All tags
Topic: "long-context"
Kimi K2 - SOTA Open MoE proves that Muon can scale to 15T tokens/1T params
kimi-k2 kimi-k2-1t deepseek-v3 grok-4 devstral-2507 gpt-4.1 sonnet-4 moonshot-ai alibaba tencent deepseek x-ai mistral-ai weights-biases hugging-face mixture-of-experts model-training model-optimization optimizer benchmarking long-context model-performance open-weights model-release yuchenj_uw andrew_n_carr scaling01 novita_labs teknium1 aravsrinivas mparakhin simonw
Moonshot AI has released Kimi K2, a 1 trillion parameter Mixture-of-Experts model trained on 15.5 trillion tokens using the new MuonClip optimizer, achieving state-of-the-art results on benchmarks like SWE-Bench Verified (65.8%) and TAU2 (58.4%). This model is competitive with GPT-4.1 and Sonnet 4 on non-thinking tasks and is available under an MIT license. Meanwhile, xAI announced Grok-4, noted for its "LEAST censored frontier model" status and strong long-context performance but criticized for rushed post-training. Mistral AI updated its Devstral 2507 models with improved performance and cost efficiency. The community is excited about the potential of the MuonClip optimizer, which may surpass the long-standing AdamW optimizer in machine learning.
Grok 4: xAI succeeds in going from 0 to new SOTA LLM in 2 years
grok-4 grok-4-heavy claude-4-opus xai perplexity-ai langchain cursor cline model-releases benchmarking long-context model-pricing model-integration voice performance scaling gpu-optimization elonmusk aravsrinivas igor_babuschkin yuchenj_uw
xAI launched Grok 4 and Grok 4 Heavy, large language models rumored to have 2.4 trillion parameters and trained with 100x more compute than Grok 2 on 100k H100 GPUs. Grok 4 achieved new state-of-the-art results on benchmarks like ARC-AGI-2 (15.9%), HLE (50.7%), and Vending-Bench, outperforming models such as Claude 4 Opus. The model supports a 256K context window and is priced at $3.00/M input tokens and $15.00/M output tokens. It is integrated into platforms like Cursor, Cline, LangChain, and Perplexity Pro/Max. The launch was accompanied by a controversial voice mode and sparked industry discussion about xAI's rapid development pace, with endorsements from figures like Elon Musk and Arav Srinivas.
not much happened today
grok-4 smollm3 t5gemma claude-3.7-sonnet deepseek-r1 langchain openai google-deepmind perplexity xai microsoft huggingface anthropic agentic-ai model-controversy open-source model-release alignment fine-tuning long-context multimodality model-research aravsrinivas clementdelangue _akhaliq
LangChain is nearing unicorn status, while OpenAI and Google DeepMind's Gemini 3 Pro models are launching soon. Perplexity rolls out its agentic browser Comet to waitlists, offering multitasking and voice command features. xAI's Grok-4 update sparked controversy due to offensive outputs, drawing comparisons to Microsoft's Tay bot and resulting in regional blocks. Hugging Face released SmolLM3, a 3B parameter open-source model with state-of-the-art reasoning and long context capabilities. Google introduced T5Gemma encoder-decoder models, a significant update in this model category. Anthropic investigates "alignment faking" in language models, focusing on safety concerns with models like Claude 3.7 Sonnet and DeepSeek-R1. "Grok 3 had high reasoning, Grok 4 has heil reasoning" was a notable user comment on the controversy.
Zuck goes Superintelligence Founder Mode: $100M bonuses + $100M+ salaries + NFDG Buyout?
llama-4 maverick scout minimax-m1 afm-4.5b chatgpt midjourney-v1 meta-ai-fair openai deeplearning-ai essential-ai minimax arcee midjourney long-context multimodality model-release foundation-models dataset-release model-training video-generation enterprise-ai model-architecture moe prompt-optimization sama nat dan ashvaswani clementdelangue amit_sangani andrewyng _akhaliq
Meta AI is reportedly offering 8-9 figure signing bonuses and salaries to top AI talent, confirmed by Sam Altman. They are also targeting key figures like Nat and Dan from the AI Grant fund for strategic hires. Essential AI released the massive 24-trillion-token Essential-Web v1.0 dataset with rich metadata and a 12-category taxonomy. DeepLearning.AI and Meta AI launched a course on Llama 4, featuring new MoE models Maverick (400B) and Scout (109B) with context windows up to 10M tokens. MiniMax open-sourced MiniMax-M1, a long-context LLM with a 1M-token window, and introduced the Hailuo 02 video model. OpenAI rolled out "Record mode" for ChatGPT Pro, Enterprise, and Edu on macOS. Arcee launched the AFM-4.5B foundation model for enterprise. Midjourney released its V1 video model enabling image animation. These developments highlight major advances in model scale, long-context reasoning, multimodality, and enterprise AI applications.
Gemini 2.5 Pro/Flash GA, 2.5 Flash-Lite in Preview
gemini-2.5 gemini-2.5-flash-lite gemini-2.5-flash gemini-2.5-pro gemini-2.5-ultra kimi-dev-72b nanonets-ocr-s ii-medical-8b-1706 jan-nano deepseek-r1 minimax-m1 google moonshot-ai deepseek cognitivecompai kling-ai mixture-of-experts multimodality long-horizon-planning benchmarking coding-performance long-context ocr video-generation model-releases tulsee_doshi oriolvinyalsml demishassabis officiallogank _philschmid swyx sainingxie scaling01 gneubig clementdelangue mervenoyann
Gemini 2.5 models are now generally available, including the new Gemini 2.5 Flash-Lite, Flash, Pro, and Ultra variants, featuring sparse Mixture-of-Experts (MoE) transformers with native multimodal support. A detailed 30-page tech report highlights impressive long-horizon planning demonstrated by Gemini Plays Pokemon. The LiveCodeBench-Pro benchmark reveals frontier LLMs struggle with hard coding problems, while Moonshot AI open-sourced Kimi-Dev-72B, achieving state-of-the-art results on SWE-bench Verified. Smaller specialized models like Nanonets-OCR-s, II-Medical-8B-1706, and Jan-nano show competitive performance, emphasizing that bigger models are not always better. DeepSeek-r1 ties for #1 in WebDev Arena, and MiniMax-M1 sets new standards in long-context reasoning. Kling AI demonstrated video generation capabilities.
not much happened today
deepseek-r1-0528 o3 gemini-2.5-pro claude-opus-4 deepseek_ai openai gemini meta-ai-fair anthropic x-ai ollama hugging-face alibaba bytedance xiaomi reasoning reinforcement-learning benchmarking quantization local-inference model-evaluation open-weights transparency post-training agentic-benchmarks long-context hallucination-detection teortaxestex wenfeng danielhanchen awnihannun reach_vb abacaj
DeepSeek R1-0528 release brings major improvements in reasoning, hallucination reduction, JSON output, and function calling, matching or surpassing closed models like OpenAI o3 and Gemini 2.5 Pro on benchmarks such as Artificial Analysis Intelligence Index, LiveBench, and GPQA Diamond. The model ranks #2 globally in open weights intelligence, surpassing Meta AI, Anthropic, and xAI. Open weights and technical transparency have fueled rapid adoption across platforms like Ollama and Hugging Face. Chinese AI labs including DeepSeek, Alibaba, ByteDance, and Xiaomi now match or surpass US labs in model releases and intelligence, driven by open weights strategies. Reinforcement learning post-training is critical for intelligence gains, mirroring trends seen at OpenAI. Optimized quantization techniques (1-bit, 4-bit) and local inference enable efficient experimentation on consumer hardware. New benchmarks like LisanBench test knowledge, planning, memory, and long-context reasoning, with OpenAI o3 and Claude Opus 4 leading. Discussions highlight concerns about benchmark contamination and overemphasis on RL-tuned gains.
not much happened today
deepseek-r1-0528 pali-gemma-2 gemma-3 shieldgemma-2 txgemma gemma-3-qat gemma-3n-preview medgemma dolphingemma signgemma claude-4 opus-4 claude-sonnet-4 codestral-embed bagel qwen nemotron-cortexa gemini-2.5-pro deepseek-ai huggingface gemma claude bytedance qwen nemotron sakana-ai-labs benchmarking model-releases multimodality code-generation model-performance long-context reinforcement-learning model-optimization open-source yuchenj_uw _akhaliq clementdelangue osanseviero alexalbert__ guillaumelample theturingpost lmarena_ai epochairesearch scaling01 nrehiew_ ctnzr
DeepSeek R1 v2 model released with availability on Hugging Face and inference partners. The Gemma model family continues prolific development including PaliGemma 2, Gemma 3, and others. Claude 4 and its variants like Opus 4 and Claude Sonnet 4 show top benchmark performance, including new SOTA on ARC-AGI-2 and WebDev Arena. Codestral Embed introduces a 3072-dimensional code embedder. BAGEL, an open-source multimodal model by ByteDance, supports reading, reasoning, drawing, and editing with long mixed contexts. Benchmarking highlights include Nemotron-CORTEXA topping SWEBench and Gemini 2.5 Pro performing on VideoGameBench. Discussions on random rewards effectiveness focus on Qwen models. "Opus 4 NEW SOTA ON ARC-AGI-2. It's happening - I was right" and "Claude 4 launch has dev moving at a different pace" reflect excitement in the community.
Qwen 3: 0.6B to 235B MoE full+base models that beat R1 and o1
qwen-3 qwen3-235b-a22b qwen3-30b-a3b deepseek-r1 o1 o3-mini grok-3 gemini-2.5-pro alibaba google-deepmind deepseek mistral-ai mixture-of-experts reinforcement-learning benchmarking model-release model-architecture long-context multi-agent-systems inference dataset-release awnihannun prince_canuma actuallyisaak oriolvinyalsml iscienceluvr reach_vb teortaxestex omarsar0
Qwen 3 has been released by Alibaba featuring a range of models including two MoE variants, Qwen3-235B-A22B and Qwen3-30B-A3B, which demonstrate competitive performance against top models like DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro. The models introduce an "enable_thinking=True" mode with advanced soft switching for inference scaling. The release is notable for its Apache 2.0 license and broad inference platform support including MCP. The dataset improvements and multi-stage RL post-training contribute to performance gains. Meanwhile, Gemini 2.5 Pro from Google DeepMind shows strong coding and long-context reasoning capabilities, and DeepSeek R2 is anticipated soon. Twitter discussions highlight Qwen3's finegrained MoE architecture, large context window, and multi-agent system applications.
gpt-image-1 - ChatGPT's imagegen model, confusingly NOT 4o, now available in API
gpt-image-1 o3 o4-mini gpt-4.1 eagle-2.5-8b gpt-4o qwen2.5-vl-72b openai nvidia hugging-face x-ai image-generation content-moderation benchmarking long-context multimodality model-performance supercomputing virology video-understanding model-releases kevinweil lmarena_ai _philschmid willdepue arankomatsuzaki epochairesearch danhendrycks reach_vb mervenoyann _akhaliq
OpenAI officially launched the gpt-image-1 API for image generation and editing, supporting features like alpha channel transparency and a "low" content moderation policy. OpenAI's models o3 and o4-mini are leading in benchmarks for style control, math, coding, and hard prompts, with o3 ranking #1 in several categories. A new benchmark called Vending-Bench reveals performance variance in LLMs on extended tasks. GPT-4.1 ranks in the top 5 for hard prompts and math. Nvidia's Eagle 2.5-8B matches GPT-4o and Qwen2.5-VL-72B in long-video understanding. AI supercomputer performance doubles every 9 months, with xAI's Colossus costing an estimated $7 billion and the US dominating 75% of global performance. The Virology Capabilities Test shows OpenAI's o3 outperforms 94% of expert virologists. Nvidia also released the Describe Anything Model (DAM), a multimodal LLM for detailed image and video captioning, now available on Hugging Face.
not much happened today
nemotron-h nvidia-eagle-2.5 gpt-4o qwen2.5-vl-72b gemini-2.5-flash gemini-2.0-pro gemini-exp-1206 gemma-3 qwen2.5-32b deepseek-r1-zero-32b uni3c seedream-3.0 adobe-dragon kimina-prover qwen2.5-72b bitnet-b1.58-2b4t nvidia deepseek hugging-face alibaba bytedance adobe transformers model-optimization multimodality long-context reinforcement-learning torch-compile image-generation diffusion-models distributional-rewards model-efficiency model-training native-quantization sampling-techniques philschmid arankomatsuzaki osanseviero iScienceLuvr akhaliq
Nemotron-H model family introduces hybrid Mamba-Transformer models with up to 3x faster inference and variants including 8B, 56B, and a compressed 47B model. Nvidia Eagle 2.5 is a frontier VLM for long-context multimodal learning, matching GPT-4o and Qwen2.5-VL-72B on long-video understanding. Gemini 2.5 Flash shows improved dynamic thinking and cost-performance, outperforming previous Gemini versions. Gemma 3 now supports torch.compile for about 60% faster inference on consumer GPUs. SRPO using Qwen2.5-32B surpasses DeepSeek-R1-Zero-32B on benchmarks with reinforcement learning only. Alibaba's Uni3C unifies 3D-enhanced camera and human motion controls for video generation. Seedream 3.0 by ByteDance is a bilingual image generation model with high-resolution outputs up to 2K. Adobe DRAGON optimizes diffusion generative models with distributional rewards. Kimina-Prover Preview is an LLM trained with reinforcement learning from Qwen2.5-72B, achieving 80.7% pass@8192 on miniF2F. BitNet b1.58 2B4T is a native 1-bit LLM with 2B parameters trained on 4 trillion tokens, matching full-precision LLM performance with better efficiency. Antidistillation sampling counters unwanted model distillation by modifying reasoning traces from frontier models.
GPT 4.1: The New OpenAI Workhorse
gpt-4.1 gpt-4.1-mini gpt-4.1-nano gpt-4o gemini-2.5-pro openai llama-index perplexity-ai google-deepmind coding instruction-following long-context benchmarks model-pricing model-integration model-deprecation sama kevinweil omarsar0 aidan_mclau danhendrycks polynoamial scaling01 aravsrinivas lmarena_ai
OpenAI released GPT-4.1, including GPT-4.1 mini and GPT-4.1 nano, highlighting improvements in coding, instruction following, and handling long contexts up to 1 million tokens. The model achieves a 54 score on SWE-bench verified and shows a 60% improvement over GPT-4o on internal benchmarks. Pricing for GPT-4.1 nano is notably low at $0.10/1M input and $0.40/1M output. GPT-4.5 Preview is being deprecated in favor of GPT-4.1. Integration support includes Llama Index with day 0 support. Some negative feedback was noted for GPT-4.1 nano. Additionally, Perplexity's Sonar API ties with Gemini-2.5 Pro for the top spot in the LM Search Arena leaderboard. New benchmarks like MRCR and GraphWalks were introduced alongside updated prompting guides and cookbooks.
not much happened today
gpt-4o deepseek-v3 claude-3.7-sonnet o3-mini gemini-2.5-pro openai deepseek anthropic google-deepmind togethercompute hypertecgroup coreweave cursor-ai windsurf-ai coding instruction-following image-generation policy-compliance long-context audio-processing video-processing gpu-clusters ai-infrastructure api-access sama kevinweil joannejang nrehiew_ giffmana _philschmid scaling01 saranormous
GPT-4o was praised for its improved coding, instruction following, and freedom, becoming the leading non-reasoning coding model surpassing DeepSeek V3 and Claude 3.7 Sonnet in coding benchmarks, though it still lags behind reasoning models like o3-mini. Concerns about policy compliance in image generation were noted, with efforts to improve adherence. Gemini 2.5 Pro was highlighted for its advanced audio and video understanding, long context capabilities, and integration with platforms like Cursor AI and Windsurf AI. AI infrastructure developments include a partnership between Together AI and Hypertec Group to deliver large-scale GPU clusters, and CoreWeave's IPO was celebrated for advancing AI infrastructure. GPU and TPU usage is expected to increase significantly. "GPT-4o's transparency and background generation feature" and "Gemini 2.5 Pro scored above 50% on Simple-Bench AI Explanation" were key highlights.
LLaDA: Large Language Diffusion Models
llada-8b llama-3-8b step-video-t2v-30b step-audio-chat-132b llama-2-7b stepfun-ai scale-ai cambridge llamaindex diffusion-models text-generation multimodality video-generation voice-processing benchmarking instruction-following model-scaling gpu-usage long-context multi-turn-dialogue arankomatsuzaki _akhaliq omarsar0 iscienceluvr gallabytes maximelabonne reach_vb
LLaDA (Large Language Diffusion Model) 8B is a breakthrough diffusion-based language model that rivals LLaMA 3 8B while training on 7x fewer tokens (2 trillion tokens) and using 0.13 million H800 GPU hours. It introduces a novel text generation approach by predicting uniformly masked tokens in a diffusion process, enabling multi-turn dialogue and instruction-following. Alongside, StepFun AI released two major models: Step-Video-T2V 30B, a text-to-video model generating up to 204 frames with high coherence and motion quality, and Step-Audio-Chat 132B, a voice-to-voice model. Additionally, challenging multimodal benchmarks like Scale AI's EnigmaEval and Cambridge's ZeroBench highlight current frontier models scoring zero, emphasizing the difficulty of these tasks. The community also noted the return of diffusion models in language modeling, a previously speculative architecture now scaled successfully.
Project Stargate: $500b datacenter (1.7% of US GDP) and Gemini 2 Flash Thinking 2
gemini-2.0-flash deepseek-r1 qwen-32b openai softbank oracle arm microsoft nvidia huggingface deepseek-ai long-context quantization code-interpretation model-distillation open-source agi-research model-performance memory-optimization noam-shazeer liang-wenfeng
Project Stargate, a US "AI Manhattan project" led by OpenAI and Softbank, supported by Oracle, Arm, Microsoft, and NVIDIA, was announced with a scale comparable to the original Manhattan project costing $35B inflation adjusted. Despite Microsoft's reduced role as exclusive compute partner, the project is serious but not immediately practical. Meanwhile, Noam Shazeer revealed a second major update to Gemini 2.0 Flash Thinking, enabling 1M token long context usable immediately. Additionally, AI Studio introduced a new code interpreter feature. On Reddit, DeepSeek R1, a distillation of Qwen 32B, was released for free on HuggingChat, sparking discussions on self-hosting, performance issues, and quantization techniques. DeepSeek's CEO Liang Wenfeng highlighted their focus on fundamental AGI research, efficient MLA architecture, and commitment to open-source development despite export restrictions, positioning DeepSeek as a potential alternative to closed-source AI trends.
Titans: Learning to Memorize at Test Time
minimax-01 gpt-4o claude-3.5-sonnet internlm3-8b-instruct transformer2 google meta-ai-fair openai anthropic langchain long-context mixture-of-experts self-adaptive-models prompt-injection agent-authentication diffusion-models zero-trust-architecture continuous-adaptation vision agentic-systems omarsar0 hwchase17 abacaj hardmaru rez0__ bindureddy akhaliq saranormous
Google released a new paper on "Neural Memory" integrating persistent memory directly into transformer architectures at test time, showing promising long-context utilization. MiniMax-01 by @omarsar0 features a 4 million token context window with 456B parameters and 32 experts, outperforming GPT-4o and Claude-3.5-Sonnet. InternLM3-8B-Instruct is an open-source model trained on 4 trillion tokens with state-of-the-art results. Transformer² introduces self-adaptive LLMs that dynamically adjust weights for continuous adaptation. Advances in AI security highlight the need for agent authentication, prompt injection defenses, and zero-trust architectures. Tools like Micro Diffusion enable budget-friendly diffusion model training, while LeagueGraph and Agent Recipes support open-source social media agents.
ModernBert: small new Retriever/Classifier workhorse, 8k context, 2T tokens,
modernbert gemini-2.0-flash-thinking o1 llama answerdotai lightonio hugging-face google-deepmind openai meta-ai-fair figure encoder-only-models long-context alternating-attention natural-language-understanding reasoning robotics-simulation physics-engine humanoid-robots model-performance model-releases jeremyphoward alec-radford philschmid drjimfan bindureddy
Answer.ai/LightOn released ModernBERT, an updated encoder-only model with 8k token context, trained on 2 trillion tokens including code, with 139M/395M parameters and state-of-the-art performance on retrieval, NLU, and code tasks. It features Alternating Attention layers mixing global and local attention. Gemini 2.0 Flash Thinking debuted as #1 in Chatbot Arena, and the O1 model scored top in reasoning benchmarks. Llama downloads surpassed 650 million, doubling in 3 months. OpenAI launched desktop app integrations with voice capabilities. Figure delivered its first humanoid robots commercially. Advances in robotics simulation and a new physics engine Genesis claiming 430,000x faster than real-time were highlighted.
not much happened today
llama mistral openai decagon sierra togethercompute vertical-saas funding protein-structure-prediction lora self-supervised-learning model-optimization neural-architecture-search model-evaluation ethics transformers multi-agent-systems long-context mira-murati demis-hassabis clement-delangue john-o-whitaker yann-lecun francois-chollet ajeya-cotra rohan-paul adcock-brett
Vertical SaaS agents are gaining rapid consensus as the future of AI applications, highlighted by Decagon's $100m funding and Sierra's $4b round. OpenAI alumni are actively raising venture capital and forming new startups, intensifying competition in the AI market. Demis Hassabis celebrated the Nobel Prize recognition for AlphaFold2, a breakthrough in protein structure prediction. Advances in AI models include techniques like LoRA projectors and annealing on high-quality data, while discussions emphasize the need for high-bandwidth sensory inputs beyond language for common sense learning. New methods like LoLCATs aim to optimize transformer models such as Llama and Mistral for efficiency. Ethical concerns about AI agents performing harmful tasks remain under investigation. The AI community continues to explore model evaluation challenges and optimization frameworks like LPZero for neural architecture search.
Not much (in AI) happened this weekend
llama-3.1-8b llama-3.2 chatgpt movie-gen openai meta-ai-fair google-deepmind microsoft x-ai spacex harvard nvidia long-context feature-prediction-loss ai-agents privacy text-to-video text-to-image humanoid-robots gpu-deployment media-foundation-models ai-research-labs sam-altman yann-lecun rasbt bindureddy andrej-karpathy soumithchintala svpino adcock_brett rohanpaul_ai
OpenAI introduced an "edit this area" feature for image generation, praised by Sam Altman. Yann LeCun highlighted a NYU paper improving pixel generation with feature prediction loss using pre-trained visual encoders like DINOv2. Long-context LLMs such as llama-3.1-8b and llama-3.2 variants now support up to 131k tokens, offering alternatives to RAG systems. Bindu Reddy announced AI agents capable of building and deploying code from English instructions, signaling AI's replacement of SQL and potential impact on Python. SpaceX's successful Starship rocket catch was celebrated by Andrej Karpathy and others, with Soumith Chintala praising SpaceX's efficient, low-bureaucracy research approach. Privacy concerns arose from Harvard students' AI glasses, I-XRAY, which can reveal personal information. Meta AI FAIR's Movie Gen model advances media foundation models with high-quality text-to-image and video generation, including synced audio. Humanoid robots like Ameca and Azi now engage in expressive conversations using ChatGPT. xAI rapidly deployed 100K Nvidia H100 GPUs in 19 days, with CEO Jensen Huang commending Elon Musk. Leading AI research labs compared include Meta-FAIR, Google DeepMind, and Microsoft Research. Skepticism about LLM intelligence was voiced by Sam Pino, emphasizing limitations in novel problem-solving despite strong memorization.
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."
a calm before the storm
o1 o1-mini qwen2.5 gpt-4 llama-2-70b llama-7b anthropic openai alibaba microsoft blackrock groq aramco disney eth-zurich pudu-robotics slack long-context kv-cache-quantization diffusion-models reinforcement-learning robotics ai-integration multilinguality model-benchmarking model-performance model-optimization adcock_brett philschmid rohanpaul_ai jvnixon kateclarktweets sama
Anthropic is raising funds at a valuation up to $40 billion ahead of anticipated major releases. OpenAI launched new reasoning models o1 and o1-mini, with increased rate limits and a multilingual MMLU benchmark. Alibaba released the open-source Qwen2.5 model supporting 29+ languages, showing competitive performance to gpt-4 at lower cost. Microsoft and Blackrock plan to invest $30 billion in AI data centers, with Groq partnering with Aramco to build the world's largest AI inference center. Robotics advances include Disney Research and ETH Zurich's diffusion-based motion generation for robots and Pudu Robotics' semi-humanoid robot. Slack and Microsoft introduced AI-powered agents integrated into their platforms. Research highlights include long-context scaling for llama-2-70b using Dual Chunk Attention and KV cache quantization enabling 1 million token context on llama-7b models.
not much happened today
llama-3 o1 deepseek-2.5 gpt-4 claude-3.5-sonnet 3dtopia-xl cogvideox anthropic meta-ai-fair openai deepseek-ai llamaindex langchainai retrieval-augmented-generation prompt-caching multimodality multi-agent-systems reasoning diffusion-models image-to-video prompting enterprise-ai agentic-ai long-context model-evaluation caching model-cost-efficiency
Anthropic introduced a RAG technique called Contextual Retrieval that reduces retrieval failure rates by 67% using prompt caching. Meta is teasing multimodal Llama 3 ahead of Meta Connect. OpenAI is hiring for a multi-agent research team focusing on improved AI reasoning with their o1 models, which have sparked mixed reactions. DeepSeek 2.5 is noted as a cost-effective alternative to GPT-4 and Claude 3.5 sonnet. New models like 3DTopia-XL for 3D asset generation and CogVideoX for image-to-video conversion were highlighted. Techniques to boost reasoning by re-reading questions and combining retrieval with prompt caching were shared. Industry insights emphasize the necessity of AI adoption in enterprises and the disruption of traditional ML businesses. Tools like LangChainAI's LangGraph Templates and LlamaIndex's LlamaParse Premium enhance agentic applications and multimodal content extraction. Discussions on LLM evals and caching highlight production challenges and improvements. "Companies not allowing developers to use AI are unlikely to succeed" was a key sentiment.
Everybody shipped small things this holiday weekend
gpt-4o-voice gemini claude jamba-1.5 mistral-nemo-minitron-8b xai google anthropic openai cognition ai21-labs nvidia langchain fine-tuning long-context parameter-efficient-fine-tuning latex-rendering real-time-audio virtual-try-on resource-tags low-code ai-agents workspace-organization model-benchmarking dario-amodei scott-wu fchollet svpino
xAI announced the Colossus 100k H100 cluster capable of training an FP8 GPT-4 class model in 4 days. Google introduced Structured Output for Gemini. Anthropic discussed Claude's performance issues possibly due to API prompt modifications. OpenAI enhanced controls for File Search in their Assistants API. Cognition and Anthropic leaders appeared on podcasts. The viral Kwai-Kolors virtual try-on model and the open-source real-time audio conversational model Mini-Omni (similar to gpt-4o-voice) were released. Tutorials on parameter-efficient fine-tuning with LoRA and QLoRA, long-context embedding challenges, and Claude's LaTeX rendering feature were highlighted. AI21 Labs released Jamba 1.5 models with a 256K context window and faster long-context performance. NVIDIA debuted Mistral-Nemo-Minitron-8B on the Open LLM Leaderboard. LangChain introduced resource tags for workspace organization, and a low-code AI app toolkit was shared by svpino. Legal AI agents and financial agent evaluations using LangSmith were also featured.
not much happened today
llama-3-1 claude-3-5-sonnet llama-3-1-405b ltm-2-mini qwen2-vl gpt-4o-mini meta-ai-fair hugging-face magic-ai-labs lmsys alibaba openai long-context style-control multimodality ai-safety model-evaluation web-crawling pdf-processing ai-hype-cycles call-center-automation sam-altman ajeya-cotra fchollet rohanpaul_ai philschmid
Meta announced significant adoption of LLaMA 3.1 with nearly 350 million downloads on Hugging Face. Magic AI Labs introduced LTM-2-Mini, a long context model with a 100 million token context window, and a new evaluation method called HashHop. LMSys added style control to their Chatbot Arena leaderboard, improving rankings for models like Claude 3.5 Sonnet and LLaMA 3.1 405B. Alibaba released Qwen2-VL, a multimodal LLM under Apache 2.0 license, competitive with GPT-4o mini. OpenAI CEO Sam Altman announced collaboration with the US AI Safety Institute for pre-release model testing. Discussions on AI safety and potential AI takeover risks were highlighted by Ajeya Cotra. Tools like firecrawl for web crawling and challenges in PDF processing were noted. AI hype cycles and market trends were discussed by François Chollet, and potential AI disruption in call centers was shared by Rohan Paul.
Summer of Code AI: $1.6b raised, 1 usable product
ltm-2 llama-3-1-405b gemini-advanced cognition poolside codeium magic google-deepmind nvidia google-cloud long-context model-efficiency custom-hardware cuda training-stack gpu-scaling neural-world-models diffusion-models quantization nat-friedman ben-chess rohan-paul
Code + AI is emphasized as a key modality in AI engineering, highlighting productivity and verifiability benefits. Recent major funding rounds include Cognition AI raising $175M, Poolside raising $400M, Codeium AI raising $150M, and Magic raising $320M. Magic announced their LTM-2 model with a 100 million token context window, boasting efficiency improvements over Llama 3.1 405B by about 1000x cheaper in sequence-dimension algorithm and drastically lower memory requirements. Magic's stack is built from scratch with custom CUDA and no open-source foundations, partnered with Google Cloud and powered by NVIDIA H100 and GB200 GPUs, aiming to scale to tens of thousands of GPUs. Google DeepMind revealed updates to Gemini Advanced with customizable expert "Gems." Neural Game Engines like GameNGen can run DOOM in a diffusion model trained on 0.9B frames. The content also references LLM quantization research by Rohan Paul.
CogVideoX: Zhipu's Open Source Sora
cogvideox llama-3-1 llama-3-405b moondream phi-3.5 llama-rank zhipu-ai alibaba meta-ai-fair google hugging-face nvidia togethercompute salesforce video-generation serverless-computing vision document-vqa text-vqa mixture-of-experts retrieval-augmented-generation long-context model-routing webgpu background-removal long-form-generation superposition-prompting rohanpaul_ai philschmid vikhyatk algo_diver jayalammar davidsholz
Zhipu AI, Alibaba's AI arm and China's 3rd largest AI lab, released the open 5B video generation model CogVIdeoX, which can run without GPUs via their ChatGLM web and desktop apps. Meta AI announced trust & safety research and CyberSecEval 3 alongside the release of Llama 3.1, with Llama 3 405B now available serverless on Google Cloud Vertex AI and Hugging Face x NVIDIA NIM API. Updates include Moondream, an open vision-language model improving DocVQA and TextVQA tasks, and the lightweight MoE chat model Phi-3.5 with 16x3.8B parameters. Together Compute introduced the Rerank API featuring Salesforce's LlamaRank model for document and code ranking. Research highlights include superposition prompting for RAG without fine-tuning, the AgentWrite pipeline for long-form content generation over 20,000 words, and a comparison showing Long Context methods outperform RAG at higher costs. Tools include Not Diamond, an AI model router, AI command line interfaces, and an open-source WebGPU background removal tool. "You don't even need GPUs to run it," referring to CogVIdeoX.
not much happened this weekend
jamba-1.5 dream-machine-1.5 ideogram-v2 mistral-nemo-minitron-8b mistral-7b llama-3-8b nous-research cursor-ai gdm george-hotz agibot unitree eth-zurich disney uc-san-diego ai21-labs luma-labs ideogram nvidia mistral-ai meta-ai-fair distributed-ai optimizer inter-gpu-communication low-latency-training open-source humanoid-robots robotics physics-based-motion teleoperation multilingual-models long-context text-to-video text-to-image model-performance george-hotz adcock_brett aman
Nous Research announced DisTrO, a new optimizer that drastically reduces inter-GPU communication by 1000x to 10,000x enabling efficient training on slow networks, offering an alternative to GDM's DiLoCo. Cursor AI gained viral attention from an 8-year-old user and announced a new fundraise, with co-host Aman returning to their podcast. George Hotz launched tinybox for sale. In robotics, AGIBOT revealed 5 new humanoid robots with open-source plans, and Unitree showcased its G1 humanoid robot nearing mass production at $16,000. ETH Zurich and Disney developed an AI system for physics-based robot motion generation from text or images. UC San Diego released ACE, an open-source teleoperation system for controlling multiple robots. AI21 Labs unveiled Jamba 1.5, a multilingual model with 256k context length and permissive licensing. Luma Labs released Dream Machine 1.5 for improved text-to-video generation. Ideogram launched v2 of its text-to-image model with near-perfect text generation. Nvidia and Mistral released Mistral-NeMo-Minitron 8B, a small model outperforming Mistral-7B and llama-3-8b on the Open LLM leaderboard.
Nvidia Minitron: LLM Pruning and Distillation updated for Llama 3.1
llama-3-1-8b llama-3-1 jamba-1.5 claude-3 dracarys-70b dracarys-72b mistral-nemo-minitron-8b mistral-7b nvidia meta-ai-fair ai21-labs anthropic hugging-face pruning knowledge-distillation weight-pruning activation-based-pruning width-pruning kl-divergence teacher-correction prompt-optimization multilinguality long-context mixture-of-experts model-fine-tuning
Nvidia and Meta researchers updated their Llama 3 results with a paper demonstrating the effectiveness of combining weight pruning and knowledge distillation to reduce training costs by training only the largest model from scratch and deriving smaller models via pruning and distillation. The process involves teacher correction, activation-based pruning (favoring width pruning), and retraining with distillation using KL Divergence loss, resulting in better-performing models at comparable sizes. However, distillation incurs some accuracy tradeoffs. Additionally, AI21 Labs launched Jamba 1.5, a hybrid SSM-Transformer MoE model with large context windows and multilingual support. Anthropic updated Claude 3 with LaTeX rendering and prompt caching. An open-source coding-focused LLM, Dracarys, was released in 70B and 72B sizes, showing improved coding performance. The Mistral Nemo Minitron 8B model outperforms Llama 3.1 8B and Mistral 7B on the Hugging Face leaderboard, highlighting pruning and distillation benefits. Research on prompt optimization reveals the complexity of prompt search spaces and the surprising effectiveness of simple algorithms like AutoPrompt/GCG.
super quiet day
jamba-1.5 phi-3.5 dracarys llama-3-1-70b llama-3-1 ai21-labs anthropic stanford hugging-face langchain qdrant aws elastic state-space-models long-context benchmarking ai-safety virtual-environments multi-agent-systems resource-management community-engagement model-performance bindu-reddy rohanpaul_ai jackclarksf danhendrycks reach_vb iqdotgraph
AI21 Labs released Jamba 1.5, a scaled-up State Space Model optimized for long context windows with 94B parameters and up to 2.5X faster inference, outperforming models like Llama 3.1 70B on benchmarks. The Phi-3.5 model was praised for its safety and performance, while Dracarys, a new 70B open-source coding model announced by Bindu Reddy, claims superior benchmarks over Llama 3.1 70B. Discussions on California's SB 1047 AI safety legislation involve Stanford and Anthropic, highlighting a balance between precaution and industry growth. Innovations include uv virtual environments for rapid setup, LangChain's LangSmith resource tags for project management, and multi-agent systems in Qdrant enhancing data workflows. Community events like the RAG workshop by AWS, LangChain, and Elastic continue to support AI learning and collaboration. Memes remain a popular way to engage with AI industry culture.
Gemini Live
gemini-1.5-pro genie falcon-mamba gemini-1.5 llamaindex google anthropic tii supabase perplexity-ai llamaindex openai hugging-face multimodality benchmarking long-context retrieval-augmented-generation open-source model-releases model-integration model-performance software-engineering linear-algebra hugging-face-hub debugging omarsar0 osanseviero dbrxmosaicai alphasignalai perplexity_ai _jasonwei svpino
Google launched Gemini Live on Android for Gemini Advanced subscribers during the Pixel 9 event, featuring integrations with Google Workspace apps and other Google services. The rollout began on 8/12/2024, with iOS support planned. Anthropic released Genie, an AI software engineering system achieving a 57% improvement on SWE-Bench. TII introduced Falcon Mamba, a 7B attention-free open-access model scalable to long sequences. Benchmarking showed that longer context lengths do not always improve Retrieval-Augmented Generation. Supabase launched an AI-powered Postgres service dubbed the "ChatGPT of databases," fully open source. Perplexity AI partnered with Polymarket to integrate real-time probability predictions into search results. A tutorial demonstrated a multimodal recipe recommender using Qdrant, LlamaIndex, and Gemini. An OpenAI engineer shared success tips emphasizing debugging and hard work. The connection between matrices and graphs in linear algebra was highlighted for insights into nonnegative matrices and strongly connected components. Keras 3.5.0 was released with Hugging Face Hub integration for model saving and loading.
Llama 3.1: The Synthetic Data Model
llama-3-405b llama-3-1 llama-3 meta-ai-fair groq fireworks synthetic-data fine-tuning reinforcement-learning multilinguality long-context tool-use code-generation math model-licensing inference-speed model-deployment bindureddy thomas
Meta AI has released Llama 3.1, including a 405B parameter model that triggers regulatory considerations like the EU AI Act and SB 1047. The model incorporates extensive synthetic data techniques for code, math, multilinguality, long context, and tool use fine-tuning, with RLHF using synthetic preference data from Llama 2. The launch was coordinated across major inference providers, with Groq demonstrating 750 tokens per second inference speed and Fireworks leading in pricing. The updated license explicitly allows synthetic data generation, marking a significant step in open frontier-class LLMs and cost-efficiency improvements since March.
Mini, Nemo, Turbo, Lite - Smol models go brrr (GPT4o-mini version)
gpt-4o-mini deepseek-v2-0628 mistral-nemo llama-8b openai deepseek-ai mistral-ai nvidia meta-ai-fair hugging-face langchain keras cost-efficiency context-windows open-source benchmarking neural-networks model-optimization text-generation fine-tuning developer-tools gpu-support parallelization cuda-integration multilinguality long-context article-generation liang-wenfeng
OpenAI launched the GPT-4o Mini, a cost-efficient small model priced at $0.15 per million input tokens and $0.60 per million output tokens, aiming to replace GPT-3.5 Turbo with enhanced intelligence but some performance limitations. DeepSeek open-sourced DeepSeek-V2-0628, topping the LMSYS Chatbot Arena Leaderboard and emphasizing their commitment to contributing to the AI ecosystem. Mistral AI and NVIDIA released the Mistral NeMo, a 12B parameter multilingual model with a record 128k token context window under an Apache 2.0 license, sparking debates on benchmarking accuracy against models like Meta Llama 8B. Research breakthroughs include the TextGrad framework for optimizing compound AI systems via textual feedback differentiation and the STORM system improving article writing by 25% through simulating diverse perspectives and addressing source bias. Developer tooling trends highlight LangChain's evolving context-aware reasoning applications and the Modular ecosystem's new official GPU support, including discussions on Mojo and Keras 3.0 integration.
Not much happened today.
phi-3-mini gpt4all-3.0 yi-large meta-3d-gen meta perplexity-ai microsoft gpt4all langchainai qdrant-engine 3d-generation long-context instruction-following reinforcement-learning-from-human-feedback persona-driven-data-synthesis meta-tuning model-steering memory-retrieval multivector-search universal-query-api rohanpaul_ai andriy_mulyar cwolferesearch sarahookr
Meta introduced Meta 3D Gen, a system for end-to-end generation of 3D assets from text in under 1 minute, producing high-quality 3D assets with detailed textures. Perplexity AI updated Pro Search to handle deeper research with multi-step reasoning and code execution. Microsoft improved Phi-3 Mini with better long-context understanding and instruction following. GPT4All 3.0 launched with support for thousands of models and major OS compatibility, featuring local file chat. Yi-Large model launched on Fireworks AI Playground. Research highlights include the evolution of reinforcement learning from human feedback (RLHF), persona-driven data synthesis using a billion diverse personas, meta-tuning for few-shot generalization, and steering vectors for model behavior control. Tools updates include LangSmith improving memory retrieval and Qdrant Engine v1.10 adding universal query API and multivector search.
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.
5 small news items
llama-3 xLSTM openai cohere deepmind hugging-face nvidia mistral-ai uncertainty-quantification parameter-efficient-fine-tuning automated-alignment model-efficiency long-context agentic-ai fine-tuning inference-optimization leopold-aschenbrenner will-brown rohanpaul_ai richardmcngo omarsar0 hwchase17 clementdelangue sophiamyang
OpenAI announces that ChatGPT's voice mode is "coming soon." Leopold Aschenbrenner launched a 5-part AGI timelines series predicting a trillion dollar cluster from current AI progress. Will Brown released a comprehensive GenAI Handbook. Cohere completed a $450 million funding round at a $5 billion valuation. DeepMind research on uncertainty quantification in LLMs and an xLSTM model outperforming transformers were highlighted. Studies on the geometry of concepts in LLMs and methods to eliminate matrix multiplication for efficiency gains were shared. Discussions on parameter-efficient fine-tuning (PEFT) and automated alignment of LLMs were noted. New tools include LangGraph for AI agents, LlamaIndex with longer context windows, and Hugging Face's integration with NVIDIA NIM for Llama3. Mistral AI released a fine-tuning API for their models.
1 TRILLION token context, real time, on device?
gemini-1.5-pro gemini-1.5 cartesia mistral-ai scale-ai state-space-models voice-models multimodality model-performance on-device-ai long-context evaluation-leaderboards learning-rate-optimization scientific-publishing research-vs-engineering yann-lecun elon-musk
Cartesia, a startup specializing in state space models (SSMs), launched a low latency voice model outperforming transformer-based models with 20% lower perplexity, 2x lower word error, and 1 point higher NISQA quality. This breakthrough highlights the potential for models that can continuously process and reason over massive streams of multimodal data (text, audio, video) with a trillion token context window on-device. The news also covers recent AI developments including Mistral's Codestral weights release, Schedule Free optimizers paper release, and Scale AI's new elo-style eval leaderboards. Additionally, a debate between yann-lecun and elon-musk on the importance of publishing AI research versus engineering achievements was noted. The Gemini 1.5 Pro/Advanced models were mentioned for their strong performance.
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.
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.
Evals: The Next Generation
gpt-4 gpt-5 gpt-3.5 phi-3 mistral-7b llama-3 scale-ai mistral-ai reka-ai openai moderna sanctuary-ai microsoft mit meta-ai-fair benchmarking data-contamination multimodality fine-tuning ai-regulation ai-safety ai-weapons neural-networks model-architecture model-training model-performance robotics activation-functions long-context sam-altman jim-fan
Scale AI highlighted issues with data contamination in benchmarks like MMLU and GSM8K, proposing a new benchmark where Mistral overfits and Phi-3 performs well. Reka released the VibeEval benchmark for multimodal models addressing multiple choice benchmark limitations. Sam Altman of OpenAI discussed GPT-4 as "dumb" and hinted at GPT-5 with AI agents as a major breakthrough. Researchers jailbroke GPT-3.5 via fine-tuning. Global calls emerged to ban AI-powered weapons, with US officials urging human control over nuclear arms. Ukraine launched an AI consular avatar, while Moderna partnered with OpenAI for medical AI advancements. Sanctuary AI and Microsoft collaborate on AI for general-purpose robots. MIT introduced Kolmogorov-Arnold networks with improved neural network efficiency. Meta AI is training Llama 3 models with over 400 billion parameters, featuring multimodality and longer context.
Mergestral, Meta MTIAv2, Cohere Rerank 3, Google Infini-Attention
mistral-8x22b command-r-plus rerank-3 infini-attention llama-3 sd-1.5 cosxl meta-ai-fair mistral-ai cohere google stability-ai hugging-face ollama model-merging training-accelerators retrieval-augmented-generation linear-attention long-context foundation-models image-generation rag-pipelines model-benchmarking context-length model-performance aidan_gomez ylecun swyx
Meta announced their new MTIAv2 chips designed for training and inference acceleration with improved architecture and integration with PyTorch 2.0. Mistral released the 8x22B Mixtral model, which was merged back into a dense model to effectively create a 22B Mistral model. Cohere launched Rerank 3, a foundation model enhancing enterprise search and retrieval-augmented generation (RAG) systems supporting 100+ languages. Google published a paper on Infini-attention, an ultra-scalable linear attention mechanism demonstrated on 1B and 8B models with 1 million sequence length. Additionally, Meta's Llama 3 is expected to start rolling out soon. Other notable updates include Command R+, an open model surpassing GPT-4 in chatbot performance with 128k context length, and advancements in Stable Diffusion models and RAG pipelines.
Claude 3 is officially America's Next Top Model
claude-3-opus claude-3-sonnet claude-3-haiku gpt-4o-mini mistral-7b qwen-72b anthropic mistral-ai huggingface openrouter stable-diffusion automatic1111 comfyui fine-tuning model-merging alignment ai-ethics benchmarking model-performance long-context cost-efficiency model-evaluation mark_riedl ethanjperez stuhlmueller ylecun aravsrinivas
Claude 3 Opus outperforms GPT4T and Mistral Large in blind Elo rankings, with Claude 3 Haiku marking a new cost-performance frontier. Fine-tuning techniques like QLoRA on Mistral 7B and evolutionary model merging on HuggingFace models are highlighted. Public opinion shows strong opposition to ASI development. Research supervision opportunities in AI alignment are announced. The Stable Diffusion 3 (SD3) release raises workflow concerns for tools like ComfyUI and automatic1111. Opus shows a 5% performance dip on OpenRouter compared to the Anthropic API. A new benchmark stresses LLM recall at long contexts, with Mistral 7B struggling and Qwen 72b performing well.
Claude 3 just destroyed GPT 4 (see for yourself)
claude-3 claude-3-opus claude-3-sonnet claude-3-haiku gpt-4 anthropic amazon google claude-ai multimodality vision long-context model-alignment model-evaluation synthetic-data structured-output instruction-following model-speed cost-efficiency benchmarking safety mmitchell connor-leahy
Claude 3 from Anthropic launches in three sizes: Haiku (small, unreleased), Sonnet (medium, default on claude.ai, AWS, and GCP), and Opus (large, on Claude Pro). Opus outperforms GPT-4 on key benchmarks like GPQA, impressing benchmark authors. All models support multimodality with advanced vision capabilities, including converting a 2-hour video into a blog post. Claude 3 offers improved alignment, fewer refusals, and extended context length up to 1 million tokens with near-perfect recall. Haiku is noted for speed and cost-efficiency, processing dense research papers in under three seconds. The models excel at following complex instructions and producing structured outputs like JSON. Safety improvements reduce refusal rates, though some criticism remains from experts. Claude 3 is trained on synthetic data and shows strong domain-specific evaluation results in finance, medicine, and philosophy.
Ring Attention for >1M Context
gemini-pro gemma-7b gemma-2b deepseek-coder-6.7b-instruct llama-cpp google cuda-mode nvidia polymind deepseek ollama runpod lmstudio long-context ringattention pytorch cuda llm-guessing-game chatbots retrieval-augmented-generation vram-optimization fine-tuning dynamic-prompt-optimization ml-workflows gpu-scaling model-updates liu zaharia abbeel
Google Gemini Pro has sparked renewed interest in long context capabilities. The CUDA MODE Discord is actively working on implementing the RingAttention paper by Liu, Zaharia, and Abbeel, including extensions from the World Model RingAttention paper, with available PyTorch and CUDA implementations. TheBloke Discord discussed various topics including LLM guessing game evaluation, chatbot UX comparisons between Nvidia's Chat with RTX and Polymind, challenges in retrieval-augmented generation (RAG) integration, VRAM optimization, fine-tuning for character roleplay using Dynamic Prompt Optimization (DPO), and model choices like deepseek-coder-6.7B-instruct. There was also discussion on ML workflows on Mac Studio, with preferences for llama.cpp over ollama, and scaling inference cost-effectively using GPUs like the 4090 on Runpod. LM Studio users face manual update requirements for version 0.2.16, which includes support for Gemma models and bug fixes, especially for MacOS. The Gemma 7B model has had performance issues, while Gemma 2B received positive feedback.
Google AI: Win some (Gemma, 1.5 Pro), Lose some (Image gen)
gemma-2b gemma-7b gemma gemini-pro-1.5 llama-2 llama-3 mistral google hugging-face nvidia benchmarking license-policies image-generation video-understanding long-context dataset-editing model-integration gpu-hardware bug-fixes quantization
Google's Gemma open models (2-7B parameters) outperform Llama 2 and Mistral in benchmarks but face criticism for an unusual license and poor image generation quality, which Google partially acknowledges. The upcoming Gemini Pro 1.5 model features a 1 million token context window, excelling in video understanding and needle-in-haystack tasks. Discord communities like TheBloke and LM Studio discuss mixed reception of Gemma models, anticipation for Llama 3 release, challenges in dataset editing, and hardware considerations such as NVIDIA GeForce RTX 3090 and RTX 4090 GPUs. LM Studio users report issues with version 0.2.15 Beta and ongoing integration of Gemma models, with resources shared on Hugging Face.
Sora pushes SOTA
gemini-1.5 sora h20-gpt mistral-7b llama-13b mistralcasualml mixtral-instruct yi-models openai google-deepmind nvidia mistral-ai h2oai multimodality gpu-power-management long-context model-merging fine-tuning retrieval-augmented-generation role-play-model-optimization cross-language-integration training-loss synthetic-data-generation coding-support
Discord communities analyzed over 20 guilds, 312 channels, and 10550 messages reveal intense discussions on AI developments. Key highlights include the Dungeon Master AI assistant for Dungeons and Dragons using models like H20 GPT, GPU power supply debates involving 3090 and 3060 GPUs, and excitement around Google's Gemini 1.5 with its 1 million token context window and OpenAI's Sora model. Challenges with large world models (LWM) multimodality, GPT-assisted coding, and role-play model optimization with Yi models and Mixtral Instruct were discussed. Technical issues like model merging errors with MistralCasualML, fine-tuning scripts like AutoFineTune, and cross-language engineering via JSPyBridge were also prominent. NVIDIA's Chat with RTX feature leveraging retrieval-augmented generation (RAG) on 30+ series GPUs was compared to LMStudio's support for Mistral 7b and Llama 13b models. The community is cautiously optimistic about these frontier models' applications in media and coding.
1/8/2024: The Four Wars of the AI Stack
mixtral mistral nous-research openai mistral-ai hugging-face context-window distributed-models long-context hierarchical-embeddings agentic-rag fine-tuning synthetic-data oil-and-gas embedding-datasets mixture-of-experts model-comparison
The Nous Research AI Discord discussions highlighted several key topics including the use of DINO, CLIP, and CNNs in the Obsidian Project. A research paper on distributed models like DistAttention and DistKV-LLM was shared to address cloud-based LLM service challenges. Another paper titled 'Self-Extend LLM Context Window Without Tuning' argued that existing LLMs can handle long contexts inherently. The community also discussed AI models like Mixtral, favored for its 32k context window, and compared it with Mistral and Marcoroni. Other topics included hierarchical embeddings, agentic retrieval-augmented generation (RAG), synthetic data for fine-tuning, and the application of LLMs in the oil & gas industry. The launch of the AgentSearch-V1 dataset with one billion embedding vectors was also announced. The discussions covered mixture-of-experts (MoE) implementations and the performance of smaller models.
12/8/2023 - Mamba v Mistral v Hyena
mistral-8x7b-moe mamba-3b stripedhyena-7b claude-2.1 gemini gpt-4 dialogrpt-human-vs-machine cybertron-7b-v2-gguf falcon-180b mistral-ai togethercompute stanford anthropic google hugging-face mixture-of-experts attention-mechanisms prompt-engineering alignment image-training model-deployment gpu-requirements cpu-performance model-inference long-context model-evaluation open-source chatbots andrej-karpathy tri-dao maxwellandrews raddka
Three new AI models are highlighted: Mistral's 8x7B MoE model (Mixtral), Mamba models up to 3B by Together, and StripedHyena 7B, a competitive subquadratic attention model from Stanford's Hazy Research. Discussions on Anthropic's Claude 2.1 focus on its prompting technique and alignment challenges. The Gemini AI from Google is noted as potentially superior to GPT-4. The community also explores Dreambooth for image training and shares resources like the DialogRPT-human-vs-machine model on Hugging Face. Deployment challenges for large language models, including CPU performance and GPU requirements, are discussed with references to Falcon 180B and transformer batching techniques. User engagement includes meme sharing and humor.