All tags
Company: "hugging-face"
not much happened today
codex claude-4-opus claude-4-sonnet gemini-2.5-pro gemini-2.5 qwen-2.5-vl qwen-3 playdiffusion openai anthropic google perplexity-ai bing playai suno hugging-face langchain-ai qwen mlx assemblyai llamacloud fine-tuning model-benchmarking text-to-video agentic-ai retrieval-augmented-generation open-source-models speech-editing audio-processing text-to-speech ultra-low-latency multimodality public-notebooks sama gdb kevinweil lmarena_ai epochairesearch reach_vb wightmanr deeplearningai mervenoyann awnihannun jordirib1 aravsrinivas omarsar0 lioronai jerryjliu0 nerdai tonywu_71 _akhaliq clementdelangue _mfelfel
OpenAI rolled out Codex to ChatGPT Plus users with internet access and fine-grained controls, improving memory features for free users. Anthropic's Claude 4 Opus and Sonnet models lead coding benchmarks, while Google's Gemini 2.5 Pro and Flash models gain recognition with new audio capabilities. Qwen 2.5-VL and Qwen 3 quantizations are noted for versatility and support. Bing Video Creator launched globally enabling text-to-video generation, and Perplexity Labs sees increased demand for travel search. New agentic AI tools and RAG innovations include LlamaCloud and FedRAG. Open-source releases include Holo-1 for web navigation and PlayAI's PlayDiffusion for speech editing. Audio and multimodal advances feature Suno's music editing upgrades, Google's native TTS in 24+ languages, and Universal Streaming's ultra-low latency speech-to-text. Google NotebookLM now supports public notebooks. "Codex's internet access brings tradeoffs, with explicit warnings about risk" and "Gemini 2.5 Pro is cited as a daily driver by users".
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.
Mary Meeker is so back: BOND Capital AI Trends report
qwen-3-8b anthropic hugging-face deepseek attention-mechanisms inference arithmetic-intensity transformers model-optimization interpretability model-quantization training tri_dao fleetwood___ teortaxestex awnihannun lateinteraction neelnanda5 eliebakouch _akhaliq
Mary Meeker returns with a comprehensive 340-slide report on the state of AI, highlighting accelerating tech cycles, compute growth, and comparisons of ChatGPT to early Google and other iconic tech products. The report also covers enterprise traction and valuation of major AI companies. On Twitter, @tri_dao discusses an "ideal" inference architecture featuring attention variants like GTA, GLA, and DeepSeek MLA with high arithmetic intensity (~256), improving efficiency and model quality. Other highlights include the release of 4-bit DWQ of DSR1 Qwen3 8B on Hugging Face, AnthropicAI's open-source interpretability tools for LLMs, and discussions on transformer training and abstractions by various researchers.
Gemini 2.5 Pro Preview 05-06 (I/O edition) - the SOTA vision+coding model
gemini-2.5-pro claude-3.7-sonnet llama-nemotron qwen3 google-deepmind nvidia alibaba hugging-face multimodality coding reasoning model-release speech-recognition recommender-systems benchmarking demishassabis _philschmid lmarena_ai scaling01 fchollet
Gemini 2.5 Pro has been updated with enhanced multimodal image-to-code capabilities and dominates the WebDev Arena Leaderboard, surpassing Claude 3.7 Sonnet in coding and other tasks. Nvidia released the Llama-Nemotron model family on Hugging Face, noted for efficient reasoning and inference. Alibaba's Qwen3 models range from 0.6B to 235B parameters, including dense and MoE variants. KerasRS was released by Fran ois Chollet as a new recommender system library compatible with JAX, PyTorch, and TensorFlow, optimized for TPUs. These updates highlight advancements in coding, reasoning, and speech recognition models.
LlamaCon: Meta AI gets into the Llama API platform business
llama-4 qwen3 qwen3-235b-a22b qwen3-30b-a3b qwen3-4b qwen2-5-72b-instruct o3-mini meta-ai-fair cerebras groq alibaba vllm ollama llamaindex hugging-face llama-cpp model-release fine-tuning reinforcement-learning moe multilingual-models model-optimization model-deployment coding benchmarking apache-license reach_vb huybery teortaxestex awnihannun thezachmueller
Meta celebrated progress in the Llama ecosystem at LlamaCon, launching an AI Developer platform with finetuning and fast inference powered by Cerebras and Groq hardware, though it remains waitlisted. Meanwhile, Alibaba released the Qwen3 family of large language models, including two MoE models and six dense models ranging from 0.6B to 235B parameters, with the flagship Qwen3-235B-A22B achieving competitive benchmark results and supporting 119 languages and dialects. The Qwen3 models are optimized for coding and agentic capabilities, are Apache 2.0 licensed, and have broad deployment support including local usage with tools like vLLM, Ollama, and llama.cpp. Community feedback highlights Qwen3's scalable performance and superiority over models like OpenAI's o3-mini.
Cognition's DeepWiki, a free encyclopedia of all GitHub repos
o4-mini perception-encoder qwen-2.5-vl dia-1.6b grok-3 gemini-2.5-pro claude-3.7 gpt-4.1 cognition meta-ai-fair alibaba hugging-face openai perplexity-ai vllm vision text-to-speech reinforcement-learning ocr model-releases model-integration open-source frameworks chatbots model-selector silas-alberti mervenoyann reach_vb aravsrinivas vikparuchuri lioronai
Silas Alberti of Cognition announced DeepWiki, a free encyclopedia of all GitHub repos providing Wikipedia-like descriptions and Devin-backed chatbots for public repos. Meta released Perception Encoders (PE) with A2.0 license, outperforming InternVL3 and Qwen2.5VL on vision tasks. Alibaba launched the Qwen Chat App for iOS and Android. Hugging Face integrated the Dia 1.6B SoTA text-to-speech model via FAL. OpenAI expanded deep research usage with a lightweight version powered by o4-mini model, now available to free users. Perplexity AI updated their model selector with Grok 3 Beta, o4-mini, and support for models like gemini 2.5 pro, claude 3.7, and gpt-4.1. vLLM project introduced OpenRLHF framework for reinforcement learning with human feedback. Surya OCR alpha model supports 90+ languages and LaTeX. MegaParse open-source library was introduced for LLM-ready data formats.
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.
Google's Agent2Agent Protocol (A2A)
kimi-vl-a3b gpt-4o llama-4-scout llama-4-maverick llama-4-behemoth deepcoder-14b o3-mini o1 llama-3.1-nemotron-ultra-253b deepseek-r1 google google-deepmind moonshot-ai meta-ai-fair uc-berkeley openai nvidia hugging-face togethercompute deepseek agent-interoperability multimodality vision math reinforcement-learning coding model-training open-source model-benchmarking context-windows streaming push-notifications enterprise-authentication model-release reach_vb _akhaliq epochairesearch artificialanlys winglian danielhanchen yuchenj_uw jeremyphoward
Google Cloud Next announcements featured the launch of Google and DeepMind's full MCP support and a new Agent to Agent protocol designed for agent interoperability with multiple partners. The protocol includes components like the Agent Card, Task communication channels, Enterprise Auth and Observability, and Streaming and Push Notification support. On the model front, Moonshot AI released Kimi-VL-A3B, a multimodal model with 128K context and strong vision and math benchmark performance, outperforming gpt-4o. Meta AI introduced smaller versions of llama-4 family models: llama-4-scout and llama-4-maverick, with a larger Behemoth model still in training. DeepCoder 14B from UC Berkeley is an open-source coding model rivaling openai's o3-mini and o1 models, trained with reinforcement learning on 24K coding problems. Nvidia released llama-3.1-nemotron-ultra-253b on Hugging Face, noted for beating llama-4-behemoth and maverick and competing with deepseek-r1.
not much happened today
gpt-4o deepseek-v3-0324 gemini-2.5-pro gemini-3 claude-3.7-sonnet openai hugging-face sambanova google-cloud instruction-following image-generation content-filtering model-performance api coding model-deployment benchmarking model-release abacaj nrehiew_ sama joannejang giffmana lmarena_ai _philschmid
OpenAI announced the new GPT-4o model with enhanced instruction-following, complex problem-solving, and native image generation capabilities. The model shows improved performance in math, coding, and creativity, with features like transparent background image generation. Discussions around content filtering and policy for image generation emphasize balancing creative freedom and harm prevention. DeepSeek V3-0324 APIs, available on Hugging Face and powered by SambaNovaAI, outperform benchmarks and models like Gemini 2.0 Pro and Claude 3.7 Sonnet. Gemini 2.5 Pro is recommended for coding, and Gemini 3 can be deployed easily on Google Cloud Vertex AI via the new Model Garden SDK. The Gemma 3 Technical Report has been released on arXiv.
Every 7 Months: The Moore's Law for Agent Autonomy
claude-3-7-sonnet llama-4 phi-4-multimodal gpt-2 cosmos-transfer1 gr00t-n1-2b orpheus-3b metr nvidia hugging-face canopy-labs meta-ai-fair microsoft agent-autonomy task-completion multimodality text-to-speech robotics foundation-models model-release scaling-laws fine-tuning zero-shot-learning latency reach_vb akhaliq drjimfan scaling01
METR published a paper measuring AI agent autonomy progress, showing it has doubled every 7 months since 2019 (GPT-2). They introduced a new metric, the 50%-task-completion time horizon, where models like Claude 3.7 Sonnet achieve 50% success in about 50 minutes. Projections estimate 1 day autonomy by 2028 and 1 month autonomy by late 2029. Meanwhile, Nvidia released Cosmos-Transfer1 for conditional world generation and GR00T-N1-2B, an open foundation model for humanoid robot reasoning with 2B parameters. Canopy Labs introduced Orpheus 3B, a high-quality text-to-speech model with zero-shot voice cloning and low latency. Meta reportedly delayed Llama-4 release due to performance issues. Microsoft launched Phi-4-multimodal.
Cohere's Command A claims #3 open model spot (after DeepSeek and Gemma)
command-a mistral-ai-small-3.1 smoldocling qwen-2.5-vl cohere mistral-ai hugging-face context-windows multilinguality multimodality fine-tuning benchmarking ocr model-performance model-releases model-optimization aidangomez sophiamyang mervenoyann aidan_mclau reach_vb lateinteraction
Cohere's Command A model has solidified its position on the LMArena leaderboard, featuring an open-weight 111B parameter model with an unusually long 256K context window and competitive pricing. Mistral AI released the lightweight, multilingual, and multimodal Mistral AI Small 3.1 model, optimized for single RTX 4090 or Mac 32GB RAM setups, with strong performance on instruct and multimodal benchmarks. The new OCR model SmolDocling offers fast document reading with low VRAM usage, outperforming larger models like Qwen2.5VL. Discussions highlight the importance of system-level improvements over raw LLM advancements, and MCBench is recommended as a superior AI benchmark for evaluating model capabilities across code, aesthetics, and awareness.
not much happened today
gemini-2.0-flash-thinking command-a qwq-32b gemma-3-27b gemma-3 shieldgemma-2 llama-3-70b deepseek-r1 o1-mini deepseek-v3 google-deepmind cohere meta-ai-fair alibaba hugging-face model-updates model-performance benchmarking reinforcement-learning transformers normalization-layers image-generation vision memory-efficiency context-windows fine-tuning yann-lecun
Google DeepMind announced updates to Gemini 2.0, including an upgraded Flash Thinking model with stronger reasoning and native image generation capabilities. Cohere launched Command A, a 111B parameter dense model with a 256K context window and competitive pricing, available on Hugging Face. Meta AI proposed Dynamic Tanh (DyT) as a replacement for normalization layers in Transformers, supported by Yann LeCun. Alibaba released QwQ-32B, a 32.5B parameter model excelling in math and coding, fine-tuned with reinforcement learning and freely available under Apache 2.0 license. Google DeepMind also released Gemma 3 models ranging from 1B to 27B parameters with a 128K token context window and over 140 language support, plus ShieldGemma 2, an image safety checker. Benchmarking shows Gemma 3 27B has strong vision and memory efficiency but is outperformed by larger models like Llama 3.3 70B and DeepSeek V3 671B. The Hugging Face LLM leaderboard history was shared by @_lewtun.
not much happened today
deepseek-r1 gemma-3 gemma-3-27b openai nvidia deepseek hugging-face fp8 model-efficiency hardware-requirements quantization benchmarking model-deployment open-source sam-altman
DeepSeek R1 demonstrates significant efficiency using FP8 precision, outperforming Gemma 3 27B in benchmarks with a Chatbot Arena Elo Score of 1363 vs. 1338, requiring substantial hardware like 32 H100 GPUs and 2,560GB VRAM. OpenAI labels DeepSeek as "state-controlled" and calls for bans on "PRC-produced" models, sparking community backlash accusing OpenAI and Sam Altman of anti-competitive behavior. Discussions emphasize DeepSeek's openness and affordability compared to OpenAI, with users highlighting its local and Hugging Face deployment options. Meanwhile, Gemma 3 receives mixed community feedback on creativity and worldbuilding.
The new OpenAI Agents Platform
reka-flash-3 o1-mini claude-3-7-sonnet llama-3-3-70b sonic-2 qwen-chat olympiccoder openai reka-ai hugging-face deepseek togethercompute alibaba ai-agents api model-releases fine-tuning reinforcement-learning model-training model-inference multimodality voice-synthesis gpu-clusters model-distillation performance-optimization open-source sama reach_vb
OpenAI introduced a comprehensive suite of new tools for AI agents, including the Responses API, Web Search Tool, Computer Use Tool, File Search Tool, and an open-source Agents SDK with integrated observability tools, marking a significant step towards the "Year of Agents." Meanwhile, Reka AI open-sourced Reka Flash 3, a 21B parameter reasoning model that outperforms o1-mini and powers their Nexus platform, with weights available on Hugging Face. The OlympicCoder series surpassed Claude 3.7 Sonnet and much larger models on competitive coding benchmarks. DeepSeek built a 32K GPU cluster capable of training V3-level models in under a week and is exploring AI distillation. Hugging Face announced Cerebras inference support, achieving over 2,000 tokens/s on Llama 3.3 70B, 70x faster than leading GPUs. Reka's Sonic-2 voice AI model delivers 40ms latency via the Together API. Alibaba's Qwen Chat enhanced its multimodal interface with video understanding up to 500MB, voice-to-text, guest mode, and expanded file uploads. Sama praised OpenAI's new API as "one of the most well-designed and useful APIs ever."
not much happened today
gpt-4.5 claude-3.7-sonnet deepseek-r1 smolagents-codeagent gpt-4o llama-3-8b tinyr1-32b-preview r1-searcher forgetting-transformer nanomoe openai deepseek hugging-face mixture-of-experts reinforcement-learning kv-cache-compression agentic-ai model-distillation attention-mechanisms model-compression minimax model-pretraining andrej-karpathy cwolferesearch aymericroucher teortaxestex jonathanross321 akhaliq
The AI news recap highlights several key developments: nanoMoE, a PyTorch implementation of a mid-sized Mixture-of-Experts (MoE) model inspired by Andrej Karpathy's nanoGPT, enables pretraining on commodity hardware within a week. An agentic leaderboard ranks LLMs powering smolagents CodeAgent, with GPT-4.5 leading, followed by Claude-3.7-Sonnet. Discussions around DeepSeek-R1 emphasize AI model commoditization, with DeepSeek dubbed the "OpenAI of China." Q-Filters offer a training-free method for KV cache compression in autoregressive models, achieving 32x compression with minimal perplexity loss. The PokéChamp minimax language agent, powered by GPT-4o and Llama-3-8b, demonstrates strong performance in Pokémon battles. Other notable models include TinyR1-32B-Preview with Branch-Merge Distillation, R1-Searcher incentivizing search capability via reinforcement learning, and the Forgetting Transformer using a Forget Gate in softmax attention. These advancements reflect ongoing innovation in model architectures, compression, reinforcement learning, and agentic AI.
DeepSeek's Open Source Stack
qwen-qwq-32b start character-3 gemini gemini-2.0 mercury-coder gpt-4.5 jamba-mini-1.6 gemini-2.0-flash gpt-4o-mini mistral-small-3 mistral-ocr deepseek pyspur hugging-face togethercompute hedra-labs google-deepmind deeplearningai openai ai21-labs mistral-ai fine-tuning benchmarking multimodality code-generation diffusion-models model-performance model-optimization ocr embedding-models context-windows runtime-limits _akhaliq lmarena_ai reach_vb danielhanchen _philschmid aidan_mclau vikhyatk jerryjliu0
DeepSeek's Open Source Week was summarized by PySpur, highlighting multiple interesting releases. The Qwen QwQ-32B model was fine-tuned into START, excelling in PhD-level science QA and math benchmarks. Character-3, an omnimodal AI video generation model by Hedra Labs and Together AI, enables realistic animated content creation. Google DeepMind introduced the Gemini embedding model with an 8k context window, ranking #1 on MMTEB, alongside the Gemini 2.0 Code Executor supporting Python libraries and auto-fix features. Inception Labs' Mercury Coder is a diffusion-based code generation model offering faster token processing. OpenAI released GPT-4.5, their largest model yet but with less reasoning ability than some competitors. AI21 Labs launched Jamba Mini 1.6, noted for superior output speed compared to Gemini 2.0 Flash, GPT-4o mini, and Mistral Small 3. A new dataset of 1.9M scanned pages was released for OCR benchmarking, with Mistral OCR showing competitive but not top-tier document parsing performance compared to LLM/LVM-powered methods. "Cracked engineers are all you need."
not much happened today
jamba-1.6 mistral-ocr qwq-32b o1 o3-mini instella llama-3-2-3b gemma-2-2b qwen-2-5-3b babel-9b babel-83b gpt-4o claude-3-7-sonnet ai21-labs mistral-ai alibaba openai amd anthropic hugging-face multimodality ocr multilinguality structured-output on-prem-deployment reasoning benchmarking api open-source model-training gpu-optimization prompt-engineering function-calling
AI21 Labs launched Jamba 1.6, touted as the best open model for private enterprise deployment, outperforming Cohere, Mistral, and Llama on benchmarks like Arena Hard. Mistral AI released a state-of-the-art multimodal OCR model with multilingual and structured output capabilities, available for on-prem deployment. Alibaba Qwen introduced QwQ-32B, an open-weight reasoning model with 32B parameters and cost-effective usage, showing competitive benchmark scores. OpenAI released o1 and o3-mini models with advanced API features including streaming and function calling. AMD unveiled Instella, open-source 3B parameter language models trained on AMD Instinct MI300X GPUs, competing with Llama-3.2-3B and others. Alibaba also released Babel, open multilingual LLMs performing comparably to GPT-4o. Anthropic launched Claude 3.7 Sonnet, enhancing reasoning and prompt engineering capabilities.
not much happened today
chatgpt-4o deepseek-r1 o3 o3-mini gemini-2-flash qwen-2.5 qwen-0.5b hugging-face openai perplexity-ai deepseek-ai gemini qwen metr_evals reasoning benchmarking model-performance prompt-engineering model-optimization model-deployment small-language-models mobile-ai ai-agents speed-optimization _akhaliq aravsrinivas lmarena_ai omarsar0 risingsayak
Smolagents library by Huggingface continues trending. ChatGPT-4o latest version
chatgpt-40-latest-20250129 released. DeepSeek R1 671B sets speed record at 198 t/s, fastest reasoning model, recommended with specific prompt settings. Perplexity Deep Research outperforms models like Gemini Thinking, o3-mini, and DeepSeek-R1 on Humanity's Last Exam benchmark with 21.1% score and 93.9% accuracy on SimpleQA. ChatGPT-4o ranks #1 on Arena leaderboard in multiple categories except math. OpenAI's o3 model powers Deep Research tool for ChatGPT Pro users. Gemini 2 Flash and Qwen 2.5 models support LLMGrading verifier. Qwen 2.5 models added to PocketPal app. MLX shows small LLMs like Qwen 0.5B generate tokens at high speed on M4 Max and iPhone 16 Pro. Gemini Flash 2.0 leads new AI agent leaderboard. DeepSeek R1 is most liked on Hugging Face with over 10 million downloads. 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.
s1: Simple test-time scaling (and Kyutai Hibiki)
qwen-2.5-32b gemini-2.0-flash smollm2 granite-vision-3.1-2b google-deepmind qwen gemini hugging-face ibm deepseek reasoning fine-tuning scaling-laws open-source-models data-centric-training vision multilingual-models language-model-reasoning niklas-muennighoff
"Wait" is all you need introduces a novel reasoning model finetuned from Qwen 2.5 32B using just 1000 questions with reasoning traces distilled from Gemini 2.0 Flash Thinking, enabling controllable test-time compute by appending "Wait" to extend reasoning. Lead author Niklas Muennighoff, known for work on Bloom, StarCoder, and BIG-bench, highlights this method's efficiency and its reproduction of the famous o1 scaling chart. Additionally, Kyutai Moshi's Hibiki project demonstrates impressive offline French-English live translation on iPhone. Recent AI model releases include DeepSeek R1 and R3 open source models, potentially marking a major open-source milestone, Hugging Face's SmolLM2 emphasizing data-centric training for small LMs, and IBM's Granite-Vision-3.1-2B, a small vision-language model with strong performance. Key research papers spotlight LIMO for minimal demonstration reasoning achieving high accuracy on AIME and MATH benchmarks, and Token-Assisted Reasoning mixing latent and text tokens to improve language model reasoning.
Gemini 2.0 Flash GA, with new Flash Lite, 2.0 Pro, and Flash Thinking
gemini-2.0-flash gemini-2.0-flash-lite gemini-2.0-pro-experimental gemini-1.5-pro deepseek-r1 gpt-2 llama-3-1 google-deepmind hugging-face anthropic multimodality context-windows cost-efficiency pretraining fine-tuning reinforcement-learning transformer tokenization embeddings mixture-of-experts andrej-karpathy jayalammar maartengr andrewyng nearcyan
Google DeepMind officially launched Gemini 2.0 models including Flash, Flash-Lite, and Pro Experimental, with Gemini 2.0 Flash outperforming Gemini 1.5 Pro while being 12x cheaper and supporting multimodal input and a 1 million token context window. Andrej Karpathy released a 3h31m video deep dive into large language models, covering pretraining, fine-tuning, and reinforcement learning with examples like GPT-2 and Llama 3.1. A free course on Transformer architecture was introduced by Jay Alammar, Maarten Gr, and Andrew Ng, focusing on tokenizers, embeddings, and mixture-of-expert models. DeepSeek-R1 reached 1.2 million downloads on Hugging Face with a detailed 36-page technical report. Anthropic increased rewards to $10K and $20K for their jailbreak challenge, while BlueRaven extension was updated to hide Twitter metrics for unbiased engagement.
How To Scale Your Model, by DeepMind
qwen-0.5 google-deepmind deepseek hugging-face transformers inference high-performance-computing robotics sim2real mixture-of-experts reinforcement-learning bias-mitigation rust text-generation open-source omarsar0 drjimfan tairanhe99 guanyashi lioronai _philschmid awnihannun clementdelangue
Researchers at Google DeepMind (GDM) released a comprehensive "little textbook" titled "How To Scale Your Model" covering modern Transformer architectures, inference optimizations beyond O(N^2) attention, and high-performance computing concepts like rooflines. The resource includes practical problems and real-time comment engagement. On AI Twitter, several key updates include the open-sourced humanoid robotics model ASAP inspired by athletes like Cristiano Ronaldo, LeBron James, and Kobe Bryant; a new paper on Mixture-of-Agents proposing the Self-MoA method for improved LLM output aggregation; training of reasoning LLMs using the GRPO algorithm from DeepSeek demonstrated on Qwen 0.5; findings on bias in LLMs used as judges highlighting the need for multiple independent evaluations; and the release of mlx-rs, a Rust library for machine learning with examples including Mistral text generation. Additionally, Hugging Face launched an AI app store featuring over 400,000 apps with 2,000 new daily additions and 2.5 million weekly visits, enabling AI-powered app search and categorization.
not much happened today
deepseek-r1 deepseek-v3 coder-v2 prover deepseek hugging-face dell openai instruction-tuning performance-benchmarks model-deployment training-costs hardware-scalability ai-safety risk-mitigation ethical-ai open-source gpu-utilization yann-lecun yoshua-bengio francois-chollet giffman
DeepSeek-R1 and DeepSeek-V3 models have made significant advancements, trained on an instruction-tuning dataset of 1.5M samples with 600,000 reasoning and 200,000 non-reasoning SFT data. The models demonstrate strong performance benchmarks and are deployed on-premise via collaborations with Dell and Hugging Face. Training costs are estimated around $5.5M to $6M, with efficient hardware utilization on 8xH100 servers. The International AI Safety Report highlights risks such as malicious use, malfunctions, and systemic risks including AI-driven cyberattacks. Industry leaders like Yann LeCun and Yoshua Bengio provide insights on market reactions, AI safety, and ethical considerations, with emphasis on AI's role in creativity and economic incentives.
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
oute-tts-0.3-1b oute-tts-0.3-500m olm-1b qwen-2.5-0.5b hover gpt-4o deepseek-v3 harvey meta-ai-fair stability-ai alibaba deepseek hugging-face text-to-speech zero-shot-learning multilinguality emotion-control motor-control reinforcement-learning local-ai distributed-inference pipeline-parallelism mathematical-reasoning process-reward-models legal-ai education-ai ai-security humor reach_vb drjimfan vikhyatk mervenoyann aiatmeta iscienceluvr alibaba_qwen awnihannun ajeya_cotra emollick qtnx_ designerx
Harvey secured a new $300M funding round. OuteTTS 0.3 1B & 500M text-to-speech models were released featuring zero-shot voice cloning, multilingual support (en, jp, ko, zh, fr, de), and emotion control, powered by OLMo-1B and Qwen 2.5 0.5B. The HOVER model, a 1.5M-parameter neural net for agile motor control, was introduced, leveraging human motion capture datasets and massively parallel reinforcement learning. kokoro.js enables running AI models locally in browsers with minimal dependencies. Meta AI awarded $200K LLM evaluation grants for projects on regional language understanding, complex reasoning, and interactive programming environments. Stability AI's Twitter account was hacked, prompting security warnings. Alibaba Qwen improved Process Reward Models (PRMs) for better mathematical reasoning using a consensus filtering mechanism. DeepSeek V3 uses pipeline parallelism to enhance distributed inference and long-context generation efficiency. Discussions on AI policy in legal frameworks and AI's role in democratizing education were highlighted. Lighthearted AI-related humor was also shared.
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.
not much happened this weekend
o3 o1 opus sonnet octave openai langchain hume x-ai amd nvidia meta-ai-fair hugging-face inference-time-scaling model-ensembles small-models voice-cloning fine-math-dataset llm-agent-framework benchmarking software-stack large-concept-models latent-space-reasoning mechanistic-interpretability planning speech-language-models lisa-su clementdelangue philschmid neelnanda5
o3 model gains significant attention with discussions around its capabilities and implications, including an OpenAI board member referencing "AGI." LangChain released their State of AI 2024 survey. Hume announced OCTAVE, a 3B parameter API-only speech-language model with voice cloning. x.ai secured a $6B Series C funding round. Discussions highlight inference-time scaling, model ensembles, and the surprising generalization ability of small models. New tools and datasets include FineMath, the best open math dataset on Hugging Face, and frameworks for LLM agents. Industry updates cover a 5-month benchmarking of AMD MI300X vs Nvidia H100 + H200, insights from a meeting with Lisa Su on AMD's software stack, and open AI engineering roles. Research innovations include Large Concept Models (LCM) from Meta AI, Chain of Continuous Thought (Coconut) for latent space reasoning, and mechanistic interpretability initiatives.
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.
Genesis: Generative Physics Engine for Robotics (o1-mini version)
o1 o1-preview gpt-4o claude-3.5-sonnet gemini-2.0-pro llama-3-3b llama-3-70b openai google-deepmind meta-ai-fair hugging-face function-calling structured-outputs vision performance-benchmarks sdk webrtc reasoning math code-generation transformer-architecture model-training humanoid-robots search model-efficiency dataset-sharing aidan_mclau sundarpichai adcock_brett
OpenAI launched the o1 model API featuring function calling, structured outputs, vision support, and developer messages, achieving 60% fewer reasoning tokens than its preview. The model excels in math and code with a 0.76 LiveBench Coding score, outperforming Sonnet 3.5. Beta SDKs for Go and Java and WebRTC support with 60% lower prices were also released. Google Gemini 2.0 Pro (Gemini Exp 1206) deployment accelerated, showing improved coding, math, and reasoning performance. Meta AI FAIR introduced research on training transformers directly on raw bytes using dynamic entropy-based patching. Commercial humanoid robots were successfully deployed by an industry player. Hugging Face researchers demonstrated that their 3B Llama model can outperform the 70B Llama model on MATH-500 accuracy using search techniques, highlighting efficiency gains with smaller models. Concerns about reproducibility and domain-specific limitations were noted.
Meta Apollo - Video Understanding up to 1 hour, SOTA Open Weights
apollo-1b apollo-3b apollo-7b veo-2 imagen-3 llama-3-70b llama-3b command-r7b llama-1b llama-8b chatgpt meta-ai-fair hugging-face google-deepmind openai figure-ai klarna cohere notion video-understanding scaling-consistency benchmarking temporal-ocr egocentric-perception spatial-perception reasoning video-generation physics-simulation voice-features map-integration language-expansion test-time-compute-scaling humanoid-robots ai-integration search-optimization self-recognition self-preference-bias akhaliq _lewtun clementdelangue adcock_brett rohanpaul_ai swyx shaneguML
Meta released Apollo, a new family of state-of-the-art video-language models available in 1B, 3B, and 7B sizes, featuring "Scaling Consistency" for efficient scaling and introducing ApolloBench, which speeds up video understanding evaluation by 41× across five temporal perception categories. Google Deepmind launched Veo 2, a 4K video generation model with improved physics and camera control, alongside an enhanced Imagen 3 image model. OpenAI globally rolled out ChatGPT search with advanced voice and map features and discussed a potential $2,000/month "ChatGPT Max" tier. Research highlights include achieving Llama 70B performance using Llama 3B via test-time compute scaling and expanding Command R7B language support from 10 to 23 languages. Industry updates feature Figure AI delivering humanoid robots commercially and Klarna reducing workforce through AI. Notion integrated Cohere Rerank for better search. Studies reveal LLMs can recognize their own writing style and show self-preference bias. Discussions note video processing progress outpacing text due to better signal-per-compute and data evaluation.
OpenAI Sora Turbo and Sora.com
sora-turbo o1 claude-3.5-sonnet claude-3.5 gemini llama-3-3-euryale-v2.3 mistral-large behemoth endurance-v1.1 openai google nvidia hugging-face mistral-ai text-to-video-generation quantum-computing coding-capabilities transformers algorithmic-innovation storytelling roleplay model-parameter-tuning anti-monopoly-investigation sama sundarpichai bindureddy denny_zhou nrehiew_
OpenAI launched Sora Turbo, enabling text-to-video generation for ChatGPT Plus and Pro users with monthly generation limits and regional restrictions in Europe and the UK. Google announced a quantum computing breakthrough with the development of the Willow chip, potentially enabling commercial quantum applications. Discussions on O1 model performance highlighted its lag behind Claude 3.5 Sonnet and Gemini in coding tasks, with calls for algorithmic innovation beyond transformer scaling. The Llama 3.3 Euryale v2.3 model was praised for storytelling and roleplay capabilities, with users suggesting parameter tuning to reduce creative liberties and repetition. Alternatives like Mistral-Large, Behemoth, and Endurance v1.1 were also noted. Additionally, Nvidia faces an anti-monopoly investigation in China. Memes and humor around GPU issues and embargo mishaps were popular on social media.
Meta Llama 3.3: 405B/Nova Pro performance at 70B price
llama-3-70b llama-3.3-70b gpt-4o gemini-exp-1206 meta-ai-fair openai google-deepmind hugging-face llamacloud reinforcement-learning fine-tuning model-performance document-processing pricing-models alignment online-rl sama steven-heidel aidan_mclau lmarena_ai oriolvinyalsml jerryjliu0
Meta AI released Llama 3.3 70B, matching the performance of the 405B model with improved efficiency using "a new alignment process and progress in online RL techniques". OpenAI announced Reinforcement Fine-Tuning (RFT) for building expert models with limited data, offering alpha access to researchers and enterprises. Google DeepMind's Gemini-Exp-1206 leads benchmarks, tying with GPT-4o in coding performance. LlamaCloud enhanced document processing with table extraction and analytics. Discussions on OpenAI's pricing plans continue in the community.
Qwen with Questions: 32B open weights reasoning model nears o1 in GPQA/AIME/Math500
deepseek-r1 qwq gpt-4o claude-3.5-sonnet qwen-2.5 llama-cpp deepseek sambanova hugging-face dair-ai model-releases benchmarking fine-tuning sequential-search inference model-deployment agentic-rag external-tools multi-modal-models justin-lin clementdelangue ggerganov vikparuchuri
DeepSeek r1 leads the race for "open o1" models but has yet to release weights, while Justin Lin released QwQ, a 32B open weight model that outperforms GPT-4o and Claude 3.5 Sonnet on benchmarks. QwQ appears to be a fine-tuned version of Qwen 2.5, emphasizing sequential search and reflection for complex problem-solving. SambaNova promotes its RDUs as superior to GPUs for inference tasks, highlighting the shift from training to inference in AI systems. On Twitter, Hugging Face announced CPU deployment for llama.cpp instances, Marker v1 was released as a faster and more accurate deployment tool, and Agentic RAG developments focus on integrating external tools and advanced LLM chains for improved response accuracy. The open-source AI community sees growing momentum with models like Flux gaining popularity, reflecting a shift towards multi-modal AI models including image, video, audio, and biology.
Perplexity starts Shopping for you
pixtral-large-124b llama-3.1-405b claude-3.6 claude-3.5 stripe perplexity-ai mistral-ai hugging-face cerebras anthropic weights-biases google vllm-project multi-modal image-generation inference context-windows model-performance model-efficiency sdk ai-integration one-click-checkout memory-optimization patrick-collison jeff-weinstein mervenoyann sophiamyang tim-dettmers omarsar0 akhaliq aravsrinivas
Stripe launched their Agent SDK, enabling AI-native shopping experiences like Perplexity Shopping for US Pro members, featuring one-click checkout and free shipping via the Perplexity Merchant Program. Mistral AI released the Pixtral Large 124B multi-modal image model, now on Hugging Face and supported by Le Chat for image generation. Cerebras Systems offers a public inference endpoint for Llama 3.1 405B with a 128k context window and high throughput. Claude 3.6 shows improvements over Claude 3.5 but with subtle hallucinations. The Bi-Mamba 1-bit architecture improves LLM efficiency. The wandb SDK is preinstalled on Google Colab, and Pixtral Large is integrated into AnyChat and supported by vLLM for efficient model usage.
BitNet was a lie?
qwen-2.5-coder-32b-instruct gpt-4o llama-3 sambanova alibaba hugging-face quantization scaling-laws model-efficiency fine-tuning model-performance code-generation open-source unit-testing ci-cd tanishq-kumar tim-dettmers
Scaling laws for quantization have been modified by a group led by Chris Re, analyzing over 465 pretraining runs and finding benefits plateau at FP6 precision. Lead author Tanishq Kumar highlights that longer training and more data increase sensitivity to quantization, explaining challenges with models like Llama-3. Tim Dettmers, author of QLoRA, warns that the era of efficiency gains from low-precision quantization is ending, signaling a shift from scaling to optimizing existing resources. Additionally, Alibaba announced Qwen 2.5-Coder-32B-Instruct, which matches or surpasses GPT-4o on coding benchmarks, and open-source initiatives like DeepEval for LLM testing are gaining traction.
not much happened this weekend
claude-3.5-sonnet llama-3 llama-3-8b notebookllama min-omni-2 moondream openai anthropic hugging-face mistral-ai google-deepmind langchain deepmind microsoft pattern-recognition reinforcement-learning prompt-optimization text-to-speech model-optimization tensor-parallelism hyperparameters multimodal modal-alignment multimodal-fine-tuning ai-productivity privacy generative-ai rag retrieval-augmentation enterprise-text-to-sql amanda-askell philschmid stasbekman francois-fleuret mervenoyann reach_vb dzhng aravsrinivas sama lateinteraction andrew-y-ng bindureddy jerryjliu0
Moondream, a 1.6b vision language model, secured seed funding, highlighting a trend in moon-themed tiny models alongside Moonshine (27-61m ASR model). Claude 3.5 Sonnet was used for AI Twitter recaps. Discussions included pattern recognition vs. intelligence in LLMs, reinforcement learning for prompt optimization, and NotebookLlama, an open-source NotebookLM variant using LLaMA models for tasks like text-to-speech. Advances in model optimization with async-TP in PyTorch for tensor parallelism and hyperparameter tuning were noted. Mini-Omni 2 demonstrated multimodal capabilities across image, audio, and text for voice conversations with emphasis on modal alignment and multimodal fine-tuning. AI productivity tools like an AI email writer and LlamaCloud-based research assistants were introduced. Emphasis on practical skill development and privacy-conscious AI tool usage with Llama3-8B was highlighted. Generative AI tools such as #AIPythonforBeginners and GenAI Agents with LangGraph were shared. Business insights covered rapid execution in AI product development and emerging AI-related job roles. Challenges in enterprise-grade text-to-SQL and advanced retrieval methods were discussed with tutorials on RAG applications using LangChain and MongoDB.
DeepSeek Janus and Meta SpiRit-LM: Decoupled Image and Expressive Voice Omnimodality
nemotron-70b claude claude-3.5-sonnet gpt-4o deepseek meta-ai-fair wandb nvidia anthropic hugging-face perplexity-ai multimodality image-generation speech-synthesis fine-tuning model-merging benchmarking open-source model-optimization reinforcement-learning bindureddy aravsrinivas danielhanchen clementdelangue cwolferesearch
DeepSeek Janus and Meta SpiRit-LM are two notable multimodality AI models recently released, showcasing advances in image generation and speech synthesis respectively. DeepSeek Janus separates vision encoders for image understanding and generation, achieving better results in both tasks. Meta's SpiRit-LM introduces an expressive speech and writing model generating pitch and style units, improving over standard TTS. Additionally, W&B Weave offers comprehensive LLM observability and multimodality fine-tuning tools. Industry updates include Nvidia's Nemotron 70b model underperforming, Meta open-sourcing Movie Gen Bench for media generation benchmarking, Perplexity launching internal search with multi-step reasoning, and Anthropic updating Claude apps. Open source progress includes Hugging Face's gradient accumulation fix in transformers and advocacy for open source AI to prevent Big Tech dominance. "Model merging for combining skills of multiple models" is also highlighted.
Did Nvidia's Nemotron 70B train on test?
nemotron-70b llama-3.1-70b llama-3.1 ministral-3b ministral-8b gpt-4o claude-3.5-sonnet claude-3.5 nvidia mistral-ai hugging-face zep benchmarking reinforcement-learning reward-models temporal-knowledge-graphs memory-layers context-windows model-releases open-source reach_vb philschmid swyx
NVIDIA's Nemotron-70B model has drawn scrutiny despite strong benchmark performances on Arena Hard, AlpacaEval, and MT-Bench, with some standard benchmarks like GPQA and MMLU Pro showing no improvement over the base Llama-3.1-70B. The new HelpSteer2-Preference dataset improves some benchmarks with minimal losses elsewhere. Meanwhile, Mistral released Ministral 3B and 8B models featuring 128k context length and outperforming Llama-3.1 and GPT-4o on various benchmarks under the Mistral Commercial License. NVIDIA's Nemotron 70B also surpasses GPT-4o and Claude-3.5-Sonnet on key benchmarks using RLHF (REINFORCE) training. Additionally, Zep introduced Graphiti, an open-source temporal knowledge graph memory layer for AI agents, built on Neo4j.
not much happened today
flux-schnell meta-ai-fair anthropic togethercompute hugging-face audio-generation quantization prompt-caching long-term-memory llm-serving-framework hallucination-detection ai-safety ai-governance geoffrey-hinton john-hopfield demis-hassabis rohanpaul_ai svpino hwchase17 shreyar philschmid mmitchell_ai bindureddy
Geoffrey Hinton and John Hopfield won the Nobel Prize in Physics for foundational work on neural networks linking AI and physics. Meta AI introduced a 13B parameter audio generation model as part of Meta Movie Gen for video-synced audio. Anthropic launched the Message Batches API enabling asynchronous processing of up to 10,000 queries at half the cost. Together Compute released Flux Schnell, a free model for 3 months. New techniques like PrefixQuant quantization and Prompt Caching for low-latency inference were highlighted by rohanpaul_ai. LangGraph added long-term memory support for persistent document storage. Hex-LLM framework was introduced for TPU-based low-cost, high-throughput LLM serving from Hugging Face models. Discussions on AI safety emphasized gender equality in science, and concerns about premature AI regulation by media and Hollywood were raised.
not much happened today
llama-3-2 llama-3 molmo meta-ai-fair google-deepmind hugging-face on-device-ai multimodality chip-design retrieval-augmented-generation rag benchmarking reliability ai-regulation free-speech pytorch-optimization demis-hassabis clementdelangue svpino awnihannun osanseviero omarsar0 sarahookr ylecun
Meta released Llama 3.2, including lightweight 1B and 3B models for on-device AI with capabilities like summarization and retrieval-augmented generation. Molmo, a new multimodal model, was introduced with a large dense captioning dataset. Google DeepMind announced AlphaChip, an AI-driven chip design method improving TPU and CPU designs. Hugging Face surpassed 1 million free public models, highlighting the value of smaller specialized models. Discussions covered challenges in scaling RAG applications, the future of on-device AI running ChatGPT-level models, reliability issues in larger LLMs, and new Elo benchmarking accepted at NeurIPS 2024. AI ethics and regulation topics included free speech responsibilities and California's SB-1047 bill potentially affecting open-source AI. "AlphaChip transformed computer chip design," and "ChatGPT-level AI on mobile devices predicted within a year."
not much happened today
o1-preview o1-mini qwen-2.5 gpt-4o deepseek-v2.5 gpt-4-turbo-2024-04-09 grin llama-3-1-405b veo kat openai qwen deepseek-ai microsoft kyutai-labs perplexity-ai together-ai meta-ai-fair google-deepmind hugging-face google anthropic benchmarking math coding instruction-following model-merging model-expressiveness moe voice voice-models generative-video competition open-source model-deployment ai-agents hyung-won-chung noam-brown bindureddy akhaliq karpathy aravsrinivas fchollet cwolferesearch philschmid labenz ylecun
OpenAI's o1-preview and o1-mini models lead benchmarks in Math, Hard Prompts, and Coding. Qwen 2.5 72B model shows strong performance close to GPT-4o. DeepSeek-V2.5 tops Chinese LLMs, rivaling GPT-4-Turbo-2024-04-09. Microsoft's GRIN MoE achieves good results with 6.6B active parameters. Moshi voice model from Kyutai Labs runs locally on Apple Silicon Macs. Perplexity app introduces voice mode with push-to-talk. LlamaCoder by Together.ai uses Llama 3.1 405B for app generation. Google DeepMind's Veo is a new generative video model for YouTube Shorts. The 2024 ARC-AGI competition increases prize money and plans a university tour. A survey on model merging covers 50+ papers for LLM alignment. The Kolmogorov–Arnold Transformer (KAT) paper proposes replacing MLP layers with KAN layers for better expressiveness. Hugging Face Hub integrates with Google Cloud Vertex AI Model Garden for easier open-source model deployment. Agent.ai is introduced as a professional network for AI agents. "Touching grass is all you need."
Pixtral 12B: Mistral beats Llama to Multimodality
pixtral-12b mistral-nemo-12b llama-3-1-70b llama-3-1-8b deeps-eek-v2-5 gpt-4-turbo llama-3-1 strawberry claude mistral-ai meta-ai-fair hugging-face arcee-ai deepseek-ai openai anthropic vision multimodality ocr benchmarking model-release model-architecture model-performance fine-tuning model-deployment reasoning code-generation api access-control reach_vb devendra_chapilot _philschmid rohanpaul_ai
Mistral AI released Pixtral 12B, an open-weights vision-language model with a Mistral Nemo 12B text backbone and a 400M vision adapter, featuring a large vocabulary of 131,072 tokens and support for 1024x1024 pixel images. This release notably beat Meta AI in launching an open multimodal model. At the Mistral AI Summit, architecture details and benchmark performances were shared, showing strong OCR and screen understanding capabilities. Additionally, Arcee AI announced SuperNova, a distilled Llama 3.1 70B & 8B model outperforming Meta's Llama 3.1 70B instruct on benchmarks. DeepSeek released DeepSeek-V2.5, scoring 89 on HumanEval, surpassing GPT-4-Turbo, Opus, and Llama 3.1 in coding tasks. OpenAI plans to release Strawberry as part of ChatGPT soon, though its capabilities are debated. Anthropic introduced Workspaces for managing multiple Claude deployments with enhanced access controls.
not much happened today + AINews Podcast?
superforecaster-ai llama-3 reflection-70b glean sambanova cerebras stanford google apple hugging-face lmsys prompt-engineering research-ideas inference-speed retrieval-augmented-generation evaluation-methods visual-intelligence on-device-ai model-performance benchmarking novelty-detection danhendrycks benjamin-clavie bclavie bindureddy swyx borismpower corbtt drjimfan clementdelangue rohanpaul_ai
Glean doubled its valuation again. Dan Hendrycks' Superforecaster AI generates plausible election forecasts with interesting prompt engineering. A Stanford study found that LLM-generated research ideas are statistically more novel than those by expert humans. SambaNova announced faster inference for llama-3 models, surpassing Cerebras. Benjamin Clavie gave a notable talk on retrieval-augmented generation techniques. Strawberry is reported to launch in two weeks. Google Illuminate offers AI-generated podcast discussions about papers and books. Apple unveiled new AI features in iOS 18, including visual intelligence and improved Siri, with on-device and cloud processing for camera-based event additions. The Reflection 70B model sparked controversy over performance claims. Experts highlighted the unreliability of traditional benchmarks like MMLU and HumanEval, recommending alternative evaluation methods such as LMSys Chatbot Arena and Hugging Face's open-sourced Lighteval suite. The AI research community continues to explore AI's role in generating novel research ideas and improving benchmarking.
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.
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.
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.
Ideogram 2 + Berkeley Function Calling Leaderboard V2
llama-3-70b gpt-4 phi-3.5 functionary-llama-3-70b llama-3 ideogram midjourney berkeley openai hugging-face microsoft meta-ai-fair baseten kai claude functionary function-calling benchmarking image-generation model-optimization vision multimodality model-performance fine-tuning context-windows cybersecurity code-analysis ai-assisted-development
Ideogram returns with a new image generation model featuring color palette control, a fully controllable API, and an iOS app, reaching a milestone of 1 billion images created. Meanwhile, Midjourney released a Web UI but still lacks an API. In function calling, the Berkeley Function Calling Leaderboard (BFCL) updated to BFCL V2 • Live, adding 2251 live, user-contributed function documentation and queries to improve evaluation quality. GPT-4 leads the leaderboard, but the open-source Functionary Llama 3-70B finetune from Kai surpasses Claude. On AI model releases, Microsoft launched three Phi-3.5 models with impressive reasoning and context window capabilities, while Meta AI FAIR introduced UniBench, a unified benchmark suite for over 50 vision-language model tasks. Baseten improved Llama 3 inference speed by up to 122% using Medusa. A new cybersecurity benchmark, Cyberbench, featuring 40 CTF tasks, was released. Additionally, Codegen was introduced as a tool for programmatic codebase analysis and AI-assisted development. "Multiple functions > parallel functions" was highlighted as a key insight in function calling.
not much happened today
gpt-4o claude-3.5-sonnet phi-3.5-mini phi-3.5-moe phi-3.5-vision llama-3-1-405b qwen2-math-72b openai anthropic microsoft meta-ai-fair hugging-face langchain box fine-tuning benchmarking model-comparison model-performance diffusion-models reinforcement-learning zero-shot-learning math model-efficiency ai-regulation ai-safety ai-engineering prompt-engineering swyx ylecun
OpenAI launched GPT-4o finetuning with a case study on Cosine. Anthropic released Claude 3.5 Sonnet with 8k token output. Microsoft Phi team introduced Phi-3.5 in three variants: Mini (3.8B), MoE (16x3.8B), and Vision (4.2B), noted for sample efficiency. Meta released Llama 3.1 405B, deployable on Google Cloud Vertex AI, offering GPT-4 level capabilities. Qwen2-Math-72B achieved state-of-the-art math benchmark performance with a Gradio demo. Discussions included model comparisons like ViT vs CNN and Mamba architecture. Tools updates featured DSPy roadmap, Flux Schnell improving diffusion speed on M1 Max, and LangChain community events. Research highlights zero-shot DUP prompting for math reasoning and fine-tuning best practices. AI ethics covered California's AI Safety Bill SB 1047 and regulatory concerns from Yann LeCun. Commentary on AI engineer roles by Swyx. "Chat with PDF" feature now available for Box Enterprise Plus users.
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.
GPT4o August + 100% Structured Outputs for All (GPT4o mini edition)
gpt-4o-mini gpt-4o-2024-08-06 llama-3 bigllama-3.1-1t-instruct meta-llama-3-120b-instruct gemma-2-2b stability-ai unsloth-ai google hugging-face lora controlnet line-art gpu-performance multi-gpu-support fine-tuning prompt-formatting cloud-computing text-to-image-generation model-integration
Stability.ai users are leveraging LoRA and ControlNet for enhanced line art and artistic style transformations, while facing challenges with AMD GPUs due to the discontinuation of ZLUDA. Community tensions persist around the r/stablediffusion subreddit moderation. Unsloth AI users report fine-tuning difficulties with LLaMA3 models, especially with PPO trainer integration and prompt formatting, alongside anticipation for multi-GPU support and cost-effective cloud computing on RunPod. Google released the lightweight Gemma 2 2B model optimized for on-device use with 2.6B parameters, featuring safety and sparse autoencoder tools, and announced Diffusers integration for efficient text-to-image generation on limited resources.
not much happened today
sam-2 gemini-1.5-pro chatgpt midjourney-v6.1 meta-ai-fair google-deepmind scale-ai apple canva hugging-face object-segmentation quantization web-development-framework adversarial-robustness on-device-ai open-source robotics voice vision jeremyphoward demis-hassabis ylecun maartengrootendorst jimfan
Meta released SAM 2, a unified model for real-time object segmentation with a new dataset 4.5x larger and 53x more annotated than previous ones. FastHTML, a new Python web framework by Jeremy Howard, enables easy creation and deployment of interactive web apps. Scale AI launched the SEAL Leaderboard on adversarial robustness, topped by Gemini 1.5 Pro from Google DeepMind. Apple published a technical report on their Intelligence Foundation Language Models for on-device and server use. Yann LeCun emphasized the importance of open source AI in an article co-authored with Martin Casado and Ion Stoica. Maarten Grootendorst's "Visual Guide to Quantization" on efficient LLM inference went viral. ChatGPT started rolling out advanced voice and vision-enabled modes to select users. Leonardo AI was acquired by Canva. Jim Fan shared insights on Project Groot augmenting human demonstration data for robotics. Midjourney v6.1 was released.
DataComp-LM: the best open-data 7B model/benchmark/dataset
mistral-nemo-12b gpt-4o-mini deepseek-v2-0628 mistral-7b llama-3 gemma-2 qwen-2 datacomp hugging-face openai nvidia mistral-ai deepseek dataset-design scaling-laws model-benchmarking model-performance fine-tuning multilinguality function-calling context-windows open-source-models model-optimization cost-efficiency benchmarking sam-altman guillaume-lample philschmid miramurati
DataComp team released a competitive 7B open data language model trained on only 2.5T tokens from the massive DCLM-POOL dataset of 240 trillion tokens, showing superior scaling trends compared to FineWeb. OpenAI launched GPT-4o mini, a cost-effective model with 82% MMLU and performance near GPT-4-Turbo, aimed at developers for broad applications. NVIDIA and Mistral jointly released the Mistral NeMo 12B model featuring a 128k token context window, FP8 checkpoint, multilingual support, and Apache 2.0 licensing. DeepSeek announced DeepSeek-V2-0628 as the top open-source model on the LMSYS Chatbot Arena leaderboard with strong rankings in coding, math, and hard prompts. This news highlights advances in dataset design, model efficiency, and open-source contributions in the AI community.
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.
SciCode: HumanEval gets a STEM PhD upgrade
gpt-4 claude-3.5-sonnet llama-3-7b llama-3 dolphin-2.9.3-yi-1.5-34b-32k-gguf anthropic hugging-face nvidia benchmarks coding model-training gpu-optimization model-performance synthetic-data compiler-optimization zero-shot-learning yi-tay rohanpaul_ai alexalbert__ tri_dao abacaj
PhD-level benchmarks highlight the difficulty of coding scientific problems for LLMs, with GPT-4 and Claude 3.5 Sonnet scoring under 5% on the new SciCode benchmark. Anthropic doubled the max output token limit for Claude 3.5 Sonnet to 8192 tokens. The Q-GaLore method enables training LLaMA-7B on a single 16GB GPU. The Mosaic compiler now generates efficient code for NVIDIA H100 GPUs. The Dolphin 2.9.3-Yi-1.5-34B-32k-GGUF model on Hugging Face has over 111k downloads. Llama 3 shows strong performance, achieving 90% zero-shot accuracy on the MATH dataset. Discussions continue on the limitations and forms of synthetic data for model training.
Microsoft AgentInstruct + Orca 3
mistral-7b orca-2.5 microsoft-research apple tencent hugging-face synthetic-data fine-tuning instruction-following transformers model-performance hallucination-detection dataset-quality flashattention mixture-of-experts philschmid sama bindureddy rohanpaul_ai zachtratar dair_ai
Microsoft Research released AgentInstruct, the third paper in its Orca series, introducing a generative teaching pipeline that produces 25.8 million synthetic instructions to fine-tune mistral-7b, achieving significant performance gains: +40% AGIEval, +19% MMLU, +54% GSM8K, +38% BBH, +45% AlpacaEval, and a 31.34% reduction in hallucinations. This synthetic data approach follows the success of FineWeb and Apple's Rephrasing research in improving dataset quality. Additionally, Tencent claims to have generated 1 billion diverse personas for synthetic data. On AI Twitter, notable discussions included a shooting incident at a Trump rally and recent ML research highlights such as FlashAttention-3, RankRAG, and Mixture of A Million Experts.
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.
Qdrant's BM42: "Please don't trust us"
claude-3.5-sonnet gemma-2 nano-llava-1.5 qdrant cohere stripe anthropic hugging-face stablequan_ai semantic-search benchmarking dataset-quality model-evaluation model-optimization vision fine-tuning context-windows nils-reimers jeremyphoward hamelhusain rohanpaul_ai
Qdrant attempted to replace BM25 and SPLADE with a new method called "BM42" combining transformer attention and collection-wide statistics for semantic and keyword search, but their evaluation using the Quora dataset was flawed. Nils Reimers from Cohere reran BM42 on better datasets and found it underperformed. Qdrant acknowledged the errors but still ran a suboptimal BM25 implementation. This highlights the importance of dataset choice and evaluation sanity checks in search model claims. Additionally, Stripe faced criticism for AI/ML model failures causing account and payment issues, prompting calls for alternatives. Anthropic revealed that Claude 3.5 Sonnet suppresses some answer parts with backend tags, sparking debate. Gemma 2 model optimizations allow 2x faster fine-tuning with 63% less memory and longer context windows, running up to 34B parameters on consumer GPUs. nanoLLaVA-1.5 was announced as a compact 1B parameter vision model with significant improvements.
GraphRAG: The Marriage of Knowledge Graphs and RAG
gemma-2 llama-3-70b claude-3.5-sonnet nemotron-340b qwen2-72b llama-3 microsoft-research anthropic nvidia hugging-face retrieval-augmented-generation knowledge-graphs token-usage inference-time attention-mechanisms instruction-following coding math long-range-reasoning synthetic-data dataset-release fine-tuning context-windows function-calling travis-fischer rasbt alexandr-wang osanseviero rohanpaul_ai hamelhusain svpino aaaazzam omarsar0
Microsoft Research open sourced GraphRAG, a retrieval augmented generation (RAG) technique that extracts knowledge graphs from sources and clusters them for improved LLM answers, though it increases token usage and inference time. Gemma 2 models were released focusing on efficient small LLMs with innovations like sliding window attention and RMS norm, nearly matching the larger Llama 3 70B. Anthropic's Claude 3.5 Sonnet leads in instruction following and coding benchmarks, while Nvidia's Nemotron 340B model was released in June. Qwen2-72B tops the HuggingFace Open LLM leaderboard excelling in math and long-range reasoning. Discussions on RAG highlighted its limitations and improvements in context usage via function calls. A persona-driven synthetic data generation approach introduced 1 billion personas, with a fine-tuned model matching GPT-4 performance on math benchmarks at 7B scale. The 200GB AutoMathText dataset was also noted for math data synthesis.
Gemini launches context caching... or does it?
nemotron llama-3-70b chameleon-7b chameleon-34b gemini-1.5-pro deepseek-coder-v2 gpt-4-turbo claude-3-opus gemini-1.5-pro nvidia meta-ai-fair google deepseek hugging-face context-caching model-performance fine-tuning reinforcement-learning group-relative-policy-optimization large-context model-training coding model-release rohanpaul_ai _philschmid aman-sanger
Nvidia's Nemotron ranks #1 open model on LMsys and #11 overall, surpassing Llama-3-70b. Meta AI released Chameleon 7B/34B models after further post-training. Google's Gemini introduced context caching, offering a cost-efficient middle ground between RAG and finetuning, with a minimum input token count of 33k and no upper limit on cache duration. DeepSeek launched DeepSeek-Coder-V2, a 236B parameter model outperforming GPT-4 Turbo, Claude-3-Opus, and Gemini-1.5-Pro in coding tasks, supporting 338 programming languages and extending context length to 128K. It was trained on 6 trillion tokens using the Group Relative Policy Optimization (GRPO) algorithm and is available on Hugging Face with a commercial license. These developments highlight advances in model performance, context caching, and large-scale coding models.
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.
Mamba-2: State Space Duality
mamba-2 mamba transformer++ llama-3-70b gpt-3 hugging-face state-space-models perplexity training-efficiency data-pruning benchmarking multimodality video-analysis _albertgu tri_dao arankomatsuzaki _akhaliq clementdelangue karpathy
Mamba-2, a new state space model (SSM), outperforms previous models like Mamba and Transformer++ in perplexity and wall-clock time, featuring 8x larger states and 50% faster training. It introduces the concept of state space duality (SSD) connecting SSMs and linear attention. The FineWeb-Edu dataset, a high-quality subset of the 15 trillion token FineWeb dataset, filtered using llama-3-70b for educational quality, enables better and faster LLM learning, potentially reducing tokens needed to surpass GPT-3 performance. Additionally, perplexity-based data pruning using a 125M parameter model improves downstream performance and reduces pretraining steps by up to 1.45x. The Video-MME benchmark evaluates multi-modal LLMs on video analysis across multiple visual domains and video lengths.
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.
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.
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.
GPT-4o: the new SOTA-EVERYTHING Frontier model (GPT4T version)
gpt-4o gpt-3.5 llama-3 openai hugging-face nous-research eleutherai hazyresearch real-time-reasoning coding-capabilities fine-tuning knowledge-distillation hardware-optimization quantization multimodality mixture-of-experts efficient-attention model-scaling depth-upscaling transformer-architecture gpu-optimization prompt-engineering
OpenAI launched GPT-4o, a frontier model supporting real-time reasoning across audio, vision, and text, now free for all ChatGPT users with enhanced coding capabilities and upcoming advanced voice and video features. Discussions cover open-source LLMs like Llama 3, fine-tuning techniques including knowledge distillation for GPT-3.5, and hardware optimization strategies such as quantization. Emerging architectures include multimodal integrations with ChatGPT voice and Open Interpreter API, Mixture of Experts models combining autoregressive and diffusion approaches, and novel designs like the YOCO architecture and ThunderKittens DSL for efficient GPU use. Research advances in efficient attention methods like Conv-Basis using FFT and model scaling techniques such as depth upscaling were also highlighted.
Perplexity, the newest AI unicorn
llama-3-8b llama-3-70b llama-3 llava-llama-3-8b-v1_1 phi-3 gpt-3.5 perplexity-ai meta-ai-fair hugging-face groq context-length fine-tuning quantization instruction-following model-comparison multimodality benchmarking memory-optimization model-performance daniel-gross aravind-srinivas
Perplexity doubles its valuation shortly after its Series B with a Series B-1 funding round. Significant developments around Llama 3 include context length extension to 16K tokens, new multimodal LLaVA models outperforming Llama 2, and fine-tuning improvements like QDoRA surpassing QLoRA. The Llama-3-70B model is praised for instruction following and performance across quantization formats. Phi-3 models by Meta AI released in multiple sizes show competitive benchmark results, with the 14B model achieving 78% on MMLU and the 3.8B model nearing GPT-3.5 performance.
Meta Llama 3 (8B, 70B)
llama-3-8b llama-3-70b llama-3-400b stable-diffusion-3 mixtral-8x22b-instruct-v0.1 vasa-1 meta-ai-fair stability-ai boston-dynamics microsoft mistral-ai hugging-face transformer tokenization model-training benchmarking robotics natural-language-processing real-time-processing synthetic-data dataset-cleaning behavior-trees ai-safety model-accuracy api model-release humor helen-toner
Meta partially released Llama 3 models including 8B and 70B variants, with a 400B variant still in training, touted as the first GPT-4 level open-source model. Stability AI launched Stable Diffusion 3 API with model weights coming soon, showing competitive realism against Midjourney V6. Boston Dynamics unveiled an electric humanoid robot Atlas, and Microsoft introduced the VASA-1 model generating lifelike talking faces at 40fps on RTX 4090. Mistral AI, a European OpenAI rival, is seeking $5B funding with its Mixtral-8x22B-Instruct-v0.1 model achieving 100% accuracy on 64K context benchmarks. AI safety discussions include calls from former OpenAI board member Helen Toner for audits of top AI companies, and the Mormon Church released AI usage principles. New AI development tools include Ctrl-Adapter for diffusion models, Distilabel 1.0.0 for synthetic dataset pipelines, Data Bonsai for data cleaning with LLMs, and Dendron for building LLM agents with behavior trees. Memes highlight AI development humor and cultural references. The release of Llama 3 models features improved reasoning, a 128K token vocabulary, 8K token sequences, and grouped query attention.
Mixtral 8x22B Instruct sparks efficiency memes
mixtral-8x22b llama-2-7b olmo-7b mistral-ai hugging-face google microsoft intel softbank nvidia multilinguality math code-generation context-window model-performance model-release retrieval-augmented-generation deepfake ai-investment ai-chip hybrid-architecture training-data guillaume-lample osanseviero _philschmid svpino
Mistral released an instruct-tuned version of their Mixtral 8x22B model, notable for using only 39B active parameters during inference, outperforming larger models and supporting 5 languages with 64k context window and math/code capabilities. The model is available on Hugging Face under an Apache 2.0 license for local use. Google plans to invest over $100 billion in AI, with other giants like Microsoft, Intel, and SoftBank also making large investments. The UK criminalized non-consensual deepfake porn, raising enforcement debates. A former Nvidia employee claims Nvidia's AI chip lead is unmatchable this decade. AI companions could become a $1 billion market. AI has surpassed humans on several basic tasks but lags on complex ones. Zyphra introduced Zamba, a novel 7B parameter hybrid model outperforming LLaMA-2 7B and OLMo-7B with less training data, trained on 128 H100 GPUs over 30 days. GroundX API advances retrieval-augmented generation accuracy.
Zero to GPT in 1 Year
gpt-4-turbo claude-3-opus mixtral-8x22b zephyr-141b medical-mt5 openai anthropic mistral-ai langchain hugging-face fine-tuning multilinguality tool-integration transformers model-evaluation open-source-models multimodal-llms natural-language-processing ocr model-training vik-paruchuri sam-altman greg-brockman miranda-murati abacaj mbusigin akhaliq clementdelangue
GPT-4 Turbo reclaimed the top leaderboard spot with significant improvements in coding, multilingual, and English-only tasks, now rolled out in paid ChatGPT. Despite this, Claude Opus remains superior in creativity and intelligence. Mistral AI released powerful open-source models like Mixtral-8x22B and Zephyr 141B suited for fine-tuning. LangChain enhanced tool integration across models, and Hugging Face introduced Transformer.js for running transformers in browsers. Medical domain-focused Medical mT5 was shared as an open-source multilingual text-to-text model. The community also highlighted research on LLMs as regressors and shared practical advice on OCR/PDF data modeling from Vik Paruchuri's journey.
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.
Gemini Pro and GPT4T Vision go GA on the same day by complete coincidence
gemini-1.5-pro gpt-4-turbo llama-3 orca-2.5-7b functionary-v2.4 cosxl google openai meta-ai-fair hugging-face cohere million-token-context-window audio-processing file-api text-embedding function-calling reasoning direct-nash-optimization contrastive-learning code-interpreter diffusion-models neural-odes inference-speed multilingual-dataset image-editing no-code-development
At Google Cloud Next, Gemini 1.5 Pro was released with a million-token context window, available in 180+ countries, featuring 9.5 hours of audio understanding, a new File API for nearly unlimited free uploads, and the Gecko-1b-256/768 embedding model. GPT-4 Turbo with Vision became generally available in the API with a major update improving reasoning capabilities. Meta Platforms plans to launch smaller versions of Llama 3 next week. The Orca 2.5 7B model using Direct Nash Optimization outperforms older GPT-4 versions in AlpacaEval. New releases include Functionary-V2.4 with enhanced function calling and code interpretation, and CosXL models for image editing. Research highlights include continuous U-Nets for diffusion models achieving up to 80% faster inference and a massive multilingual dataset with ~5.6 trillion word tokens. Creative applications include a no-code touch screen game made with Gemini 1.5 and AI-generated novel trailers.
ReALM: Reference Resolution As Language Modeling
flan-t5 gpt-4 apple openai hugging-face stability-ai reference-resolution finetuning quantization retrieval-augmented-generation open-source coding-agents podcast-generation image-generation ai-industry-trends takuto-takizawa
Apple is advancing in AI with a new approach called ReALM: Reference Resolution As Language Modeling, which improves understanding of ambiguous references using three contexts and finetunes a smaller FLAN-T5 model that outperforms GPT-4 on this task. In Reddit AI news, an open-source coding agent SWE-agent achieves 12.29% on the SWE-bench benchmark, and RAGFlow introduces a customizable retrieval-augmented generation engine. A new quantization method, QuaRot, enables efficient 4-bit inference. AI applications include a t-shirt design generator, podgenai for GPT-4 based podcast generation, and an open-source model from HuggingFace that runs without a GPU. Industry discussions focus on the impact of large language models on the AI field and efforts to decentralize AI development. Takuto Takizawa joins Stability AI Japan as Head of Sales & Partnerships.
Not much happened today
jamba-v0.1 command-r gpt-3.5-turbo openchat-3.5-0106 mixtral-8x7b mistral-7b midnight-miqu-70b-v1.0.q5_k_s cohere lightblue openai mistral-ai nvidia amd hugging-face ollama rag mixture-of-experts model-architecture model-analysis debate-persuasion hardware-performance gpu-inference cpu-comparison local-llm stable-diffusion ai-art-bias
RAGFlow open sourced, a deep document understanding RAG engine with 16.3k context length and natural language instruction support. Jamba v0.1, a 52B parameter MoE model by Lightblue, released but with mixed user feedback. Command-R from Cohere available on Ollama library. Analysis of GPT-3.5-Turbo architecture reveals about 7 billion parameters and embedding size of 4096, comparable to OpenChat-3.5-0106 and Mixtral-8x7B. AI chatbots, including GPT-4, outperform humans in debates on persuasion. Mistral-7B made amusing mistakes on a math riddle. Hardware highlights include a discounted HGX H100 640GB machine with 8 H100 GPUs bought for $58k, and CPU comparisons between Epyc 9374F and Threadripper 1950X for LLM inference. GPU recommendations for local LLMs focus on VRAM and inference speed, with users testing 4090 GPU and Midnight-miqu-70b-v1.0.q5_k_s model. Stable Diffusion influences gaming habits and AI art evaluation shows bias favoring human-labeled art.
AdamW -> AaronD?
claude-3-opus llama-3 llama-3-300m bert-large stable-diffusion-1.5 wdxl openai hugging-face optimizer machine-learning-benchmarks vision time-series-forecasting image-generation prompt-injection policy-enforcement aaron-defazio
Aaron Defazio is gaining attention for proposing a potential tuning-free replacement of the long-standing Adam optimizer, showing promising experimental results across classic machine learning benchmarks like ImageNet ResNet-50 and CIFAR-10/100. On Reddit, Claude 3 Opus has surpassed all OpenAI models on the LMSys leaderboard, while a user pretrained a LLaMA-based 300M model outperforming bert-large on language modeling tasks with a modest budget. The new MambaMixer architecture demonstrates promising results in vision and time series forecasting. In image generation, Stable Diffusion 1.5 with LoRAs achieves realistic outputs, and the WDXL release showcases impressive capabilities. AI applications include an AI-generated Nike spec ad and a chatbot built with OpenAI models that may resist prompt injections. OpenAI is reportedly planning a ban wave targeting policy violators and jailbreak users. "The high alpha seems to come from Aaron Defazio," highlighting his impactful work in optimizer research.
Jamba: Mixture of Architectures dethrones Mixtral
jamba dbrx mixtral animatediff fastsd sdxs512-0.9 b-lora supir ai21-labs databricks together-ai hugging-face midjourney mixture-of-experts model-architecture context-windows model-optimization fine-tuning image-generation video-generation cpu-optimization style-content-separation high-resolution-upscaling
AI21 labs released Jamba, a 52B parameter MoE model with 256K context length and open weights under Apache 2.0 license, optimized for single A100 GPU performance. It features a unique blocks-and-layers architecture combining transformer and MoE layers, competing with models like Mixtral. Meanwhile, Databricks introduced DBRX, a 36B active parameter MoE model trained on 12T tokens, noted as a new standard for open LLMs. In image generation, advancements include Animatediff for video-quality image generation and FastSD CPU v1.0.0 beta 28 enabling ultra-fast image generation on CPUs. Other innovations involve style-content separation using B-LoRA and improvements in high-resolution image upscaling with SUPIR.
DBRX: Best open model (just not most efficient)
dbrx grok mixtral llama-2 mpt-7b gpt-4 databricks hugging-face mistral-ai mosaicml openai mixture-of-experts model-efficiency tokenization model-training code-generation model-architecture open-source-models benchmarking fine-tuning
Databricks Mosaic has released a new open-source model called DBRX that outperforms Grok, Mixtral, and Llama2 on evaluations while being about 2x more efficient than Llama2 and Grok. The model was trained on 12 trillion tokens using 3,000 H100 GPUs over 2 months, with an estimated compute cost of $10 million. It uses OpenAI's 100k tiktoken tokenizer and shows strong zero-shot code generation performance, even beating GPT-4 on the Humaneval benchmark. DBRX also upstreamed work to MegaBlocks open source. Despite its scale and efficiency, DBRX's performance on MMLU is only slightly better than Mixtral, raising questions about its scaling efficiency. The focus of DBRX is on enabling users to train models efficiently, with MoE training being about 2x more FLOP-efficient than dense models, achieving similar quality with nearly 4x less compute than previous MPT models. This release is part of the ongoing competition for open-source AI leadership, including models like Dolly, MPT, and Mistral. "If it activates 36B params, the model's perf should be equivalent to a 72B dense model or even 80B," says Qwen's tech lead.
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.
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.
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.
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.
The Era of 1-bit LLMs
bitnet-b1.58 hugging-face quantization model-optimization energy-efficiency fine-tuning robotics multimodality ai-security ethics humor swyx levelsio gdb npew _akhaliq osanseviero mmitchell_ai deliprao nearcyan clementdelangue
The Era of 1-bit LLMs research, including the BitNet b1.58 model, introduces a ternary parameter approach that matches full-precision Transformer LLMs in performance while drastically reducing energy costs by 38x. This innovation promises new scaling laws and hardware designs optimized for 1-bit LLMs. Discussions on AI Twitter highlight advances in AGI societal impact, robotics with multimodal models, fine-tuning techniques like ResLoRA, and AI security efforts at Hugging Face. Ethical considerations in generative AI and humor within the AI community are also prominent topics.
Dia de las Secuelas (StarCoder, The Stack, Dune, SemiAnalysis)
starcoder-2 starcoder2-15b hugging-face bigcode code-generation model-training dataset-release model-performance dylan-patel
HuggingFace/BigCode has released StarCoder v2, including the StarCoder2-15B model trained on over 600 programming languages using the The Stack v2 dataset. This release marks a state-of-the-art achievement for models of this size, with opt-out requests excluded from training data. A detailed technical report is available, highlighting the model's capabilities and training methodology. Additionally, a live event featuring Dylan Patel discussing GPU economics is announced for San Francisco.
Mistral Large disappoints
mistral-large mistral-small mixtral-8x7b gpt-4-turbo dreamgen-opus-v1 mistral-ai openai hugging-face benchmarking model-merging fine-tuning reinforcement-learning model-training tokenization model-optimization ai-assisted-decompilation performance cost-efficiency deception roleplay deep-speed dpo timotheeee1 cogbuji plasmator jsarnecki maldevide spottyluck mrjackspade
Mistral announced Mistral Large, a new language model achieving 81.2% accuracy on MMLU, trailing GPT-4 Turbo by about 5 percentage points on benchmarks. The community reception has been mixed, with skepticism about open sourcing and claims that Mistral Small outperforms the open Mixtral 8x7B. Discussions in the TheBloke Discord highlighted performance and cost-efficiency comparisons between Mistral Large and GPT-4 Turbo, technical challenges with DeepSpeed and DPOTrainer for training, advances in AI deception for roleplay characters using DreamGen Opus V1, and complexities in model merging using linear interpolation and PEFT methods. Enthusiasm for AI-assisted decompilation was also expressed, emphasizing the use of open-source projects for training data.
One Year of Latent Space
gemini-1.5 gemma-7b mistral-next opus-v1 orca-2-13b nous-hermes-2-dpo-7b google-deepmind nous-research mistral-ai hugging-face nvidia langchain jetbrains ai-ethics bias-mitigation fine-tuning performance-optimization model-merging knowledge-transfer text-to-3d ai-hallucination hardware-optimization application-development vulnerability-research jim-keller richard-socher
Latent Space podcast celebrated its first anniversary, reaching #1 in AI Engineering podcasts and 1 million unique readers on Substack. The Gemini 1.5 image generator by Google DeepMind sparked controversy over bias and inaccurate representation, leading to community debates on AI ethics. Discussions in TheBloke and LM Studio Discords highlighted AI's growing role in creative industries, especially game development and text-to-3D tools. Fine-tuning and performance optimization of models like Gemma 7B and Mistral-next were explored in Nous Research AI and Mistral Discords, with shared solutions including learning rates and open-source tools. Emerging trends in AI hardware and application development were discussed in CUDA MODE and LangChain AI Discords, including critiques of Nvidia's CUDA by Jim Keller and advancements in reducing AI hallucinations hinted by Richard Socher.
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.
The Dissection of Smaug (72B)
smaug-72b qwen-1.0 qwen-1.5 gpt-4 mistral-7b miqumaid wizardlm_evol_instruct_v2_196k openhermes-2.5 abacus-ai hugging-face nous-research laion thebloke lm-studio intel nvidia elevenlabs fine-tuning model-merging quantization web-ui model-conversion hardware-setup privacy image-generation optical-character-recognition prompt-engineering bindureddy
Abacus AI launched Smaug 72B, a large finetune of Qwen 1.0, which remains unchallenged on the Hugging Face Open LLM Leaderboard despite skepticism from Nous Research. LAION introduced a local voice assistant model named Bud-E with a notable demo. The TheBloke Discord community discussed model performance trade-offs between large models like GPT-4 and smaller quantized models, fine-tuning techniques using datasets like WizardLM_evol_instruct_V2_196k and OpenHermes-2.5, and challenges in web UI development and model merging involving Mistral-7b and MiquMaid. The LM Studio Discord highlighted issues with model conversion from PyTorch to gguf, hardware setups involving Intel Xeon CPUs and Nvidia P40 GPUs, privacy concerns, and limitations in image generation and web UI availability.
Gemini Ultra is out, to mixed reviews
gemini-ultra gemini-advanced solar-10.7b openhermes-2.5-mistral-7b subformer billm google openai mistral-ai hugging-face multi-gpu-support training-data-contamination model-merging model-alignment listwise-preference-optimization high-performance-computing parameter-sharing post-training-quantization dataset-viewer gpu-scheduling fine-tuning vram-optimization
Google released Gemini Ultra as a paid tier for "Gemini Advanced with Ultra 1.0" following the discontinuation of Bard. Reviews noted it is "slightly faster/better than ChatGPT" but with reasoning gaps. The Steam Deck was highlighted as a surprising AI workstation capable of running models like Solar 10.7B. Discussions in AI communities covered topics such as multi-GPU support for OSS Unsloth, training data contamination from OpenAI outputs, ethical concerns over model merging, and new alignment techniques like Listwise Preference Optimization (LiPO). The Mojo programming language was praised for high-performance computing. In research, the Subformer model uses sandwich-style parameter sharing and SAFE for efficiency, and BiLLM introduced 1-bit post-training quantization to reduce resource use. The OpenHermes dataset viewer tool was launched, and GPU scheduling with Slurm was discussed. Fine-tuning challenges for models like OpenHermes-2.5-Mistral-7B and VRAM requirements were also topics of interest.
Qwen 1.5 Released
qwen-1.5 mistral-7b sparsetral-16x7b-v2 bagel-7b-v0.4 deepseek-math-7b-instruct deepseek qwen mistral-ai hugging-face meta-ai-fair quantization token-context multilinguality retrieval-augmented-generation agent-planning code-generation sparse-moe model-merging fine-tuning direct-preference-optimization character-generation ascii-art kanji-generation vr retinal-resolution light-field-passthrough frozen-networks normalization-layers
Chinese AI models Yi, Deepseek, and Qwen are gaining attention for strong performance, with Qwen 1.5 offering up to 32k token context and compatibility with Hugging Face transformers and quantized models. The TheBloke Discord discussed topics like quantization of a 70B LLM, the introduction of the Sparse MoE model Sparsetral based on Mistral, debates on merging vs fine-tuning, and Direct Preference Optimization (DPO) for character generation. The Nous Research AI Discord covered challenges in Japanese Kanji generation, AI scams on social media, and Meta's VR headset prototypes showcased at SIGGRAPH 2023. Discussions also included fine-tuning frozen networks and new models like bagel-7b-v0.4, DeepSeek-Math-7b-instruct, and Sparsetral-16x7B-v2.
Less Lazy AI
hamster-v0.2 flan-t5 miqu-1-120b-gguf qwen2 axolotl openai hugging-face nous-research h2oai apple model-merging fine-tuning quantization vram-optimization plugin-development chatbot-memory model-training bug-reporting api-compatibility philschmid
The AI Discord summaries for early 2024 cover various community discussions and developments. Highlights include 20 guilds, 308 channels, and 10449 messages analyzed, saving an estimated 780 minutes of reading time. Key topics include Polymind Plugin Puzzle integrating PubMed API, roleplay with HamSter v0.2, VRAM challenges in Axolotl training, fine-tuning tips for FLAN-T5, and innovative model merging strategies. The Nous Research AI community discussed GPT-4's lyricism issues, quantization techniques using
llama.cpp, frankenmerging with models like miqu-1-120b-GGUF, anticipation for Qwen2, and tools like text-generation-webui and ExLlamaV2. The LM Studio community reported a bug where the app continues running after UI closure, with a workaround to forcibly terminate the process. These discussions reflect ongoing challenges and innovations in AI model training, deployment, and interaction. The Core Skills of AI Engineering
miqumaid olmo aphrodite awq exl2 mistral-medium internlm ssd-1b lora qlora loftq ai2 hugging-face ai-engineering quantization fine-tuning open-source model-deployment data-quality tokenization prompt-adherence distillation ai-security batching hardware role-playing eugene-yan
AI Discords for 2/2/2024 analyzed 21 guilds, 312 channels, and 4782 messages saving an estimated 382 minutes of reading time. Discussions included Eugene Yan initiating a deep dive into AI engineering challenges, highlighting overlaps between software engineering and data science skills. The TheBloke Discord featured talks on MiquMaid, OLMo (an open-source 65B LLM by AI2 under Apache 2.0), Aphrodite model batching, AWQ quantization, and LoRA fine-tuning techniques like QLoRA and LoftQ. The LAION Discord discussed SSD-1B distillation issues, data quality optimization with captioning datasets like BLIP, COCO, and LLaVA, and tokenization strategies for prompt adherence in image generation. Other topics included AI security with watermarking, superconductors and carbon nanotubes for hardware, and deployment of LLMs via Hugging Face tools.
Trust in GPTs at all time low
llama-3 mistral-medium llava-1.6 miquella-120b-gguf tinymodels miqumaid harmony-4x7b-bf16 smaug-34b-v0.1 openai hugging-face mistral-ai nous-research bittensor context-management fine-tuning model-merging quantization gpu-servers visual-reasoning ocr dataset-release incentive-structures nick-dobos manojbh teknium arthurmensch
Discord communities were analyzed with 21 guilds, 312 channels, and 8530 messages reviewed, saving an estimated 628 minutes of reading time. Discussions highlighted challenges with GPTs and the GPT store, including critiques of the knowledge files capability and context management issues. The CUDA MODE Discord was introduced for CUDA coding support. Key conversations in the TheBloke Discord covered Xeon GPU server cost-effectiveness, Llama3 and Mistral Medium model comparisons, LLaVA-1.6's visual reasoning and OCR capabilities, and the leaked Miqu 70B model. Technical topics included fine-tuning TinyLlama and MiquMaid+Euryale models, and model merging with examples like Harmony-4x7B-bf16 and Smaug-34B-v0.1. The Nous Research AI Discord discussed style influence in LLMs, quantization issues, Bittensor incentives for AI model improvements, and the identification of MIQU as Mistral Medium. The release of the Open Hermes 2.5 dataset on Hugging Face was also announced. "Discussions pointed towards the need for better context management in GPTs, contrasting with OpenAI's no-code approach."
Miqu confirmed to be an early Mistral-medium checkpoint
miqu-1-70b mistral-medium llama-2-70b-chat mixtral sqlcoder-70b codellama-70b bagelmistery-tour-v2 psyfighter-v2 mistral-ai hugging-face nous-research aiatmeta instruction-following sampling-methods fp16-quantization fine-tuning model-training context-length text-to-sql model-performance model-optimization intrstllrninja
Miqu, an open access model, scores 74 on MMLU and 84.5 on EQ-Bench, sparking debates about its performance compared to Mistral Medium. The CEO of Mistral confirmed these results. Discussions in the TheBloke Discord highlight Miqu's superiority in instruction-following and sampling methods like dynatemp and min-p. Developers also explore browser preferences and Discord UI themes. Role-playing with models like BagelMistery Tour v2 and Psyfighter v2 is popular, alongside technical talks on fp16 quantization of Miqu-1-70b. Training and fine-tuning tips for models like Unsloth and Mistral 7B are shared. In the Nous Research AI Discord, the Activation Beacon method is discussed for extending LLM context length from 4K to 400K tokens. SQLCoder-70B, fine-tuned on CodeLlama-70B, leads in text-to-SQL generation and is available on Hugging Face. The Miqu model also impresses with an 83.5 EQ-Bench score, fueling speculation about its capabilities.
CodeLLama 70B beats GPT4 on HumanEval
codellama miqu mistral-medium llama-2-70b aphrodite-engine mixtral flatdolphinmaid noromaid rpcal chatml mistral-7b activation-beacon eagle-7b rwkv-v5 openhermes2.5 nous-hermes-2-mixtral-8x7b-dpo imp-v1-3b bakllava moondream qwen-vl meta-ai-fair ollama nous-research mistral-ai hugging-face ai-ethics alignment gpu-optimization direct-prompt-optimization fine-tuning cuda-programming optimizer-technology quantization multimodality context-length dense-retrieval retrieval-augmented-generation multilinguality model-performance open-source code-generation classification vision
Meta AI surprised the community with the release of CodeLlama, an open-source model now available on platforms like Ollama and MLX for local use. The Miqu model sparked debate over its origins, possibly linked to Mistral Medium or a fine-tuned Llama-2-70b, alongside discussions on AI ethics and alignment risks. The Aphrodite engine showed strong performance on A6000 GPUs with specific configurations. Role-playing AI models such as Mixtral and Flatdolphinmaid faced challenges with repetitiveness, while Noromaid and Rpcal performed better, with ChatML and DPO recommended for improved responses. Learning resources like fast.ai's course were highlighted for ML/DL beginners, and fine-tuning techniques with optimizers like Paged 8bit lion and adafactor were discussed.
At Nous Research AI, the Activation Beacon project introduced a method for unlimited context length in LLMs using "global state" tokens, potentially transforming retrieval-augmented models. The Eagle-7B model, based on RWKV-v5, outperformed Mistral in benchmarks with efficiency and multilingual capabilities. OpenHermes2.5 was recommended for consumer hardware due to its quantization methods. Multimodal and domain-specific models like IMP v1-3b, Bakllava, Moondream, and Qwen-vl were explored for classification and vision-language tasks. The community emphasized centralizing AI resources for collaborative research.
RWKV "Eagle" v5: Your move, Mamba
rwkv-v5 mistral-7b miqu-1-70b mistral-medium llama-2 mistral-instruct-v0.2 mistral-tuna llama-2-13b kunoichi-dpo-v2-7b gpt-4 eleutherai mistral-ai hugging-face llamaindex nous-research rwkv lmsys fine-tuning multilinguality rotary-position-embedding model-optimization model-performance quantization speed-optimization prompt-engineering model-benchmarking reinforcement-learning andrej-karpathy
RWKV v5 Eagle was released with better-than-mistral-7b evaluation results, trading some English performance for multilingual capabilities. The mysterious miqu-1-70b model sparked debate about its origins, possibly a leak or distillation of Mistral Medium or a fine-tuned Llama 2. Discussions highlighted fine-tuning techniques, including the effectiveness of 1,000 high-quality prompts over larger mixed-quality datasets, and tools like Deepspeed, Axolotl, and QLoRA. The Nous Research AI community emphasized the impact of Rotary Position Embedding (RoPE) theta settings on LLM extrapolation, improving models like Mistral Instruct v0.2. Speed improvements in Mistral Tuna kernels reduced token processing costs, enhancing efficiency. The launch of Eagle 7B with 7.52B parameters showcased strong multilingual performance, surpassing other 7B class models.
GPT4Turbo A/B Test: gpt-4-0125-preview
gpt-4-turbo gpt-4-1106-preview gpt-3.5 llama-2-7b-chat tiny-llama mistral openai thebloke nous-research hugging-face multi-gpu-support model-optimization model-merging fine-tuning context-windows chatbot-personas api-performance text-transcription cost-considerations model-troubleshooting
OpenAI released a new GPT-4 Turbo version in January 2024, prompting natural experiments in summarization and discussions on API performance and cost trade-offs. The TheBloke Discord highlighted UnSloth's upcoming limited multi-GPU support for Google Colab beginners, AI models like Tiny Llama and Mistral running on Nintendo Switch, and advanced model merging techniques such as DARE and SLERP. The OpenAI Discord noted issues with GPT-4-1106-preview processing delays, troubleshooting GPT model errors, and transcription challenges with GPT-3.5 and GPT-4 Turbo. Nous Research AI focused on extending context windows, notably LLaMA-2-7B-Chat reaching 16,384 tokens, and fine-tuning alternatives like SelfExtend. Discussions also touched on chatbot persona creation, model configuration optimizations, and societal impacts of AI technology.
Adept Fuyu-Heavy: Multimodal model for Agents
fuyu-heavy fuyu-8b gemini-pro claude-2 gpt4v gemini-ultra deepseek-coder-33b yi-34b-200k goliath-120b mistral-7b-instruct-v0.2 mamba rwkv adept hugging-face deepseek mistral-ai nous-research multimodality visual-question-answering direct-preference-optimization benchmarking model-size-estimation quantization model-merging fine-tuning instruct-tuning rms-optimization heterogeneous-ai-architectures recurrent-llms contrastive-preference-optimization
Adept launched Fuyu-Heavy, a multimodal model focused on UI understanding and visual QA, outperforming Gemini Pro on the MMMU benchmark. The model uses DPO (Direct Preference Optimization), gaining attention as a leading tuning method. The size of Fuyu-Heavy is undisclosed but estimated between 20B-170B parameters, smaller than rumored frontier models like Claude 2, GPT4V, and Gemini Ultra. Meanwhile, Mamba was rejected at ICLR for quality concerns. In Discord discussions, DeepSeek Coder 33B was claimed to outperform GPT-4 in coding tasks, and deployment strategies for large models like Yi-34B-200K and Goliath-120B were explored. Quantization debates highlighted mixed views on Q8 and EXL2 quants. Fine-tuning and instruct-tuning of Mistral 7B Instruct v0.2 were discussed, alongside insights on RMS optimization and heterogeneous AI architectures combining Transformers and Selective SSM (Mamba). The potential of recurrent LLMs like RWKV and techniques like Contrastive Preference Optimization (CPO) were also noted.
RIP Latent Diffusion, Hello Hourglass Diffusion
gpt-4 latent-diffusion stable-diffusion meta-ai-fair openai hugging-face diffusion-models transformers image-generation model-efficiency fine-tuning quantization prompt-engineering roleplay training-optimization katherine-crowson lucidrains
Katherine Crowson from Stable Diffusion introduces a hierarchical pure transformer backbone for diffusion-based image generation that efficiently scales to megapixel resolutions with under 600 million parameters, improving upon the original ~900M parameter model. This architecture processes local and global image phenomena separately, enhancing efficiency and resolution without latent steps. Additionally, Meta's Self Rewarding LM paper has inspired lucidrains to begin an implementation. Discord summaries highlight GPT-4's robustness against quantification tricks, discussions on open-source GPT-0 alternatives, challenges in DPO training on limited VRAM with suggestions like QLoRA and rmsprop, and efforts to improve roleplay model consistency through fine-tuning and merging. Philosophical debates on AI sentience and GPT-4 customization for markdown and translation tasks were also noted.
Nightshade poisons AI art... kinda?
mistral-7b falcon-7b mistral-ai hugging-face mixture-of-experts gpu-parallelism quantization fine-tuning model-merging ai-detection role-playing benchmarking
Over the weekend of 1/19-20/2024, discussions in TheBloke Discord covered key topics including Mixture of Experts (MoE) model efficiency, GPU parallelism, and quantization strategies. Users debated the effectiveness of AI detection tools like GPTZero and explored fine-tuning challenges with models such as Mistral 7B and Falcon 7B. Community interest was strong in developing simpler, community-powered quantization services and understanding model merging techniques. Ethical considerations around AI applications like AI girlfriend sites were also discussed.
Sama says: GPT-5 soon
gpt-5 mixtral-7b gpt-3.5 gemini-pro gpt-4 llama-cpp openai codium thebloke amd hugging-face mixture-of-experts fine-tuning model-merging 8-bit-optimization gpu-acceleration performance-comparison command-line-ai vector-stores embeddings coding-capabilities sam-altman ilya-sutskever itamar andrej-karpathy
Sam Altman at Davos highlighted that his top priority is launching the new model, likely called GPT-5, while expressing uncertainty about Ilya Sutskever's employment status. Itamar from Codium introduced the concept of Flow Engineering with AlphaCodium, gaining attention from Andrej Karpathy. On the TheBloke Discord, engineers discussed a multi-specialty mixture-of-experts (MOE) model combining seven distinct 7 billion parameter models specialized in law, finance, and medicine. Debates on 8-bit fine-tuning and the use of bitsandbytes with GPU support were prominent. Discussions also covered model merging using tools like Mergekit and compatibility with Alpaca format. Interest in optimizing AI models on AMD hardware using AOCL blas and lapack libraries with llama.cpp was noted. Users experimented with AI for command line tasks, and the Mixtral MoE model was refined to surpass larger models in coding ability. Comparisons among LLMs such as GPT-3.5, Mixtral, Gemini Pro, and GPT-4 focused on knowledge depth, problem-solving, and speed, especially for coding tasks.
1/17/2024: Help crowdsource function calling datasets
mistral-7b dolphin-2.7-mixtral-8x7b mega-dolphin dolphin-2.6-mistral-7b-dpo llama-cpp lm-studio mistral-ai microsoft hugging-face apple function-calling quantization model-performance gpu-optimization model-selection closed-source memory-optimization linux-server api-fees headless-mode yagilb heyitsyorkie
LM Studio updated its FAQ clarifying its closed-source status and perpetual freeness for personal use with no data collection. The new beta release includes fixes and hints at upcoming 2-bit quantization support. For gaming, models like Dolphin 2.7 Mixtral 8x7B, MegaDolphin, and Dolphin 2.6 Mistral 7B DPO with Q4_K_M quantization were recommended. Discussions highlighted that single powerful GPUs outperform multi-GPU setups due to bottlenecks, with older GPUs like Tesla P40 being cost-effective. Microsoft's AutoGen Studio was introduced but has issues and requires API fees for open-source models. Linux users are advised to use llama.cpp over LM Studio due to lack of headless mode. Additional tools like LLMFarm for iOS and various Hugging Face repositories were also mentioned. "LM Studio must be running to use the local inference server as there is no headless mode available" and "matching model size to GPU memory is key for performance" were notable points.
1/16/2024: ArtificialAnalysis - a new model/host benchmark site
mixtral hermes-2-mixtral openchat-7b byte-mistral nous-research nvidia hugging-face summarization fine-tuning byte-level-tokenization multimodality inference-speed-optimization dataset-sharing quantization swyx gabriel_syme manojbh carsonpoole fullstack6209
Artificial Analysis launched a new models and hosts comparison site, highlighted by swyx. Nous Research AI Discord discussed innovative summarization techniques using NVIDIA 3090 and 2080ti GPUs for processing around 100k tokens, and adapting prompts for smaller models like OpenChat 7B. The availability of Hermes 2 Mixtral on Huggingface's HuggingChat was noted, alongside fine-tuning challenges with Mixtral using Axolotl. Discussions included byte-level tokenization experiments with Byte Mistral, multimodal training on COCO image bytes, and inference speed improvements using vllm and llama.cpp. Calls for transparency in data sharing and open-sourcing the Hermes 2 Mixtral dataset were emphasized, with comparisons of dpo and sft methods and quantized LLM use on M1 MacBook Pro.
1/16/2024: TIES-Merging
mixtral-8x7b nous-hermes-2 frankendpo-4x7b-bf16 thebloke hugging-face nous-research togethercompute oak-ridge-national-laboratory vast-ai runpod mixture-of-experts random-gate-routing quantization gptq exl2-quants reinforcement-learning-from-human-feedback supercomputing trillion-parameter-models ghost-attention model-fine-tuning reward-models sanjiwatsuki superking__ mrdragonfox _dampf kaltcit rombodawg technotech
TheBloke's Discord community actively discusses Mixture of Experts (MoE) models, focusing on random gate routing layers for training and the challenges of immediate model use. There is a robust debate on quantization methods, comparing GPTQ and EXL2 quants, with EXL2 noted for faster execution on specialized hardware. A new model, Nous Hermes 2, based on Mixtral 8x7B and trained with RLHF, claims benchmark superiority but shows some inconsistencies. The Frontier supercomputer at Oak Ridge National Laboratory is highlighted for training a trillion-parameter LLM with 14TB RAM, sparking discussions on open-sourcing government-funded AI research. Additionally, the application of ghost attention in the academicat model is explored, with mixed reactions from the community. "Random gate layer is good for training but not for immediate use," and "EXL2 might offer faster execution on specialized hardware," are key insights shared.
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.
1/11/2024: Mixing Experts vs Merging Models
gpt-4-turbo gpt-4-0613 mixtral deepseekmoe phixtral deepseek-ai hugging-face nous-research teenage-engineering discord mixture-of-experts model-merging fine-tuning rag security discord-tos model-performance prompt-engineering function-calling semantic-analysis data-frameworks ash_prabaker shacrw teknium 0xevil everyoneisgross ldj pramod8481 mgreg_42266 georgejrjrjr kenakafrosty
18 guilds, 277 channels, and 1342 messages were analyzed with an estimated reading time saved of 187 minutes. The community switched to GPT-4 turbo and discussed the rise of Mixture of Experts (MoE) models like Mixtral, DeepSeekMOE, and Phixtral. Model merging techniques, including naive linear interpolation and "frankenmerges" by SOLAR and Goliath, are driving new performance gains on open leaderboards. Discussions in the Nous Research AI Discord covered topics such as AI playgrounds supporting prompt and RAG parameters, security concerns about third-party cloud usage, debates on Discord bots and TOS, skepticism about Teenage Engineering's cloud LLM, and performance differences between GPT-4 0613 and GPT-4 turbo. The community also explored fine-tuning strategies involving DPO, LoRA, and safetensors, integration of RAG with API calls, semantic differences between MoE and dense LLMs, and data frameworks like llama index and SciPhi-AI's synthesizer. Issues with anomalous characters in fine-tuning were also raised.
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.
1/4/2024: Jeff Bezos backs Perplexity's $520m Series B.
wizardcoder-33b-v1.1 mobilellama-1.4b-base shearedllama tinyllama mixtral-8x7b perplexity anthropic google nous-research mistral-ai hugging-face document-recall rnn-memory synthetic-data benchmarking multi-gpu-support context-length model-architecture sliding-window-attention model-parallelism gpu-optimization jeff-bezos
Perplexity announced their Series B funding round with notable investor Jeff Bezos, who previously invested in Google 25 years ago. Anthropic is raising $750 million, projecting at least $850 million in annualized revenue next year and implementing "brutal" changes to their Terms of Service. Discussions in Nous Research AI Discord cover topics such as document recall limits from gigabytes of data, RNN memory and compute trade-offs, synthetic datasets, and benchmarking of models like WizardCoder-33B-V1.1, MobileLLaMA-1.4B-Base, ShearedLLaMA, and TinyLLaMA. Other highlights include UnsLOTH optimizations for multi-GPU systems, AI rap voice models, context-extending code, and architectural innovations like applying Detectron/ViT backbones to LLMs, sliding window attention in Mistral, and parallelizing Mixtral 8x7b with FSDP and HF Accelerate.
1/3/2024: RIP Coqui
sdxl diffusers-0.25 coqui mozilla hugging-face google text-to-speech performance-optimization token-management transformer-architecture image-datasets web-crawling pytorch leaderboards
Coqui, a prominent open source text-to-speech project from the Mozilla ML group, officially shut down. Discussions in the HuggingFace Discord highlighted skepticism about the claimed
3X faster speed of sdxl, attributing improvements more to techniques like torch.compile and removal of fp16 and attention rather than diffusers 0.25 features. Users confirmed that a HuggingFace user token can be used across multiple machines, though distinct tokens are recommended for safety. The Learning Loss Minimization (LLM) Leaderboard briefly experienced issues but was later confirmed operational. A Kaggle notebook was shared demonstrating how to build Transformer architectures from scratch using PyTorch. Additionally, a new image dataset with 15k shoe, sandal, and boot images was introduced for multiclass classification tasks. Explanations about the workings of the Common Crawl web-crawling process were also shared. 12/31/2023: Happy New Year
mistral-7b mixtral lm-studio mistral-ai hugging-face amd fine-tuning hardware-optimization vram emotional-intelligence model-deployment integration gpu-optimization software-updates
LM Studio community discussions highlight variations and optimizations in Dolphin and Mistral 7b models, focusing on hardware-software configurations and GPU vRAM impact on processing speed. Challenges with Mixtral model deployment on local machines and workarounds for downloading models from HuggingFace in restricted regions were addressed. Users explored enhancing AI's emotional intelligence and personalities through extended prompts, referencing research on emotional stimuli in large language models. The community also discussed hardware setups for budget AI compute servers, integration issues with ChromaDB and Autogen, and shared positive feedback on LM Studio's usability and UI. Celebrations for the New Year added a social touch to the guild interactions.
12/30/2023: Mega List of all LLMs
deita-v1.0 mixtral amazon-titan-text-express amazon-titan-text-lite nous-research hugging-face amazon mistral-ai local-attention computational-complexity benchmarking model-merging graded-modal-types function-calling data-contamination training-methods stella-biderman euclaise joey00072
Stella Biderman's tracking list of LLMs is highlighted, with resources shared for browsing. The Nous Research AI Discord discussed the Local Attention Flax module focusing on computational complexity, debating linear vs quadratic complexity and proposing chunking as a solution. Benchmark logs for various LLMs including Deita v1.0 with its SFT+DPO training method were shared. Discussions covered model merging, graded modal types, function calling in AI models, and data contamination issues in Mixtral. Community insights were sought on Amazon Titan Text Express and Amazon Titan Text Lite LLMs, including a unique training strategy involving bad datasets. Several GitHub repositories and projects like DRUGS, MathPile, CL-FoMo, and SplaTAM were referenced for performance and data quality evaluations.
12/29/2023: TinyLlama on the way
tinyllama-1.1b openai hugging-face gpu-optimization model-deployment discord-bots embedding-models inference-server hardware-compatibility model-performance beta-testing autogen context-window
The Nous/Axolotl community is pretraining a 1.1B model on 3 trillion tokens, showing promising results on HellaSwag for a small 1B model. The LM Studio Discord discussions cover extensive GPU-related issues, Discord bot integration with the OpenAI API, and hardware limitations affecting model usage. Community members also discuss server hosting for embeddings and LLMs, propose updates for Discord channels to improve model development collaboration, and address a gibberish problem in beta releases. The Autogen tool's installation and operational challenges are also clarified by users.
12/23/2023: NeurIPS Best Papers of 2023
gpt-4 palm2 hermes-2.5 mistral-7b nous-research hugging-face apple context-length malware-security video-content music-content linear-layers api-access large-language-models embedding vector-databases model-merging model-interpretability striped-hyena-architecture quantization rmsnorm attention-mechanisms
The Latent Space Pod released a 3-hour recap of the best NeurIPS 2023 papers. The Nous Research AI Discord community discussed optimizing AI performance with shorter context lengths, malware security concerns linked to HuggingFace, and shared insights on video and music content. Technical discussions included the DYAD research paper proposing a faster alternative to linear layers, Apple's ML Ferret machine learning tool, and accessing PALM2 via API. The community also explored Large Language Models focusing on specialized models, data scaling, embedding/vector databases, model merging, and interpretability, with mentions of Hermes 2.5, GPT-4, and Mistral. Additionally, there were conversations on the Striped Hyena Architecture, quantization challenges, and fixes related to RMSNorm and the "Attention is All You Need" paper.
12/19/2023: Everybody Loves OpenRouter
gpt-4 gpt-3.5 mixtral-8x7b-instruct dolphin-2.0-mistral-7b gemini openai mistral-ai google hugging-face performance memory-management api prompt-engineering local-language-models translation censorship video-generation
OpenRouter offers an easy OpenAI-compatible proxy for Mixtral-8x7b-instruct. Discord discussions highlight GPT-4 performance and usability issues compared to GPT-3.5, including memory management and accessibility problems. Users debate local language models versus OpenAI API usage, with mentions of Dolphin 2.0 Mistral 7B and Google's video generation project. Prompt engineering and custom instructions for GPT models are also key topics. Concerns about censorship on models like Gemini and translation tool preferences such as DeepL were discussed.
12/10/2023: not much happened today
mixtral-8x7b-32kseqlen mistral-7b stablelm-zephyr-3b openhermes-2.5-neural-chat-v3-3-slerp gpt-3.5 gpt-4 nous-research openai mistral-ai hugging-face ollama lm-studio fine-tuning mixture-of-experts model-benchmarking inference-optimization model-evaluation open-source decentralized-ai gpu-optimization community-engagement andrej-karpathy yann-lecun richard-blythman gabriel-syme pradeep1148 cyborg_1552
Nous Research AI Discord community discussed attending NeurIPS and organizing future AI events in Australia. Highlights include interest in open-source and decentralized AI projects, with Richard Blythman seeking co-founders. Users shared projects like Photo GPT AI and introduced StableLM Zephyr 3B. The Mixtral model, based on Mistral, sparked debate on performance and GPU requirements, with comparisons to GPT-3.5 and potential competitiveness with GPT-4 after fine-tuning. Tools like Tensorboard, Wandb, and Llamahub were noted for fine-tuning and evaluation. Discussions covered Mixture of Experts (MoE) architectures, fine-tuning with limited data, and inference optimization strategies for ChatGPT. Memes and community interactions referenced AI figures like Andrej Karpathy and Yann LeCun. The community also shared resources such as GitHub links and YouTube videos related to these models and tools.
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.
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.