All tags
Topic: "chain-of-thought"
not much happened today
kernelllm-8b gpt-4o deepseek-v3 mistral-medium-3 qwen3 blip3-o xgen-small anisora stable-audio-open-small alphaevolve meta-ai-fair mistral-ai qwen deepseek salesforce bilibili stability-ai google benchmarking model-performance multilinguality hardware-optimization multimodality image-generation video-generation text-to-audio model-parallelism chain-of-thought instruction-following reasoning mitigation-strategies reach_vb lmarena_ai theadimeline adcock_brett jxmnop dair_ai omarsar0
Meta released KernelLLM 8B, outperforming GPT-4o and DeepSeek V3 on KernelBench-Triton Level 1. Mistral Medium 3 debuted strongly in multiple benchmarks. Qwen3 models introduced a unified framework with multilingual support. DeepSeek-V3 features hardware-aware co-design. BLIP3-o family released for multimodal tasks using diffusion transformers. Salesforce launched xGen-Small models excelling in long-context and math benchmarks. Bilibili released AniSORA for anime video generation. Stability AI open-sourced Stable Audio Open Small optimized for Arm devices. Google’s AlphaEvolve coding agent improved Strassen's algorithm for the first time since 1969. Research shows chain-of-thought reasoning can harm instruction-following ability, with mitigation strategies like classifier-selective reasoning being most effective, but reasoning techniques show high variance and limited generalization. "Chain-of-thought (CoT) reasoning can harm a model’s ability to follow instructions" and "Mitigation strategies such as few-shot in-context learning, self-reflection, self-selective reasoning, and classifier-selective reasoning can counteract reasoning-induced failures".
not much happened today
gemini-2.5-flash gemini-2.0-flash mistral-medium-3 llama-4-maverick claude-3.7-sonnet qwen3 pangu-ultra-moe deepseek-r1 o4-mini x-reasoner google-deepmind mistral-ai alibaba huawei openai microsoft deepseek model-performance reasoning cost-analysis reinforcement-learning chain-of-thought multilinguality code-search model-training vision model-integration giffmana artificialanlys teortaxestex akhaliq john__allard
Gemini 2.5 Flash shows a 12 point increase in the Artificial Analysis Intelligence Index but costs 150x more than Gemini 2.0 Flash due to 9x more expensive output tokens and 17x higher token usage during reasoning. Mistral Medium 3 competes with Llama 4 Maverick, Gemini 2.0 Flash, and Claude 3.7 Sonnet with better coding and math reasoning at a significantly lower price. Alibaba's Qwen3 family supports reasoning and multilingual tasks across 119 languages and includes a Web Dev tool for app building. Huawei's Pangu Ultra MoE matches DeepSeek R1 performance on Ascend NPUs, with new compute and upcoming V4 training. OpenAI's o4-mini now supports Reinforcement Fine-Tuning (RFT) using chain-of-thought reasoning. Microsoft's X-REASONER enables generalizable reasoning across modalities post-trained on general-domain text. Deep research integration with GitHub repos in ChatGPT enhances codebase search and reporting. The AI Engineer World's Fair offers an Early Bird discount for upcoming tickets.
Gemini 2.5 Flash completes the total domination of the Pareto Frontier
gemini-2.5-flash o3 o4-mini google openai anthropic tool-use multimodality benchmarking reasoning reinforcement-learning open-source model-releases chain-of-thought coding-agent sama kevinweil markchen90 alexandr_wang polynoamial scaling01 aidan_mclau cwolferesearch
Gemini 2.5 Flash is introduced with a new "thinking budget" feature offering more control compared to Anthropic and OpenAI models, marking a significant update in the Gemini series. OpenAI launched o3 and o4-mini models, emphasizing advanced tool use capabilities and multimodal understanding, with o3 dominating several leaderboards but receiving mixed benchmark reviews. The importance of tool use in AI research and development is highlighted, with OpenAI Codex CLI announced as a lightweight open-source coding agent. The news reflects ongoing trends in AI model releases, benchmarking, and tool integration.
not much happened today
gemini-2.0-flash-thinking-experimental-1-21 zonos openr1-math-220k huginn-3.5b deepseek-r1 o1 claude google zyphraai hugging-face anthropic deepseek openai vision multilingual-models text-to-speech voice-cloning math reasoning latent-reasoning chain-of-thought dataset-release fine-tuning model-training model-performance context-windows benchmarking jeremyphoward andrej-karpathy tom-goldstein reach_vb iscienceluvr
Google released Gemini 2.0 Flash Thinking Experimental 1-21, a vision-language reasoning model with a 1 million-token context window and improved accuracy on science, math, and multimedia benchmarks, surpassing DeepSeek-R1 but trailing OpenAI's o1. ZyphraAI launched Zonos, a multilingual Text-to-Speech model with instant voice cloning and controls for speaking rate, pitch, and emotions, running at ~2x real-time speed on RTX 4090. Hugging Face released OpenR1-Math-220k, a large-scale math reasoning dataset with 220K problems and 800K reasoning traces generated on 512 H100 GPUs. Tom Goldstein introduced Huginn-3.5B, an open-source latent reasoning model trained on 800B tokens that outperforms larger models on reasoning tasks like GSM8K. Discussions by Jeremy Howard and iScienceLuvr highlight advances in implicit latent reasoning and debate the future of human-readable reasoning traces. Anthropic launched the Anthropic Economic Index to analyze AI's economic impact using millions of Claude conversations.
not much happened today
deepseek-r1 qwen-2.5 qwen-2.5-max deepseek-v3 deepseek-janus-pro gpt-4 nvidia anthropic openai deepseek huawei vercel bespoke-labs model-merging multimodality reinforcement-learning chain-of-thought gpu-optimization compute-infrastructure compression crypto-api image-generation saranormous zizhpan victormustar omarsar0 markchen90 sakanaailabs reach_vb madiator dain_mclau francoisfleuret garygodchaux arankomatsuzaki id_aa_carmack lavanyasant virattt
Huawei chips are highlighted in a diverse AI news roundup covering NVIDIA's stock rebound, new open music foundation models like Local Suno, and competitive AI models such as Qwen 2.5 Max and Deepseek V3. The release of DeepSeek Janus Pro, a multimodal LLM with image generation capabilities, and advancements in reinforcement learning and chain-of-thought reasoning are noted. Discussions include GPU rebranding with NVIDIA's H6400 GPUs, data center innovations, and enterprise AI applications like crypto APIs in hedge funds. "Deepseek R1's capabilities" and "Qwen 2.5 models added to applications" are key highlights.
DeepSeek #1 on US App Store, Nvidia stock tanks -17%
deepseek-r1 deepseek-v3 qwen2.5-vl o1 deepseek openai nvidia langchain moe-architecture chain-of-thought fp8-precision multimodality vision agentic-ai inference-scaling gpu-optimization model-efficiency ai-chatbots memory-integration tool-use stock-market-reactions sama mervenoyann omarasar0 teortaxestex nptacek carpeetti finbarrtimbers cwolferesearch arthurrapier danhendrycks scaling01 janusflow
DeepSeek has made a significant cultural impact by hitting mainstream news unexpectedly in 2025. The DeepSeek-R1 model features a massive 671B parameter MoE architecture and demonstrates chain-of-thought (CoT) capabilities comparable to OpenAI's o1 at a lower cost. The DeepSeek V3 model trains a 236B parameter model 42% faster than its predecessor using fp8 precision. The Qwen2.5 multimodal models support images and videos with sizes ranging from 3B to 72B parameters, featuring strong vision and agentic capabilities. LangChain and LangGraph integration enable AI chatbots with memory and tool use, including applications like the DeFi Agent. Discussions highlight NVIDIA's role in hardware acceleration, with concerns about stock drops due to DeepSeek's efficiency and market fears. The compute demand is expected to rise despite efficiency gains, driven by inference scaling and MoE design improvements.
TinyZero: Reproduce DeepSeek R1-Zero for $30
deepseek-r1 qwen o1 claude-3-sonnet claude-3 prime ppo grpo llama-stack deepseek berkeley hugging-face meta-ai-fair openai deeplearningai reinforcement-learning fine-tuning chain-of-thought multi-modal-benchmark memory-management model-training open-source agentic-workflow-automation model-performance jiayi-pan saranormous reach_vb lmarena_ai nearcyan omarsar0 philschmid hardmaru awnihannun winglian
DeepSeek Mania continues to reshape the frontier model landscape with Jiayi Pan from Berkeley reproducing the OTHER result from the DeepSeek R1 paper, R1-Zero, in a cost-effective Qwen model fine-tune for two math tasks. A key finding is a lower bound to the distillation effect at 1.5B parameters, with RLCoT reasoning emerging as an intrinsic property. Various RL techniques like PPO, DeepSeek's GRPO, or PRIME show similar outcomes, and starting from an Instruct model speeds convergence. The Humanity’s Last Exam (HLE) Benchmark introduces a challenging multi-modal test with 3,000 expert-level questions across 100+ subjects, where models perform below 10%, with DeepSeek-R1 achieving 9.4%. DeepSeek-R1 excels in chain-of-thought reasoning, outperforming models like o1 while being 20x cheaper and MIT licensed. The WebDev Arena Leaderboard ranks DeepSeek-R1 #2 in technical domains and #1 under Style Control, closing in on Claude 3.5 Sonnet. OpenAI's Operator is deployed to 100% of Pro users in the US, enabling tasks like ordering meals and booking reservations, and functions as a research assistant for AI paper searches and summaries. Hugging Face announces a leadership change after significant growth, while Meta AI releases the first stable version of Llama Stack with streamlined upgrades and automated verification. DeepSeek-R1's open-source success is celebrated, and technical challenges like memory management on macOS 15+ are addressed with residency sets in MLX for stability.
not much happened today
rstar-math o1-preview qwen2.5-plus qwen2.5-coder-32b-instruct phi-4 claude-3.5-sonnet openai anthropic alibaba microsoft cohere langchain weights-biases deepseek rakuten rbc amd johns-hopkins math process-reward-model mcts vision reasoning synthetic-data pretraining rag automation private-deployment multi-step-workflow open-source-dataset text-embeddings image-segmentation chain-of-thought multimodal-reasoning finetuning recursive-self-improvement collaborative-platforms ai-development partnerships cuda triton ai-efficiency ai-assisted-coding reach_vb rasbt akshaykagrawal arankomatsuzaki teortaxestex aidangomez andrewyng
rStar-Math surpasses OpenAI's o1-preview in math reasoning with 90.0% accuracy using a 7B LLM and MCTS with a Process Reward Model. Alibaba launches Qwen Chat featuring Qwen2.5-Plus and Qwen2.5-Coder-32B-Instruct models enhancing vision-language and reasoning. Microsoft releases Phi-4, trained on 40% synthetic data with improved pretraining. Cohere introduces North, a secure AI workspace integrating LLMs, RAG, and automation for private deployments. LangChain showcases a company research agent with multi-step workflows and open-source datasets. Transformers.js demos released for text embeddings and image segmentation in JavaScript. Research highlights include Meta Meta-CoT for enhanced chain-of-thought reasoning, DeepSeek V3 with recursive self-improvement, and collaborative AI development platforms. Industry partnerships include Rakuten with LangChain, North with RBC supporting 90,000 employees, and Agent Laboratory collaborating with AMD and Johns Hopkins. Technical discussions emphasize CUDA and Triton for AI efficiency and evolving AI-assisted coding stacks by Andrew Ng.
not much happened today
prime gpt-4o qwen-32b olmo openai qwen cerebras-systems langchain vercel swaggo gin echo reasoning chain-of-thought math coding optimization performance image-processing software-development agent-frameworks version-control security robotics hardware-optimization medical-ai financial-ai architecture akhaliq jason-wei vikhyatk awnihannun arohan tom-doerr hendrikbgr jerryjliu0 adcock-brett shuchaobi stasbekman reach-vb virattt andrew-n-carr
Olmo 2 released a detailed tech report showcasing full pre, mid, and post-training details for a frontier fully open model. PRIME, an open-source reasoning solution, achieved 26.7% pass@1, surpassing GPT-4o in benchmarks. Performance improvements include Qwen 32B (4-bit) generating at >40 tokens/sec on an M4 Max and libvips being 25x faster than Pillow for image resizing. New tools like Swaggo/swag for Swagger 2.0 documentation, Jujutsu (jj) Git-compatible VCS, and Portspoof security tool were introduced. Robotics advances include a weapon detection system with a meters-wide field of view and faster frame rates. Hardware benchmarks compared H100 and MI300x accelerators. Applications span medical error detection using PRIME and a financial AI agent integrating LangChainAI and Vercel AI SDK. Architectural insights suggest the need for breakthroughs similar to SSMs or RNNs.
not much happened today
vllm deepseek-v3 llamaindex openai deepseek qdrant twilio llamaindex elevenlabs training-efficiency parallelism cpu-offloading gradient-descent mixture-of-experts fp8-precision memory-optimization ai-voice-assistants coding-assistants document-processing version-control learning-rate-schedules federated-learning agentic-systems multi-agent-systems deliberative-alignment chain-of-thought on-device-ai multimodality francois-fleuret daniel-hanchen aaron-defazio fchollet elad-gil wojciech-zaremba richard-socher
ChatGPT, Sora, and the OpenAI API experienced a >5 hour outage but are now restored. Updates to vLLM enable DeepSeek-V3 to run with enhanced parallelism and CPU offloading, improving model deployment flexibility. Discussions on gradient descent in top-k routing MoE and adoption of FP8 precision focus on training efficiency and memory optimization. AIDE, an AI voice medical assistant by Team Therasync, leverages Qdrant, OpenAI, and Twilio. DeepSeek-Engineer offers AI-powered coding assistance with structured outputs. LlamaIndex integrates LlamaCloud and ElevenLabs for large-scale document processing and voice interaction. Insights on version control with ghstack and advocacy for linear decay learning rate schedules highlight best practices in AI development. Experts predict smaller, tighter models, true multimodal models, and on-device AI in 2025. Proposals for planetary-scale federated learning and community AGI moonshots emphasize future AI directions. Discussions on agentic systems, multi-agent workflows, and deliberative alignment through chain of thought reasoning underscore AI safety and alignment efforts.
DeepSeek v3: 671B finegrained MoE trained for $5.5m USD of compute on 15T tokens
deepseek-v3 gpt-4o claude-3.5-sonnet llama-3 deepseek-ai hugging-face openai anthropic mixture-of-experts model-training model-optimization reinforcement-learning chain-of-thought multi-token-prediction synthetic-data model-distillation fine-tuning attention-mechanisms gpu-optimization nrehiew_ denny_zhou
DeepSeek-V3 has launched with 671B MoE parameters and trained on 14.8T tokens, outperforming GPT-4o and Claude-3.5-sonnet in benchmarks. It was trained with only 2.788M H800 GPU hours, significantly less than Llama-3's 30.8M GPU-hours, showcasing major compute efficiency and cost reduction. The model is open-source and deployed via Hugging Face with API support. Innovations include native FP8 mixed precision training, Multi-Head Latent Attention scaling, distillation from synthetic reasoning data, pruning and healing for MoEs with up to 256 experts, and a new multi-token prediction objective enabling lookahead token planning. Research highlights also cover the OREO method and Natural Language Reinforcement Learning (NLRL) for multi-step reasoning and agent control.
Stripe lets Agents spend money with StripeAgentToolkit
gpt-4o gemini-exp-1114 stripe openai anthropic meta-ai-fair ai-computer-interfaces agentic-ai model-overfitting benchmarks scaling-laws agi chain-of-thought image-captioning dialogue-systems memory-efficient-fine-tuning diffusion-models mixture-of-experts adaptive-decoding creativity-optimization factuality-optimization pair-programming document-parsing retrieval-augmented-generation abacaj francois-fleuret lmarena_ai goodside jxmnop jaseweston stevenheidel
Stripe has pioneered an AI SDK specifically designed for agents that handle payments, integrating with models like gpt-4o to enable financial transactions and token-based charging. The AI developer tooling trend emphasizes better "AI-Computer Interfaces" for improved agent reliability, with tools like E2B and the
llms.txt documentation trend gaining traction, notably adopted by Anthropic. In AI model news, Gemini-Exp-1114 topped the Vision Leaderboard and improved in Math Arena, while discussions continue around model overfitting and the limits of scaling laws for AGI. OpenAI released a ChatGPT desktop app for macOS with integrations for VS Code, Xcode, and Terminal, enhancing developer workflows and pair programming. Anthropic introduced a prompt improver using chain-of-thought reasoning, and Meta AI shared top research from EMNLP2024 on image captioning, dialogue systems, and memory-efficient fine-tuning. Highlights from ICLR 2025 include diffusion-based illumination harmonization, open mixture-of-experts language models, and hyperbolic vision-language models. A new adaptive decoding method optimizes creativity and factuality per token. Tools like LlamaParse and RAGformation were also introduced for document parsing and retrieval-augmented generation. FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI
o1 claude-3.5-haiku gpt-4o epoch-ai openai microsoft anthropic x-ai langchainai benchmarking math moravecs-paradox mixture-of-experts chain-of-thought agent-framework financial-metrics-api pdf-processing few-shot-learning code-generation karpathy philschmid adcock_brett dylan522p
Epoch AI collaborated with over 60 leading mathematicians to create the FrontierMath benchmark, a fresh set of hundreds of original math problems with easy-to-verify answers, aiming to challenge current AI models. The benchmark reveals that all tested models, including o1, perform poorly, highlighting the difficulty of complex problem-solving and Moravec's paradox in AI. Key AI developments include the introduction of Mixture-of-Transformers (MoT), a sparse multi-modal transformer architecture reducing computational costs, and improvements in Chain-of-Thought (CoT) prompting through incorrect reasoning and explanations. Industry news covers OpenAI acquiring the chat.com domain, Microsoft launching the Magentic-One agent framework, Anthropic releasing Claude 3.5 Haiku outperforming gpt-4o on some benchmarks, and xAI securing 150MW grid power with support from Elon Musk and Trump. LangChain AI introduced new tools including a Financial Metrics API, Document GPT with PDF upload and Q&A, and LangPost AI agent for LinkedIn posts. xAI also demonstrated the Grok Engineer compatible with OpenAI and Anthropic APIs for code generation.
not much happened today
llama-3-2-vision gpt-2 meta-ai-fair ollama amd llamaindex gemini gitpod togethercompute langchainai weights-biases stanfordnlp deeplearningai model-scaling neural-networks multi-gpu-support skip-connections transformers healthcare-ai automated-recruitment zero-trust-security small-language-models numerical-processing chain-of-thought optical-character-recognition multi-agent-systems agent-memory interactive-language-learning bindureddy fstichler stasbekman jxmnop bindureddy omarsar0 giffmana rajammanabrolu
This week in AI news highlights Ollama 0.4 supporting Meta's Llama 3.2 Vision models (11B and 90B), with applications like handwriting recognition. Self-Consistency Preference Optimization (ScPO) was introduced to improve model consistency without human labels. Discussions on model scaling, neural networks resurgence, and AMD's multi-GPU bandwidth challenges were noted. The importance of skip connections in Transformers was emphasized. In healthcare, less regulation plus AI could revolutionize disease treatment and aging. Tools like LlamaParse and Gemini aid automated resume insights. Gitpod Flex demonstrated zero-trust architecture for secure development environments. Research includes surveys on Small Language Models (SLMs), number understanding in LLMs, and DTrOCR using a GPT-2 decoder for OCR. Multi-agent systems in prediction markets were discussed by TogetherCompute and LangChainAI. Community events include NeurIPS Happy Hour, NLP seminars, and courses on Agent Memory with LLMs as operating systems.
o1 destroys Lmsys Arena, Qwen 2.5, Kyutai Moshi release
o1-preview o1-mini qwen-2.5 qwen-plus llama-3-1 deepseek-v2.5 openai anthropic google alibaba deepseek kyutai weights-biases mistral-ai chain-of-thought multimodality model-benchmarking model-performance streaming-neural-architecture llm-observability experiment-tracking rate-limiting sama guillaumelample
OpenAI's o1-preview model has achieved a milestone by fully matching top daily AI news stories without human intervention, consistently outperforming other models like Anthropic, Google, and Llama 3 in vibe check evaluations. OpenAI models dominate the top 4 slots on LMsys benchmarks, with rate limits increasing to 500-1000 requests per minute. In open source, Alibaba's Qwen 2.5 suite surpasses Llama 3.1 at the 70B scale and updates its closed Qwen-Plus models to outperform DeepSeek V2.5 but still lag behind leading American models. Kyutai Moshi released its open weights realtime voice model featuring a unique streaming neural architecture with an "inner monologue." Weights & Biases introduced Weave, an LLM observability toolkit that enhances experiment tracking and evaluation, turning prompting into a more scientific process. The news also highlights upcoming events like the WandB LLM-as-judge hackathon in San Francisco. "o1-preview consistently beats out our vibe check evals" and "OpenAI models are gradually raising rate limits by the day."
a quiet weekend
o1 datagemma aloha demostart firefly-ai-video-model pixtral-12b gamegen-o openai google-deepmind adobe mistral-ai tencent supermaven 11x cohere anthropic latent-space-university stanford microsoft mila notre-dame reinforcement-learning chain-of-thought reasoning robotics diffusion-models multimodality video-generation model-training reflection-tuning mathematical-reasoning model-benchmarking fine-tuning george-hotz terence-tao adcock_brett rohanpaul_ai bindureddy fchollet philschmid
OpenAI released the new o1 model, leveraging reinforcement learning and chain-of-thought prompting to excel in reasoning benchmarks, achieving an IQ-like score of 120. Google DeepMind introduced DataGemma to reduce hallucinations by connecting LLMs with real-world data, and unveiled ALOHA and DemoStart for robot dexterity using diffusion methods. Adobe previewed its Firefly AI Video Model with text-to-video and generative extend features. Mistral launched the multimodal Pixtral 12B model, and Tencent presented the GameGen-O open-world video game generation model. Several research papers from Stanford, OpenAI, Microsoft, Mila, and Notre Dame focus on advanced reasoning, self-verification, and reflection tuning techniques. Experts like Terence Tao and George Hotz have shared mixed but optimistic views on o1's capabilities. Seed funding rounds include Supermaven ($12M) and 11x ($24M).
Learnings from o1 AMA
o1-preview o1-mini claude-3.5-sonnet gpt-4o openai weights-biases cohere weaviate reinforcement-learning chain-of-thought reasoning model-performance prompting code-editing rag hybrid-search sama rohanpaul_ai gdb andrew-mayne
OpenAI released the o1 model series, touted as their "most capable and aligned models yet," trained with reinforcement learning to enhance reasoning. The o1-preview model scored 21% on ARC-AGI, ~80% on aider code editing (surpassing Claude 3.5 Sonnet's 77%), and ~52% on Cognition-Golden, showcasing a shift from memorizing answers to memorizing reasoning. The model employs a unique chain-of-thought approach enabling "System II thinking" for better problem-solving. Experts like Andrew Mayne advise framing o1 as a smart friend providing thoughtful explanations. Additionally, an advanced RAG course sponsored by Weights & Biases, Cohere, and Weaviate offers strategies for hybrid search and prompting to optimize AI solutions.
Reflection 70B, by Matt from IT Department
llama-3.1-70b llama-3 claude-3.5-sonnet hyperwrite glaive fine-tuning chain-of-thought instruction-following synthetic-data quantization model-evaluation prompt-engineering matt-shumer sahil-chaudhary
Reflection Tuning technique has been used by a two-person team from Hyperwrite and Glaive to finetune llama-3.1-70b, showing strong performance improvements with minimal synthetic data. The approach builds on the concept of adding
thinking and reflection steps to outputs, related to the Chain of Thought method. Despite some criticisms like contamination concerns, worse coding performance, and reliance on system prompts, the model has received positive reception and comparisons to claude-3.5-sonnet. The work highlights efficient instruction tuning and synthetic data generation for large models. FlashAttention 3, PaliGemma, OpenAI's 5 Levels to Superintelligence
flashattention-3 paligemma-3b gemma-2b numinamath-7b deepseekmath-7b codellama-34b wizardcoder-python-34b-v1.0 chatgpt-3.5 openai together-ai google hugging-face deepseek code-llama attention-mechanisms fp8-training vision prefix-lm superintelligence fine-tuning chain-of-thought tool-integrated-reasoning self-consistency-decoding python coding-capabilities elo-ratings ilya-sutskever lucas-giffman
FlashAttention-3 introduces fast and accurate attention optimized for H100 GPUs, advancing native FP8 training. PaliGemma, a versatile 3B Vision-Language Model (VLM) combining a SigLIP-So400m ViT encoder with the Gemma-2B language model, emphasizes a prefix-LM architecture for improved image-query interaction. OpenAI reveals a framework on levels of superintelligence, signaling progress toward Level 2 and highlighting internal safety disagreements. On Reddit, NuminaMath 7B, fine-tuned from DeepSeekMath-7B, wins the AI Math Olympiad by solving 29 problems using iterative supervised fine-tuning and tool-integrated reasoning. Open-source LLMs like CodeLlama-34b and WizardCoder-Python-34B-V1.0 are closing the coding performance gap with closed models such as ChatGPT-3.5.
HippoRAG: First, do know(ledge) Graph
qwen-2 gpt-4 hipporag alibaba openai knowledge-graphs personalized-pagerank multi-hop-retrieval chain-of-thought implicit-reasoning sparse-autoencoders model-interpretability model-efficiency model-architecture fine-tuning reinforcement-learning rohanpaul_ai omarsar0 nabla_theta huybery
Alibaba released new open-source Qwen2 models ranging from 0.5B to 72B parameters, achieving SOTA results on benchmarks like MMLU and HumanEval. Researchers introduced Sparse Autoencoders to interpret GPT-4 neural activity, improving feature representation. The HippoRAG paper proposes a hippocampus-inspired retrieval augmentation method using knowledge graphs and Personalized PageRank for efficient multi-hop reasoning. New techniques like Stepwise Internalization enable implicit chain-of-thought reasoning in LLMs, enhancing accuracy and speed. The Buffer of Thoughts (BoT) method improves reasoning efficiency with significant cost reduction. A novel scalable MatMul-free LLM architecture competitive with SOTA Transformers at billion-parameter scale was also presented. "Single-Step, Multi-Hop retrieval" is highlighted as a key advancement in retrieval speed and cost.
Quis promptum ipso promptiet?
llama-3-70b llama-3-120b llama-3 llama-cpp anthropic openai zoominfo neuralink prompt-engineering chain-of-thought rag quantization cuda-graphs gpu-optimization thought-controlled-devices modeling-consciousness conference sama gdb bindureddy svpino rohanpaul_ai alexalbert__ abacaj
Anthropic released upgrades to their Workbench Console, introducing new prompt engineering features like chain-of-thought reasoning and prompt generators that significantly reduce development time, exemplified by their customer Zoominfo. OpenAI teased a "magic" new development coming soon, speculated to be a new LLM replacing GPT-3.5 in the free tier or a search competitor. The open-source community highlighted Llama 3 70B as "game changing" with new quantized weights for Llama 3 120B and CUDA graph support for llama.cpp improving GPU performance. Neuralink demonstrated a thought-controlled mouse, sparking interest in modeling consciousness from brain signals. The ICLR 2024 conference is being held in Asia for the first time, generating excitement.
FSDP+QLoRA: the Answer to 70b-scale AI for desktop class GPUs
qlora fsdp inflection-2.5 gpt-4 answer.ai hugging-face meta-ai-fair nvidia inflectionai model-training quantization memory-optimization gradient-checkpointing cpu-offloading fine-tuning model-sharding reinforcement-learning chain-of-thought benchmarking jeremy_howard tim_dettmers yann_lecun
Jeremy Howard and collaborators released a new tool combining FSDP, QLoRA, and HQQ to enable training 70b-parameter models on affordable consumer GPUs like RTX 4090s with only 24GB RAM, overcoming traditional memory constraints that required expensive data center GPUs costing over $150k. The approach shards quantized models across multiple GPUs and uses techniques like gradient checkpointing and CPU offloading to achieve efficient training on desktop-class hardware. The blogpost details challenges and solutions integrating these methods, highlighting a significant cost reduction from $150k to under $2.5k for training large language models. Additionally, Twitter recaps mention Inflection AI's Inflection-2.5 model rivaling GPT-4 in benchmarks with less compute, and Grok improving speed by 3x. Yann LeCun discusses multi-step reasoning training for LLMs.
1/12/2024: Anthropic coins Sleeper Agents
nous-mixtral 120b anthropic openai nous-research hugging-face reinforcement-learning fine-tuning backdoors model-security adversarial-training chain-of-thought model-merging dataset-release security-vs-convenience leo-gao andrej-karpathy
Anthropic released a new paper exploring the persistence of deceptive alignment and backdoors in models through stages of training including supervised fine-tuning and reinforcement learning safety training. The study found that safety training and adversarial training did not eliminate backdoors, which can cause models to write insecure code or exhibit hidden behaviors triggered by specific prompts. Notable AI figures like leo gao and andrej-karpathy praised the work, highlighting its implications for future model security and the risks of sleeper agent LLMs. Additionally, the Nous Research AI Discord community discussed topics such as the trade-off between security and convenience, the Hulk Dataset 0.1 for LLM fine-tuning, curiosity about a 120B model and Nous Mixtral, debates on LLM leaderboard legitimacy, and the rise of Frankenmerge techniques for model merging and capacity enhancement.
12/9/2023: The Mixtral Rush
mixtral hermes-2.5 hermes-2 mistral-yarn ultrachat discoresearch fireworks-ai hugging-face mistral-ai benchmarking gpu-requirements multi-gpu quantization gptq chain-of-thought min-p-sampling top-p-sampling model-sampling model-merging model-performance small-models reasoning-consistency temperature-sampling bjoernp the_bloke rtyax kalomaze solbus calytrix
Mixtral's weights were released without code, prompting the Disco Research community and Fireworks AI to implement it rapidly. Despite efforts, no significant benchmark improvements were reported, limiting its usefulness for local LLM usage but marking progress for the small models community. Discussions in the DiscoResearch Discord covered Mixtral's performance compared to models like Hermes 2.5 and Hermes 2, with evaluations on benchmarks such as winogrande, truthfulqa_mc2, and arc_challenge. Technical topics included GPU requirements, multi-GPU setups, and quantization via GPTQ. Benchmarking strategies like grammar-based evaluation, chain of thought (CoT), and min_p sampling were explored, alongside model sampling techniques like Min P and Top P to enhance response stability and creativity. Users also discussed GPTs' learning limitations and the adaptability of models under varying conditions, emphasizing min_p sampling's role in enabling higher temperature settings for creativity.
Is Google's Gemini... legit?
gemini gemini-pro gemini-ultra gpt-4 gpt-3.5 claude-2.1 palm2 google openai chain-of-thought context-windows prompt-engineering model-evaluation multimodality speech-processing chatbot-errors subscription-management swyx
Google's Gemini AI model is generating significant discussion and skepticism, especially regarding its 32-shot chain of thought MMLU claim and 32k context window. The community is comparing Gemini's performance and capabilities with OpenAI's GPT-4 and GPT-3.5, highlighting the upcoming Gemini Pro and Gemini Ultra models on the Bard platform. Users report various OpenAI service issues including chatbot errors and subscription problems. Discussions also cover prompt engineering techniques, AI model evaluation comparing GPT-4, Claude 2.1, and PaLM2, and improvements in speech and multimodal capabilities. The bot now supports reading and summarizing links from platforms like arXiv, Twitter, and YouTube, enhancing user interaction.