shaneguML
Meta released Apollo, a new family of state-of-the-art video-language models available in 1B, 3B, and 7B sizes, featuring "Scaling Consistency" for efficient scaling and introducing ApolloBench, which speeds up video understanding evaluation by 41× across five temporal perception categories. Google Deepmind launched Veo 2, a 4K video generation model with improved physics and camera control, alongside an enhanced Imagen 3 image model. OpenAI globally rolled out ChatGPT search with advanced voice and map features and discussed a potential $2,000/month "ChatGPT Max" tier. Research highlights include achieving Llama 70B performance using Llama 3B via test-time compute scaling and expanding Command R7B language support from 10 to 23 languages. Industry updates feature Figure AI delivering humanoid robots commercially and Klarna reducing workforce through AI. Notion integrated Cohere Rerank for better search. Studies reveal LLMs can recognize their own writing style and show self-preference bias. Discussions note video processing progress outpacing text due to better signal-per-compute and data evaluation.
Meta BLT: Tokenizer-free, Byte-level LLM
byte-latent-transformer llama-3 phi-4 gpt-4o command-r7b meta-ai-fair llamaindex microsoft deepseek-ai openai cohere anthropic tokenization transformer-architecture model-efficiency benchmarking multimodality vision reinforcement-learning model-scaling jailbreaking model-optimization
Meta AI introduces the Byte Latent Transformer (BLT), a tokenizer-free architecture that dynamically forms byte patches for efficient compute allocation, outperforming Llama 3 on benchmarks including the CUTE benchmark. The model was trained on approximately 1 trillion tokens and features a three-block transformer design with local and global components. This approach challenges traditional tokenization and may enable new multimodal capabilities such as direct file interaction without retrieval-augmented generation. Additionally, Microsoft announced the Phi-4 14B parameter model achieving state-of-the-art results on STEM and reasoning benchmarks, surpassing GPT-4o. DeepSeek AI launched new vision-language models based on their MoE architecture with sizes ranging from 1.0B to 27B parameters. OpenAI released a new Projects feature for ChatGPT, and Cohere introduced their smallest and fastest Command R7B model. Anthropic published research on "Best-of-N Jailbreaking" vulnerabilities across text, vision, and audio models. Industry discussion highlights a trend of decreasing frontier LLM sizes, with GPT-4 at approximately 1.8 trillion parameters compared to newer models.