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Model: "gemma-2"
not much happened today
llama-3-2 llama-3 gemma-2 phi-3-5-mini claude-3-haiku gpt-4o-mini molmo gemini-1.5 gemini meta-ai-fair openai allenai google-deepmind multimodality model-optimization benchmarks ai-safety model-distillation pruning adapter-layers open-source-models performance context-windows mira-murati demis-hassabis ylecun sama
Meta AI released Llama 3.2 models including 1B, 3B text-only and 11B, 90B vision variants with 128K token context length and adapter layers for image-text integration. These models outperform competitors like Gemma 2 and Phi 3.5-mini, and are supported on major platforms including AWS, Azure, and Google Cloud. OpenAI CTO Mira Murati announced her departure. Allen AI released Molmo, an open-source multimodal model family outperforming proprietary systems. Google improved Gemini 1.5 with Flash and Pro models. Meta showcased Project Orion AR glasses and hinted at a Quest 3S priced at $300. Discussions covered new benchmarks for multimodal models, model optimization, and AI safety and alignment.
Llama 3.2: On-device 1B/3B, and Multimodal 11B/90B (with AI2 Molmo kicker)
llama-3-2 llama-3-1 claude-3-haiku gpt-4o-mini molmo-72b molmo-7b gemma-2 phi-3-5 llama-3-2-vision llama-3-2-3b llama-3-2-20b meta-ai-fair ai2 qualcomm mediatek arm ollama together-ai fireworks-ai weights-biases cohere weaviate multimodality vision context-windows quantization model-release tokenization model-performance model-optimization rag model-training instruction-following mira-murati daniel-han
Meta released Llama 3.2 with new multimodal versions including 3B and 20B vision adapters on a frozen Llama 3.1, showing competitive performance against Claude Haiku and GPT-4o-mini. AI2 launched multimodal Molmo 72B and 7B models outperforming Llama 3.2 in vision tasks. Meta also introduced new 128k-context 1B and 3B models competing with Gemma 2 and Phi 3.5, with collaborations hinted with Qualcomm, Mediatek, and Arm for on-device AI. The release includes a 9 trillion token count for Llama 1B and 3B. Partner launches include Ollama, Together AI offering free 11B model access, and Fireworks AI. Additionally, a new RAG++ course from Weights & Biases, Cohere, and Weaviate offers systematic evaluation and deployment guidance for retrieval-augmented generation systems based on extensive production experience.
DataComp-LM: the best open-data 7B model/benchmark/dataset
mistral-nemo-12b gpt-4o-mini deepseek-v2-0628 mistral-7b llama-3 gemma-2 qwen-2 datacomp hugging-face openai nvidia mistral-ai deepseek dataset-design scaling-laws model-benchmarking model-performance fine-tuning multilinguality function-calling context-windows open-source-models model-optimization cost-efficiency benchmarking sam-altman guillaume-lample philschmid miramurati
DataComp team released a competitive 7B open data language model trained on only 2.5T tokens from the massive DCLM-POOL dataset of 240 trillion tokens, showing superior scaling trends compared to FineWeb. OpenAI launched GPT-4o mini, a cost-effective model with 82% MMLU and performance near GPT-4-Turbo, aimed at developers for broad applications. NVIDIA and Mistral jointly released the Mistral NeMo 12B model featuring a 128k token context window, FP8 checkpoint, multilingual support, and Apache 2.0 licensing. DeepSeek announced DeepSeek-V2-0628 as the top open-source model on the LMSYS Chatbot Arena leaderboard with strong rankings in coding, math, and hard prompts. This news highlights advances in dataset design, model efficiency, and open-source contributions in the AI community.
Qdrant's BM42: "Please don't trust us"
claude-3.5-sonnet gemma-2 nano-llava-1.5 qdrant cohere stripe anthropic hugging-face stablequan_ai semantic-search benchmarking dataset-quality model-evaluation model-optimization vision fine-tuning context-windows nils-reimers jeremyphoward hamelhusain rohanpaul_ai
Qdrant attempted to replace BM25 and SPLADE with a new method called "BM42" combining transformer attention and collection-wide statistics for semantic and keyword search, but their evaluation using the Quora dataset was flawed. Nils Reimers from Cohere reran BM42 on better datasets and found it underperformed. Qdrant acknowledged the errors but still ran a suboptimal BM25 implementation. This highlights the importance of dataset choice and evaluation sanity checks in search model claims. Additionally, Stripe faced criticism for AI/ML model failures causing account and payment issues, prompting calls for alternatives. Anthropic revealed that Claude 3.5 Sonnet suppresses some answer parts with backend tags, sparking debate. Gemma 2 model optimizations allow 2x faster fine-tuning with 63% less memory and longer context windows, running up to 34B parameters on consumer GPUs. nanoLLaVA-1.5 was announced as a compact 1B parameter vision model with significant improvements.
GraphRAG: The Marriage of Knowledge Graphs and RAG
gemma-2 llama-3-70b claude-3.5-sonnet nemotron-340b qwen2-72b llama-3 microsoft-research anthropic nvidia hugging-face retrieval-augmented-generation knowledge-graphs token-usage inference-time attention-mechanisms instruction-following coding math long-range-reasoning synthetic-data dataset-release fine-tuning context-windows function-calling travis-fischer rasbt alexandr-wang osanseviero rohanpaul_ai hamelhusain svpino aaaazzam omarsar0
Microsoft Research open sourced GraphRAG, a retrieval augmented generation (RAG) technique that extracts knowledge graphs from sources and clusters them for improved LLM answers, though it increases token usage and inference time. Gemma 2 models were released focusing on efficient small LLMs with innovations like sliding window attention and RMS norm, nearly matching the larger Llama 3 70B. Anthropic's Claude 3.5 Sonnet leads in instruction following and coding benchmarks, while Nvidia's Nemotron 340B model was released in June. Qwen2-72B tops the HuggingFace Open LLM leaderboard excelling in math and long-range reasoning. Discussions on RAG highlighted its limitations and improvements in context usage via function calls. A persona-driven synthetic data generation approach introduced 1 billion personas, with a fine-tuned model matching GPT-4 performance on math benchmarks at 7B scale. The 200GB AutoMathText dataset was also noted for math data synthesis.
That GPT-4o Demo
gpt-4o gemma-2 meta-code-llama openai google-deepmind meta-ai-fair voice-generation ocr screen-sharing vision code-understanding model-customization efficiency textual-intelligence multimodal-agents sft distillation rlhf model-merging model-optimization safety romain-huet fchollet
Romain Huet demonstrated an unreleased version of GPT-4o on ChatGPT Desktop showcasing capabilities like low latency voice generation, whisper tone moderation, camera mode streaming video to GPT-4o, rapid OCR, screen sharing with ChatGPT for programming help, clipboard reading, and vision-based code conversation. OpenAI's four investment areas highlighted include textual intelligence, efficiency/cost, model customization, and multimodal agents. Google DeepMind released Gemma 2 models in 9B and 27B sizes trained on 8T and 13T tokens respectively, using SFT, distillation, RLHF, and model merging, optimized for TPUv5e with strong performance and safety measures. Meta AI announced the Meta LLM Compiler built on Meta Code Llama with enhanced code optimization and compiler features.
Gemma 2: The Open Model for Everyone
gemma-2 qwen-72b mixtral-8x22b-instruct claude-3.5-sonnet google-deepmind alibaba mistral-ai anthropic knowledge-distillation attention-mechanisms multilingual-models multimodality model-training model-optimization memory-optimization fine-tuning kathleen-kenealy daniel-han
Gemma 2, a 27B parameter model from google-deepmind, was released with innovations like 1:1 local-global attention alternation and logit soft-capping, leveraging knowledge distillation to train smaller models on over 50× the compute-optimal token quantity. The model supports multilingual and multimodal capabilities, with fine-tuning success on over 200 Indic language variants. The Open LLM Leaderboard highlights alibaba's Qwen 72B as the top model, with mistral-ai's Mixtral-8x22B-Instruct also ranking highly. Anthropic launched Claude 3.5 Sonnet, improving intelligence at mid-tier cost and speed. Research on eliminating matrix multiplication in LLMs promises significant memory savings without performance loss. Kathleen Kenealy and Daniel Han provided insights on Gemma 2's tokenizer and attention scaling respectively.
Google I/O in 60 seconds
gemini-1.5-pro gemini-flash gemini-ultra gemini-pro gemini-nano gemma-2 llama-3-70b paligemma imagen-3 veo google google-deepmind youtube tokenization model-performance fine-tuning vision multimodality model-release model-training model-optimization ai-integration image-generation watermarking hardware-optimization voice video-understanding
Google announced updates to the Gemini model family, including Gemini 1.5 Pro with 2 million token support, and the new Gemini Flash model optimized for speed with 1 million token capacity. The Gemini suite now includes Ultra, Pro, Flash, and Nano models, with Gemini Nano integrated into Chrome 126. Additional Gemini features include Gemini Gems (custom GPTs), Gemini Live for voice conversations, and Project Astra, a live video understanding assistant. The Gemma model family was updated with Gemma 2 at 27B parameters, offering near-llama-3-70b performance at half the size, plus PaliGemma, a vision-language open model inspired by PaLI-3. Other launches include DeepMind's Veo, Imagen 3 for photorealistic image generation, and a Music AI Sandbox collaboration with YouTube. SynthID watermarking now extends to text, images, audio, and video. The Trillium TPUv6 codename was revealed. Google also integrated AI across its product suite including Workspace, Email, Docs, Sheets, Photos, Search, and Lens. "The world awaits Apple's answer."
Cohere Command R+, Anthropic Claude Tool Use, OpenAI Finetuning
c4ai-command-r-plus claude-3 gpt-3.5-turbo gemini mistral-7b gemma-2 claude-3-5 llama-3 vicuna cohere anthropic openai microsoft stability-ai opera-software meta-ai-fair google-deepmind mistral-ai tool-use multilingual-models rag fine-tuning quantum-computing audio-generation local-inference context-windows model-size-analysis model-comparison
Cohere launched Command R+, a 104B dense model with 128k context length focusing on RAG, tool-use, and multilingual capabilities across 10 key languages. It supports Multi-Step Tool use and offers open weights for research. Anthropic introduced tool use in beta for Claude, supporting over 250 tools with new cookbooks for practical applications. OpenAI enhanced its fine-tuning API with new upgrades and case studies from Indeed, SK Telecom, and Harvey, promoting DIY fine-tuning and custom model training. Microsoft achieved a quantum computing breakthrough with an 800x error rate improvement and the most usable qubits to date. Stability AI released Stable Audio 2.0, improving audio generation quality and control. The Opera browser added local inference support for large language models like Meta's Llama, Google's Gemma, and Vicuna. Discussions on Reddit highlighted Gemini's large context window, analysis of GPT-3.5-Turbo model size, and a battle simulation between Claude 3 and ChatGPT using local 7B models like Mistral and Gemma.