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Model: "gemini-1.5-flash"
Olympus has dropped (aka, Amazon Nova Micro|Lite|Pro|Premier|Canvas|Reel)
amazon-nova claude-3 llama-3-70b gemini-1.5-flash gpt-4o amazon anthropic google-deepmind sakana-ai-labs multimodality benchmarking model-merging model-performance model-architecture model-optimization population-based-learning philschmid bindureddy
Amazon announced the Amazon Nova family of multimodal foundation models at AWS Re:Invent, available immediately with no waitlist in configurations like Micro, Lite, Pro, Canvas, and Reel, with Premier and speech-to-speech coming next year. These models offer 2-4x faster token speeds and are 25%-400% cheaper than competitors like Anthropic Claude models, positioning Nova as a serious contender in AI engineering. Pricing undercuts models such as Google DeepMind Gemini Flash 8B, and some Nova models extend context length up to 300k tokens. However, benchmarking controversy exists as some evaluations show Nova scoring below Llama-3 70B in LiveBench AI metrics. Separately, CycleQD was introduced by Sakana AI Labs, using evolutionary computation for population-based model merging to develop niche LLM agents.
not much happened today
aria o1-preview o1-mini gemini-1.5-pro gemini-1.5-flash gemini-1.5 claude-3.5-sonnet rhymes-ai openai anthropic google meta-ai-fair oxylabs multimodality mixture-of-experts long-context retrieval-augmented-generation benchmarking software-engineering llm-evaluation prompt-engineering web-scraping python production-applications mervenoyann osanseviero dbrxmosaicai ylecun ofirpress clefourrier omarsar0 rohanpaul_ai svpino finbarrtimbers _philschmid
Rhymes AI released Aria, a new 25.3B parameter multimodal MoE model supporting text, code, image, and video with a 64k token context window and Apache-2.0 license. OpenAI's o1-preview and o1-mini models show consistent improvement over Anthropic and Google Gemini 1.5 Pro/Flash on long context RAG benchmarks up to 128k tokens, while Google Gemini 1.5 models excel at extreme context lengths up to 2 million tokens. Meta AI expanded rollout to 21 countries with new language support but remains unavailable in the EU. The one-year anniversary of SWE-bench benchmark for software engineering tasks was celebrated, alongside the introduction of SWE-bench Multimodal. New AI tools include OxyCopilot by Oxylabs for web scraping, Taipy for Python-based production apps, and Latitude for prompt engineering. Industry insights highlight changing AI funding dynamics and OpenAI's strategic focus on consumer products like ChatGPT. "all recaps done by Claude 3.5 Sonnet, best of 4 runs."
Cerebras Inference: Faster, Better, AND Cheaper
llama-3.1-8b llama-3.1-70b gemini-1.5-flash gemini-1.5-pro cogvideox-5b mamba-2 rene-1.3b llama-3.1 gemini-1.5 claude groq cerebras cursor google-deepmind anthropic inference-speed wafer-scale-chips prompt-caching model-merging benchmarking open-source-models code-editing model-optimization jeremyphoward sam-altman nat-friedman daniel-gross swyx
Groq led early 2024 with superfast LLM inference speeds, achieving ~450 tokens/sec for Mixtral 8x7B and 240 tokens/sec for Llama 2 70B. Cursor introduced a specialized code edit model hitting 1000 tokens/sec. Now, Cerebras claims the fastest inference with their wafer-scale chips, running Llama3.1-8b at 1800 tokens/sec and Llama3.1-70B at 450 tokens/sec at full precision, with competitive pricing and a generous free tier. Google's Gemini 1.5 models showed significant benchmark improvements, especially Gemini-1.5-Flash and Gemini-1.5-Pro. New open-source models like CogVideoX-5B and Mamba-2 (Rene 1.3B) were released, optimized for consumer hardware. Anthropic's Claude now supports prompt caching, improving speed and cost efficiency. "Cerebras Inference runs Llama3.1 20x faster than GPU solutions at 1/5 the price."
Too Cheap To Meter: AI prices cut 50-70% in last 30 days
gpt-4o gpt-4o-mini llama-3-1-405b mistral-large-2 gemini-1.5-flash deepseek-v2 sonnet-3.5 exaone-3.0 minicpm-v-2.6 claude-3.5 gpt-4o-2024-08-06 llamaindex together-ai deepinfra deepseek-ai mistral-ai google-deepmind lg-ai-research llamaindex llamaindex llamaindex price-cuts context-caching instruction-tuning vision benchmarks pytorch attention-mechanisms reinforcement-learning-from-human-feedback compute-optimal-scaling rohanpaul_ai akhaliq mervenoyann sophiamyang chhillee karpathy
Gemini 1.5 Flash has cut prices by approximately 70%, offering a highly competitive free tier of 1 million tokens per minute at $0.075/mtok, intensifying the AI model price war. Other significant price reductions include GPT-4o (~50% cut to $2.50/mtok), GPT-4o mini (70-98.5% cut to $0.15/mtok), Llama 3.1 405b (46% cut to $2.7/mtok), and Mistral Large 2 (62% cut to $3/mtok). Deepseek v2 introduced context caching, reducing input token costs by up to 90% to $0.014/mtok. New model releases include Llama 3.1 405b, Sonnet 3.5, EXAONE-3.0 (7.8B instruction-tuned by LG AI Research), and MiniCPM V 2.6 (vision-language model combining SigLIP 400M and Qwen2-7B). Benchmarks show Mistral Large performing well on ZebraLogic and Claude-3.5 leading LiveBench. FlexAttention, a new PyTorch API, simplifies and optimizes attention mechanisms. Andrej Karpathy analyzed RLHF, highlighting its limitations compared to traditional reinforcement learning. Google DeepMind research on compute-optimal scaling was also summarized.
Contextual Position Encoding (CoPE)
cope gemini-1.5-flash gemini-1.5-pro claude gpt-3 meta-ai-fair google-deepmind anthropic perplexity-ai langchain openai positional-encoding transformers counting copying language-modeling coding external-memory tool-use model-evaluation inference-speed model-benchmarking scaling research-synthesis jason-weston alexandr-wang karpathy arav-srinivas
Meta AI researcher Jason Weston introduced CoPE, a novel positional encoding method for transformers that incorporates context to create learnable gates, enabling improved handling of counting and copying tasks and better performance on language modeling and coding. The approach can potentially be extended with external memory for gate calculation. Google DeepMind released Gemini 1.5 Flash and Pro models optimized for fast inference. Anthropic announced general availability of tool use for Claude, enhancing its ability to orchestrate tools for complex tasks. Alexandr Wang launched SEAL Leaderboards for private, expert evaluations of frontier models. Karpathy reflected on the 4th anniversary of GPT-3, emphasizing scaling and practical improvements. Perplexity AI launched Perplexity Pages to convert research into visually appealing articles, described as an "AI Wikipedia" by Arav Srinivas.
Skyfall
gemini-1.5-pro gemini-1.5-flash yi-1.5 kosmos-2.5 paligemma falcon-2 deepseek-v2 hunyuan-dit gemini-1.5 gemini-1.5-flash yi-1.5 google-deepmind yi-ai microsoft hugging-face langchain maven multimodality mixture-of-experts transformer model-optimization long-context model-performance model-inference fine-tuning local-ai scaling-laws causal-models hallucination-detection model-distillation model-efficiency hamel-husain dan-becker clement-delangue philschmid osanseviero arankomatsuzaki jason-wei rohanpaul_ai
Between 5/17 and 5/20/2024, key AI updates include Google DeepMind's Gemini 1.5 Pro and Flash models, featuring sparse multimodal MoE architecture with up to 10M context and a dense Transformer decoder that is 3x faster and 10x cheaper. Yi AI released Yi-1.5 models with extended context windows of 32K and 16K tokens. Other notable releases include Kosmos 2.5 (Microsoft), PaliGemma (Google), Falcon 2, DeepSeek v2 lite, and HunyuanDiT diffusion model. Research highlights feature an Observational Scaling Laws paper predicting model performance across families, a Layer-Condensed KV Cache technique boosting inference throughput by up to 26×, and the SUPRA method converting LLMs into RNNs for reduced compute costs. Hugging Face expanded local AI capabilities enabling on-device AI without cloud dependency. LangChain updated its v0.2 release with improved documentation. The community also welcomed a new LLM Finetuning Discord by Hamel Husain and Dan Becker for Maven course users. "Hugging Face is profitable, or close to profitable," enabling $10 million in free shared GPUs for developers.
Chameleon: Meta's (unreleased) GPT4o-like Omnimodal Model
chameleon gpt-4o gemini-1.5-flash claude-3 meta-ai-fair openai google-deepmind anthropic reddit multimodality early-fusion benchmarking model-training tokenization streaming tool-use vision coding hallucination-detection model-performance armen-aghajanyan sama alexandr-wang abacaj alexalbert__
Meta AI FAIR introduced Chameleon, a new multimodal model family with 7B and 34B parameter versions trained on 10T tokens of interleaved text and image data enabling "early fusion" multimodality that can natively output any modality. While reasoning benchmarks are modest, its "omnimodality" approach competes well with pre-GPT4o multimodal models. OpenAI launched GPT-4o, a model excelling in benchmarks like MMLU and coding tasks, with strong multimodal capabilities but some regression in ELO scores and hallucination issues. Google DeepMind announced Gemini 1.5 Flash, a small model with 1M context window and flash performance, highlighting convergence trends between OpenAI and Google models. Anthropic updated Claude 3 with streaming support, forced tool use, and vision tool integration for multimodal knowledge extraction. OpenAI also partnered with Reddit, raising industry attention.
Not much happened today
gpt-4o gemini-1.5-pro gemini-1.5-flash imagen-3 veo reka-core qwen-1.5-110b openai google-deepmind anthropic rekailabs alibaba salesforce multimodality long-context model-releases reinforcement-learning model-benchmarking text-to-image video-generation ai-assistants ilya-sutskever jakub-pachocki mike-krieger sama
Ilya Sutskever steps down as Chief Scientist at OpenAI after nearly a decade, with Jakub Pachocki named as his successor. Google DeepMind announces Gemini 1.5 Pro and Gemini 1.5 Flash models featuring 2 million token context and improved multimodal capabilities, alongside demos of Project Astra AI assistant, Imagen 3 text-to-image model, and Veo generative video model. GPT-4o tops the VHELM leaderboard and outperforms competitors on LMSYS Chatbot Arena. Reka Core multimodal model with 128K context and Alibaba's Qwen1.5-110B open-source model are released. Salesforce shares an online RLHF recipe.