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Model: "claude-3"
Claude 3.7 Sonnet
claude-3-7-sonnet claude-3 claude-code anthropic hybrid-reasoning extended-thinking coding-benchmarks agentic-ai prompt-caching streaming token-capacity tool-use
Anthropic launched Claude 3.7 Sonnet, their most intelligent model to date featuring hybrid reasoning with two thinking modes: near-instant and extended step-by-step thinking. The release includes Claude Code, an agentic coding tool in limited preview, and supports a 128k output token capability in beta. Claude 3.7 Sonnet performs well on coding benchmarks like SWE-Bench Verified and Cognition's junior-dev eval, and introduces advanced features such as streaming thinking, prompt caching, and tool use. The model is also benchmarked on Pokebench, reflecting agentic capabilities similar to the Voyager paper. The launch is accompanied by extensive documentation, cookbooks, and prompting guides for extended thinking. "The first generally available hybrid reasoning model" and "first coding tool from Anthropic" were highlighted in social media announcements.
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.
Olympus has dropped (aka, Amazon Nova Micro|Lite|Pro|Premier|Canvas|Reel)
amazon-nova claude-3 llama-3-70b gemini-1.5-flash gpt-4o amazon anthropic google-deepmind sakana-ai-labs multimodality benchmarking model-merging model-performance model-architecture model-optimization population-based-learning philschmid bindureddy
Amazon announced the Amazon Nova family of multimodal foundation models at AWS Re:Invent, available immediately with no waitlist in configurations like Micro, Lite, Pro, Canvas, and Reel, with Premier and speech-to-speech coming next year. These models offer 2-4x faster token speeds and are 25%-400% cheaper than competitors like Anthropic Claude models, positioning Nova as a serious contender in AI engineering. Pricing undercuts models such as Google DeepMind Gemini Flash 8B, and some Nova models extend context length up to 300k tokens. However, benchmarking controversy exists as some evaluations show Nova scoring below Llama-3 70B in LiveBench AI metrics. Separately, CycleQD was introduced by Sakana AI Labs, using evolutionary computation for population-based model merging to develop niche LLM agents.
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.
Life after DPO (RewardBench)
gpt-3 gpt-4 gpt-5 gpt-6 llama-3-8b llama-3 claude-3 gemini x-ai openai mistral-ai anthropic cohere meta-ai-fair hugging-face nvidia reinforcement-learning-from-human-feedback direct-preference-optimization reward-models rewardbench language-model-history model-evaluation alignment-research preference-datasets personalization transformer-architecture nathan-lambert chris-manning elon-musk bindureddy rohanpaul_ai nearcyan
xAI raised $6 billion at a $24 billion valuation, positioning it among the most highly valued AI startups, with expectations to fund GPT-5 and GPT-6 class models. The RewardBench tool, developed by Nathan Lambert, evaluates reward models (RMs) for language models, showing Cohere's RMs outperforming open-source alternatives. The discussion highlights the evolution of language models from Claude Shannon's 1948 model to GPT-3 and beyond, emphasizing the role of RLHF (Reinforcement Learning from Human Feedback) and the newer DPO (Direct Preference Optimization) method. Notably, some Llama 3 8B reward model-focused models are currently outperforming GPT-4, Cohere, Gemini, and Claude on the RewardBench leaderboard, raising questions about reward hacking. Future alignment research directions include improving preference datasets, DPO techniques, and personalization in language models. The report also compares xAI's valuation with OpenAI, Mistral AI, and Anthropic, noting speculation about xAI's spending on Nvidia hardware.
Ten Commandments for Deploying Fine-Tuned Models
claude-3-opus claude-3 gpt-4o anthropic google openai fine-tuning prompt-engineering model-evaluation feature-alteration benchmarking model-performance open-source-models kyle-corbitt bindureddy alexalbert__
Gemini-in-Google-Slides is highlighted as a useful tool for summarizing presentations. Kyle Corbitt's talk on deploying fine-tuned models in production emphasizes avoiding fine-tuning unless necessary, focusing on prompting, data quality, appropriate model choice, and thorough evaluation. Anthropic showcased feature alteration in Claude AI, demonstrating control over model behavior and increased understanding of large language models. Open-source models like GPT-4o are approaching closed-source performance on benchmarks like MMLU for simple tasks, though advanced models remain necessary for complex automation.
ALL of AI Engineering in One Place
claude-3-sonnet claude-3 openai google-deepmind anthropic mistral-ai cohere hugging-face adept midjourney character-ai microsoft amazon nvidia salesforce mastercard palo-alto-networks axa novartis discord twilio tinder khan-academy sourcegraph mongodb neo4j hasura modular cognition anysphere perplexity-ai groq mozilla nous-research galileo unsloth langchain llamaindex instructor weights-biases lambda-labs neptune datastax crusoe covalent qdrant baseten e2b octo-ai gradient-ai lancedb log10 deepgram outlines crew-ai factory-ai interpretability feature-steering safety multilinguality multimodality rag evals-ops open-models code-generation gpus agents ai-leadership
The upcoming AI Engineer World's Fair in San Francisco from June 25-27 will feature a significantly expanded format with booths, talks, and workshops from top model labs like OpenAI, DeepMind, Anthropic, Mistral, Cohere, HuggingFace, and Character.ai. It includes participation from Microsoft Azure, Amazon AWS, Google Vertex, and major companies such as Nvidia, Salesforce, Mastercard, Palo Alto Networks, and more. The event covers 9 tracks including RAG, multimodality, evals/ops, open models, code generation, GPUs, agents, AI in Fortune 500, and a new AI leadership track. Additionally, Anthropic shared interpretability research on Claude 3 Sonnet, revealing millions of interpretable features that can be steered to modify model behavior, including safety-relevant features related to bias and unsafe content, though more research is needed for practical applications. The event offers a discount code for AI News readers.
Anthropic's "LLM Genome Project": learning & clamping 34m features on Claude Sonnet
claude-3-sonnet claude-3 anthropic scale-ai suno-ai microsoft model-interpretability dictionary-learning neural-networks feature-activation intentional-modifiability scaling mechanistic-interpretability emmanuel-ameisen alex-albert
Anthropic released their third paper in the MechInterp series, Scaling Monosemanticity, scaling interpretability analysis to 34 million features on Claude 3 Sonnet. This work introduces the concept of dictionary learning to isolate recurring neuron activation patterns, enabling more interpretable internal states by combining features rather than neurons. The paper reveals abstract features related to code, errors, sycophancy, crime, self-representation, and deception, demonstrating intentional modifiability by clamping feature values. The research marks a significant advance in model interpretability and neural network analysis at frontier scale.
Chameleon: Meta's (unreleased) GPT4o-like Omnimodal Model
chameleon gpt-4o gemini-1.5-flash claude-3 meta-ai-fair openai google-deepmind anthropic reddit multimodality early-fusion benchmarking model-training tokenization streaming tool-use vision coding hallucination-detection model-performance armen-aghajanyan sama alexandr-wang abacaj alexalbert__
Meta AI FAIR introduced Chameleon, a new multimodal model family with 7B and 34B parameter versions trained on 10T tokens of interleaved text and image data enabling "early fusion" multimodality that can natively output any modality. While reasoning benchmarks are modest, its "omnimodality" approach competes well with pre-GPT4o multimodal models. OpenAI launched GPT-4o, a model excelling in benchmarks like MMLU and coding tasks, with strong multimodal capabilities but some regression in ELO scores and hallucination issues. Google DeepMind announced Gemini 1.5 Flash, a small model with 1M context window and flash performance, highlighting convergence trends between OpenAI and Google models. Anthropic updated Claude 3 with streaming support, forced tool use, and vision tool integration for multimodal knowledge extraction. OpenAI also partnered with Reddit, raising industry attention.
Cohere Command R+, Anthropic Claude Tool Use, OpenAI Finetuning
c4ai-command-r-plus claude-3 gpt-3.5-turbo gemini mistral-7b gemma-2 claude-3-5 llama-3 vicuna cohere anthropic openai microsoft stability-ai opera-software meta-ai-fair google-deepmind mistral-ai tool-use multilingual-models rag fine-tuning quantum-computing audio-generation local-inference context-windows model-size-analysis model-comparison
Cohere launched Command R+, a 104B dense model with 128k context length focusing on RAG, tool-use, and multilingual capabilities across 10 key languages. It supports Multi-Step Tool use and offers open weights for research. Anthropic introduced tool use in beta for Claude, supporting over 250 tools with new cookbooks for practical applications. OpenAI enhanced its fine-tuning API with new upgrades and case studies from Indeed, SK Telecom, and Harvey, promoting DIY fine-tuning and custom model training. Microsoft achieved a quantum computing breakthrough with an 800x error rate improvement and the most usable qubits to date. Stability AI released Stable Audio 2.0, improving audio generation quality and control. The Opera browser added local inference support for large language models like Meta's Llama, Google's Gemma, and Vicuna. Discussions on Reddit highlighted Gemini's large context window, analysis of GPT-3.5-Turbo model size, and a battle simulation between Claude 3 and ChatGPT using local 7B models like Mistral and Gemma.
World_sim.exe
gpt-4 gpt-4o grok-1 llama-cpp claude-3-opus claude-3 gpt-5 nvidia nous-research stability-ai hugging-face langchain anthropic openai multimodality foundation-models hardware-optimization model-quantization float4 float6 retrieval-augmented-generation text-to-video prompt-engineering long-form-rag gpu-optimization philosophy-of-ai agi-predictions jensen-huang yann-lecun sam-altman
NVIDIA announced Project GR00T, a foundation model for humanoid robot learning using multimodal instructions, built on their tech stack including Isaac Lab, OSMO, and Jetson Thor. They revealed the DGX Grace-Blackwell GB200 with over 1 exaflop compute, capable of training GPT-4 1.8T parameters in 90 days on 2000 Blackwells. Jensen Huang confirmed GPT-4 has 1.8 trillion parameters. The new GB200 GPU supports float4/6 precision with ~3 bits per parameter and achieves 40,000 TFLOPs on fp4 with 2x sparsity.
Open source highlights include the release of Grok-1, a 340B parameter model, and Stability AI's SV3D, an open-source text-to-video generation solution. Nous Research collaborated on implementing Steering Vectors in Llama.CPP.
In Retrieval Augmented Generation (RAG), a new 5.5-hour tutorial builds a pipeline using open-source HF models, and LangChain released a video on query routing and announced integration with NVIDIA NIM for GPU-optimized LLM inference.
Prominent opinions include Yann LeCun distinguishing language from other cognitive abilities, Sam Altman predicting AGI arrival in 6 years with a leap from GPT-4 to GPT-5 comparable to GPT-3 to GPT-4, and discussions on the philosophical status of LLMs like Claude. There is also advice against training models from scratch for most companies.
Grok-1 in Bio
grok-1 mixtral miqu-70b claude-3-opus claude-3 claude-3-haiku xai mistral-ai perplexity-ai groq anthropic openai mixture-of-experts model-release model-performance benchmarking finetuning compute hardware-optimization mmlu model-architecture open-source memes sam-altman arthur-mensch daniel-han arav-srinivas francis-yao
Grok-1, a 314B parameter Mixture-of-Experts (MoE) model from xAI, has been released under an Apache 2.0 license, sparking discussions on its architecture, finetuning challenges, and performance compared to models like Mixtral and Miqu 70B. Despite its size, its MMLU benchmark performance is currently unimpressive, with expectations that Grok-2 will be more competitive. The model's weights and code are publicly available, encouraging community experimentation. Sam Altman highlighted the growing importance of compute resources, while Grok's potential deployment on Groq hardware was noted as a possible game-changer. Meanwhile, Anthropic's Claude continues to attract attention for its "spiritual" interaction experience and consistent ethical framework. The release also inspired memes and humor within the AI community.
MM1: Apple's first Large Multimodal Model
mm1 gemini-1 command-r claude-3-opus claude-3-sonnet claude-3-haiku claude-3 apple cohere anthropic hugging-face langchain multimodality vqa fine-tuning retrieval-augmented-generation open-source robotics model-training react reranking financial-agents yann-lecun francois-chollet
Apple announced the MM1 multimodal LLM family with up to 30B parameters, claiming performance comparable to Gemini-1 and beating larger older models on VQA benchmarks. The paper targets researchers and hints at applications in embodied agents and business/education. Yann LeCun emphasized that human-level AI requires understanding the physical world, memory, reasoning, and hierarchical planning, while Fran ois Chollet cautioned that NLP is far from solved despite LLM advances. Cohere released Command-R, a model for Retrieval Augmented Generation, and Anthropic highlighted the Claude 3 family (Opus, Sonnet, Haiku) for various application needs. Open-source hardware DexCap enables dexterous robot manipulation data collection affordably. Tools like CopilotKit simplify AI integration into React apps, and migration to Keras 3 with JAX backend offers faster training. New projects improve reranking for retrieval and add financial agents to LangChain. The content includes insights on AI progress, new models, open-source tools, and frameworks.
DeepMind SIMA: one AI, 9 games, 600 tasks, vision+language ONLY
llama-3 claude-3-opus claude-3 gpt-3.5-turbo deepmind cognition-labs deepgram modal-labs meta-ai-fair anthropic multimodality transformer software-engineering ai-agents ai-infrastructure training text-to-speech speech-to-text real-time-processing model-architecture benchmarking andrej-karpathy arav-srinivas francois-chollet yann-lecun soumith-chintala john-carmack
DeepMind SIMA is a generalist AI agent for 3D virtual environments evaluated on 600 tasks across 9 games using only screengrabs and natural language instructions, achieving 34% success compared to humans' 60%. The model uses a multimodal Transformer architecture. Andrej Karpathy outlines AI autonomy progression in software engineering, while Arav Srinivas praises Cognition Labs' AI agent demo. François Chollet expresses skepticism about automating software engineering fully. Yann LeCun suggests moving away from generative models and reinforcement learning towards human-level AI. Meta's Llama-3 training infrastructure with 24k H100 Cluster Pods is shared by Soumith Chintala and Yann LeCun. Deepgram's Aura offers low-latency speech APIs, and Modal Labs' Devin AI demonstrates document navigation and interaction with ComfyUI. Memes and humor circulate in the AI community.
Fixing Gemma
gemma claude-3-opus claude-3 mistral-large gpt-4 google unsloth anthropic mistral-ai finetuning numerical-precision benchmarking structured-data-extraction adaptive-equalizer information-theory hallucination-detection model-stability daniel-han yann-lecun francois-chollet arav-srinivas _aidan_clark_
Google's Gemma model was found unstable for finetuning until Daniel Han from Unsloth AI fixed 8 bugs, improving its implementation. Yann LeCun explained technical details of a pseudo-random bit sequence for adaptive equalizers, while François Chollet discussed the low information bandwidth of the human visual system. Arav Srinivas reported that Claude 3 Opus showed no hallucinations in extensive testing, outperforming GPT-4 and Mistral-Large in benchmarks. Reflections from Yann LeCun highlight ongoing AI progress toward human-level intelligence. The community is shifting pipelines to work better with Claude models, and emotional experiences in ML development were shared by Aidan Clark.
Not much happened today
claude-3 claude-3-opus claude-3-sonnet gpt-4 gemma-2b anthropic perplexity langchain llamaindex cohere accenture mistral-ai snowflake together-ai hugging-face european-space-agency google gpt4all multimodality instruction-following out-of-distribution-reasoning robustness enterprise-ai cloud-infrastructure open-datasets model-deployment model-discoverability generative-ai image-generation
Anthropic released Claude 3, replacing Claude 2.1 as the default on Perplexity AI, with Claude 3 Opus surpassing GPT-4 in capability. Debate continues on whether Claude 3's performance stems from emergent properties or pattern matching. LangChain and LlamaIndex added support for Claude 3 enabling multimodal and tool-augmented applications. Despite progress, current models still face challenges in out-of-distribution reasoning and robustness. Cohere partnered with Accenture for enterprise AI search, while Mistral AI and Snowflake collaborate to provide LLMs on Snowflake's platform. Together AI Research integrates Deepspeed innovations to accelerate generative AI infrastructure. Hugging Face and the European Space Agency released a large earth observation dataset, and Google open sourced Gemma 2B, optimized for smartphones via the MLC-LLM project. GPT4All improved model discoverability for open models. The AI community balances excitement over new models with concerns about limitations and robustness, alongside growing enterprise adoption and open-source contributions. Memes and humor continue to provide social commentary.
Stable Diffusion 3 — Rombach & Esser did it again!
stable-diffusion-3 claude-3 orca dolphincoder-starcoder2-15b stability-ai anthropic microsoft latitude perplexity-ai llamaindex tripo-ai diffusion-models multimodality benchmarking human-evaluation text-generation image-generation 3d-modeling fine-tuning roleplay coding dataset-release soumith-chintala bill-peebles swyx kevinafischer jeremyphoward akhaliq karinanguyen_ aravsrinivas
Over 2500 new community members joined following Soumith Chintala's shoutout, highlighting growing interest in SOTA LLM-based summarization. The major highlight is the detailed paper release of Stable Diffusion 3 (SD3), showcasing advanced text-in-image control and complex prompt handling, with the model outperforming other SOTA image generation models in human-evaluated benchmarks. The SD3 model is based on an enhanced Diffusion Transformer architecture called MMDiT. Meanwhile, Anthropic released Claude 3 models, noted for human-like responses and emotional depth, scoring 79.88% on HumanEval but costing over twice as much as GPT-4. Microsoft launched new Orca-based models and datasets, and Latitude released DolphinCoder-StarCoder2-15b with strong coding capabilities. Integration of image models by Perplexity AI and 3D CAD generation by PolySpectra powered by LlamaIndex were also highlighted. "SD3's win rate beats all other SOTA image gen models (except perhaps Ideogram)" and "Claude 3 models are very good at generating d3 visualizations from text descriptions."
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.