All tags
Topic: "fine-tuning"
Execuhires Round 2: Scale-Meta, Lamini-AMD, and Instacart-OpenAI
o3-pro o3 o1-pro gpt-4o gpt-4.1 gpt-4.1-mini gpt-4.1-nano meta-ai-fair scale-ai lamini amd openai gemini google anthropic model-release benchmarking reasoning fine-tuning pricing model-performance direct-preference-optimization complex-problem-solving alexandr_wang sharon_zhou fidji_simo sama jack_rae markchen90 kevinweil gdb gregkamradt lechmazur wesrothmoney paul_cal imjaredz cto_junior johnowhitaker polynoamial scaling01
Meta hires Scale AI's Alexandr Wang to lead its new "Superintelligence" division following a $15 billion investment for a 49% stake in Scale. Lamini's Sharon Zhou joins AMD as VP of AI under Lisa Su, while Instacart's Fidji Simo becomes CEO of Apps at OpenAI under Sama. Meta offers over $10 million/year compensation packages to top researchers, successfully recruiting Jack Rae from Gemini. OpenAI releases o3-pro model to ChatGPT Pro users and API, outperforming o3 and setting new benchmarks like Extended NYT Connections and SnakeBench. Despite being slower than o1-pro, o3-pro excels in reasoning and complex problem-solving. OpenAI cuts o3 pricing by 80%, making it cheaper than GPT-4o and pressuring competitors like Google and Anthropic to lower prices. Users can now fine-tune the GPT-4.1 family using direct preference optimization (DPO) for subjective tasks.
not much happened today
dots-llm1 qwen3-235b xiaohongshu rednote-hilab deepseek huggingface mixture-of-experts open-source model-benchmarking fine-tuning inference context-windows training-data model-architecture model-performance model-optimization
China's Xiaohongshu (Rednote) released dots.llm1, a 142B parameter open-source Mixture-of-Experts (MoE) language model with 14B active parameters and a 32K context window, pretrained on 11.2 trillion high-quality, non-synthetic tokens. The model supports efficient inference frameworks like Docker, HuggingFace, and vLLM, and provides intermediate checkpoints every 1 trillion tokens, enabling flexible fine-tuning. Benchmarking claims it slightly surpasses Qwen3 235B on MMLU, though some concerns exist about benchmark selection and synthetic data verification. The release is notable for its truly open-source licensing and no synthetic data usage, sparking community optimism for support in frameworks such as llama.cpp and mlx.
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".
ChatGPT Codex, OpenAI's first cloud SWE agent
codex-1 openai-o3 codex-mini gemma-3 blip3-o qwen-2.5 marigold-iid deepseek-v3 lightlab gemini-2.0 lumina-next openai runway salesforce qwen deepseek google google-deepmind j1 software-engineering parallel-processing multimodality diffusion-models depth-estimation scaling-laws reinforcement-learning fine-tuning model-performance multi-turn-conversation reasoning audio-processing sama kevinweil omarsar0 iscienceluvr akhaliq osanseviero c_valenzuelab mervenoyann arankomatsuzaki jasonwei demishassabis philschmid swyx teortaxestex jaseweston
OpenAI launched Codex, a cloud-based software engineering agent powered by codex-1 (an optimized version of OpenAI o3) available in research preview for Pro, Enterprise, and Team ChatGPT users, featuring parallel task execution like refactoring and bug fixing. The Codex CLI was enhanced with quick sign-in and a new low-latency model, codex-mini. Gemma 3 is highlighted as the best open model runnable on a single GPU. Runway released the Gen-4 References API for style transfer in generation. Salesforce introduced BLIP3-o, a unified multimodal model family using diffusion transformers for CLIP image features. The Qwen 2.5 models (1.5B and 3B versions) were integrated into the PocketPal app with various chat templates. Marigold IID, a new state-of-the-art open-source depth estimation model, was released.
In research, DeepSeek shared insights on scaling and hardware for DeepSeek-V3. Google unveiled LightLab, a diffusion-based light source control in images. Google DeepMind's AlphaEvolve uses Gemini 2.0 to discover new math and reduce costs without reinforcement learning. Omni-R1 studied audio's role in fine-tuning audio LLMs. Qwen proposed a parallel scaling law inspired by classifier-free guidance. Salesforce released Lumina-Next on the Qwen base, outperforming Janus-Pro. A study found LLM performance degrades in multi-turn conversations due to unreliability. J1 is incentivizing LLM-as-a-Judge thinking via reinforcement learning. A new Qwen study correlates question and strategy similarity to predict reasoning strategies.
Prime Intellect's INTELLECT-2 and PRIME-RL advance distributed reinforcement learning
intellect-2 dreamo qwen gemini-2.5-pro dynamic-byte-latent-transformer gen-4-references mistral-medium-3 le-chat-enterprise primeintellect bytedance qwen gemma meta-ai-fair runwayml mistral-ai google distributed-training reinforcement-learning gpu-clusters model-optimization quantization multimodality agentic-ai video-understanding fine-tuning _akhaliq reach_vb osanseviero aiatmeta c_valenzuelab lmarena_ai adcock_brett
Prime Intellect released INTELLECT-2, a decentralized GPU training and RL framework with a vision for distributed AI training overcoming colocation limits. ByteDance launched DreamO, a unified image customization model on Hugging Face. Qwen released models optimized for GPTQ, GGUF, and AWQ quantization. Gemma surpassed 150 million downloads on Hugging Face. Meta released weights for the Dynamic Byte Latent Transformer and the Collaborative Reasoner framework to improve language model efficiency and reasoning. RunwayML introduced Gen-4 References, a near-realtime model requiring no fine-tuning. Mistral AI released Mistral Medium 3, a strong multimodal model, and Le Chat Enterprise, an agentic AI assistant for business. Google updated Gemini 2.5 Pro Preview with video understanding and UI improvements. "Airbnb for spare GPUs from all over the world" highlights the ongoing challenges and potential of distributed GPU training.
not much happened today
open-code-reasoning-32b open-code-reasoning-14b open-code-reasoning-7b mistral-medium-3 llama-4-maverick gemini-2.5-pro gemini-2.5-flash claude-3.7-sonnet absolute-zero-reasoner x-reasoner fastvlm parakeet-asr openai nvidia mistral-ai google apple huggingface reinforcement-learning fine-tuning code-generation reasoning vision on-device-ai model-performance dataset-release model-optimization reach_vb artificialanlys scaling01 iscienceluvr arankomatsuzaki awnihannun risingsayak
OpenAI launched both Reinforcement Finetuning and Deep Research on GitHub repos, drawing comparisons to Cognition's DeepWiki. Nvidia open-sourced Open Code Reasoning models (32B, 14B, 7B) with Apache 2.0 license, showing 30% better token efficiency and compatibility with llama.cpp, vLLM, transformers, and TGI. Independent evaluations highlight Mistral Medium 3 rivaling Llama 4 Maverick, Gemini 2.0 Flash, and Claude 3.7 Sonnet in coding and math reasoning, priced significantly lower but no longer open-source. Google's Gemini 2.5 Pro is noted as their most intelligent model with improved coding from simple prompts, while Gemini 2.5 Flash incurs a 150x cost increase over Gemini 2.0 Flash due to higher token usage and cost. The Absolute Zero Reasoner (AZR) achieves SOTA performance in coding and math reasoning via reinforced self-play without external data. Vision-language model X-REASONER is post-trained on general-domain text for reasoning. Apple ML research released FastVLM with on-device iPhone demo. HiDream LoRA trainer supports QLoRA fine-tuning under memory constraints. Nvidia's Parakeet ASR model tops Hugging Face ASR leaderboard with MLX implementation. New datasets SwallowCode and SwallowMath boost LLM performance in math and code. Overall, a quiet day with significant model releases and performance insights.
not much happened today
qwen3-14b qwen3-32b qwen3-235b phi-4-reasoning o3-mini command-a gemini-2.5-pro o4-mini olm-o2-1b o3 alibaba together-ai scaling01 microsoft deepseek cohere google epoch-ai-research inception-labs openai allenai quantization fine-tuning reinforcement-learning benchmarking video-generation diffusion-models model-performance model-evaluation model-release text-generation cline _philschmid iscienceluvr alexalbert__ _lewtun teortaxestex sarahookr reach_vb
Qwen model family released quantized versions of Qwen3 models including 14B, 32B, and 235B parameters, with promising coding capabilities in Qwen3-235B. Microsoft launched Phi-4-reasoning, a 14B parameter model distilled from OpenAI's o3-mini, emphasizing supervised fine-tuning and reinforcement learning, outperforming larger models in some benchmarks. Cohere's Command A leads SQL performance on Bird Bench. Google introduced the TRAJAN eval for video generation temporal consistency and updated the Gemini OpenAI compatibility layer. Inception Labs launched a diffusion LLM API claiming 5x speed improvements over autoregressive models. Community rankings show OpenAI's o3 model debuting strongly in web app-building tasks. Other releases include AllenAI's OLMo2 1B and additional Phi 4 variants. "Qwen3-235B shows promise for coding" and "Phi-4-reasoning tech report emphasizes SFT gains" highlight key advancements.
ChatGPT responds to GlazeGate + LMArena responds to Cohere
qwen3-235b-a22b qwen3 qwen3-moe llama-4 openai cohere lm-arena deepmind x-ai meta-ai-fair alibaba vllm llamaindex model-releases model-benchmarking performance-evaluation open-source multilinguality model-integration fine-tuning model-optimization joannejang arankomatsuzaki karpathy sarahookr reach_vb
OpenAI faced backlash after a controversial ChatGPT update, leading to an official retraction admitting they "focused too much on short-term feedback." Researchers from Cohere published a paper criticizing LMArena for unfair practices favoring incumbents like OpenAI, DeepMind, X.ai, and Meta AI Fair. The Qwen3 family by Alibaba was released, featuring models up to 235B MoE, supporting 119 languages and trained on 36 trillion tokens, with integration into vLLM and support in tools like llama.cpp. Meta announced the second round of Llama Impact Grants to promote open-source AI innovation. Discussions on AI Twitter highlighted concerns about leaderboard overfitting and fairness in model benchmarking, with notable commentary from karpathy and others.
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.
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.
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."
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."
Reasoning Models are Near-Superhuman Coders (OpenAI IOI, Nvidia Kernels)
o3 o1 o3-mini deepseek-r1 qwen-2.5 openthinker openai nvidia ollama elevenlabs sakana-ai apple reinforcement-learning gpu-kernel-optimization fine-tuning knowledge-distillation scaling-laws chain-of-thought-reasoning model-accessibility alex-wei karpathy abacaj awnihannun
o3 model achieved a gold medal at the 2024 IOI and ranks in the 99.8 percentile on Codeforces, outperforming most humans with reinforcement learning (RL) methods proving superior to inductive bias approaches. Nvidia's DeepSeek-R1 autonomously generates GPU kernels that surpass some expert-engineered kernels, showcasing simple yet effective AI-driven optimization. OpenAI updated o1 and o3-mini models to support file and image uploads in ChatGPT and released DeepResearch, a powerful research assistant based on the o3 model with RL for deep chain-of-thought reasoning. Ollama introduced OpenThinker models fine-tuned from Qwen2.5, outperforming some DeepSeek-R1 distillation models. ElevenLabs grew into a $3.3 billion company specializing in AI voice synthesis without open-sourcing their technology. Research highlights include Sakana AI Labs' TAID knowledge distillation method receiving a Spotlight at ICLR 2025, and Apple's work on scaling laws for mixture-of-experts (MoEs). The importance of open-source AI for scientific discovery was also emphasized.
small news items
gpt-4.5 gpt-5 deepseek-r1-distilled-qwen-1.5b o1-preview modernbert-0.3b qwen-0.5b o3 openai ollama mistral perplexity cerebras alibaba groq bytedance math benchmarking fine-tuning model-performance reinforcement-learning model-architecture partnerships funding jeremyphoward arankomatsuzaki sama nrehiew_ danhendrycks akhaliq
OpenAI announced plans for GPT-4.5 (Orion) and GPT-5, with GPT-5 integrating the o3 model and offering unlimited chat access in the free tier. DeepSeek R1 Distilled Qwen 1.5B outperforms OpenAI's o1-preview on math benchmarks, while ModernBERT 0.3b surpasses Qwen 0.5b at MMLU without fine-tuning. Mistral and Perplexity adopt Cerebras hardware for 10x performance gains. OpenAI's o3 model won a gold medal at the 2024 International Olympiad in Informatics. Partnerships include Qwen with Groq. Significant RLHF activity is noted in Nigeria and the global south, and Bytedance is expected to rise in AI prominence soon. "GPT5 is all you need."
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.
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.
DeepSeek R1: o1-level open weights model and a simple recipe for upgrading 1.5B models to Sonnet/4o level
deepseek-r1 deepseek-v3 qwen-2.5 llama-3.1 llama-3.3-70b deepseek ollama qwen llama reinforcement-learning fine-tuning model-distillation model-optimization reasoning reward-models multi-response-sampling model-training
DeepSeek released DeepSeek R1, a significant upgrade over DeepSeek V3 from just three weeks prior, featuring 8 models including full-size 671B MoE models and multiple distillations from Qwen 2.5 and Llama 3.1/3.3. The models are MIT licensed, allowing finetuning and distillation. Pricing is notably cheaper than o1 by 27x-50x. The training process used GRPO (reward for correctness and style outcomes) without relying on PRM, MCTS, or reward models, focusing on reasoning improvements through reinforcement learning. Distilled models can run on Ollama and show strong capabilities like writing Manim code. The release emphasizes advances in reinforcement-learning, fine-tuning, and model-distillation with a novel RL framework from DeepSeekMath.
not much happened today
deepseek-v3 llama-3-1-405b gpt-4o gpt-5 minimax-01 claude-3-haiku cosmos-nemotron-34b openai deep-learning-ai meta-ai-fair google-deepmind saama langchain nvidia mixture-of-experts coding math scaling visual-tokenizers diffusion-models inference-time-scaling retrieval-augmented-generation ai-export-restrictions security-vulnerabilities prompt-injection gpu-optimization fine-tuning personalized-medicine clinical-trials ai-agents persistent-memory akhaliq
DeepSeek-V3, a 671 billion parameter mixture-of-experts model, surpasses Llama 3.1 405B and GPT-4o in coding and math benchmarks. OpenAI announced the upcoming release of GPT-5 on April 27, 2023. MiniMax-01 Coder mode in ai-gradio enables building a chess game in one shot. Meta research highlights trade-offs in scaling visual tokenizers. Google DeepMind improves diffusion model quality via inference-time scaling. The RA-DIT method fine-tunes LLMs and retrievers for better RAG responses. The U.S. proposes a three-tier export restriction system on AI chips and models, excluding countries like China and Russia. Security vulnerabilities in AI chatbots involving CSRF and prompt injection were revealed. Concerns about superintelligence and weapons-grade AI models were expressed. ai-gradio updates include NVIDIA NIM compatibility and new models like cosmos-nemotron-34b. LangChain integrates with Claude-3-haiku for AI agents with persistent memory. Triton Warp specialization optimizes GPU usage for matrix multiplication. Meta's fine-tuned Llama models, OpenBioLLM-8B and OpenBioLLM-70B, target personalized medicine and clinical trials.
not much happened today
cosmos nvidia openai robotics autonomous-driving open-source fine-tuning foundation-models memory-optimization sama
NVIDIA has launched Cosmos, an open-source video world model trained on 20 million hours of video, aimed at advancing robotics and autonomous driving. The release sparked debate over its open-source status and technical approach. Additionally, NVIDIA announced Digits, a $3,000 personal AI supercomputer designed to democratize AI computing. The AI community expresses mixed feelings about rapid AI progress, with concerns about AGI, job displacement, and investment hype. Discussions also highlight upcoming tools for fine-tuning AI models at home and foundation models for AI robotics.
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.
o1 API, 4o/4o-mini in Realtime API + WebRTC, DPO Finetuning
o1-2024-12-17 o1 o1-pro 4o 4o-mini gemini-2-0-flash claude-3.5-sonnet claude-3.5 openai google google-deepmind function-calling structured-outputs vision reasoning webrtc realtime-api preference-tuning fine-tuning api model-performance aidan_mclau kevinweil simonw michpokrass morgymcg juberti
OpenAI launched the o1 API with enhanced features including vision inputs, function calling, structured outputs, and a new
reasoning_effort parameter, achieving 60% fewer reasoning tokens on average. The o1 pro variant is confirmed as a distinct implementation coming soon. Improvements to the Realtime API with WebRTC integration offer easier usage, longer sessions (up to 30 minutes), and significantly reduced pricing (up to 10x cheaper with mini models). DPO Preference Tuning for fine-tuning is introduced, currently available for the 4o model. Additional updates include official Go and Java SDKs and OpenAI DevDay videos. The news also highlights discussions on Google Gemini 2.0 Flash model's performance reaching 83.6% accuracy. 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.
$200 ChatGPT Pro and o1-full/pro, with vision, without API, and mixed reviews
o1 o1-pro claude-3.5-sonnet pali-gemma-2 openai google llamaindex multimodality vision fine-tuning benchmarking model-performance image-generation document-processing model-release sama bindureddy mervenoyann fchollet
OpenAI launched the o1 model with multimodal capabilities, faster reasoning, and image input support, marking it as a state-of-the-art model despite some bugs and mixed community reviews. The new o1-pro tier offers unlimited access for $200/month with notable benchmark improvements but some performance trade-offs compared to claude-3.5-sonnet. Google released the PaliGemma 2 vision-language model family in sizes 3B, 10B, and 28B, excelling in visual question answering, image segmentation, and OCR, with day-0 support for fine-tuning. LlamaIndex announced discounts and feature updates for large-scale document processing. The AI community also reacted humorously to the new pricing tiers and model comparisons. "o1 can see now, which makes it the SOTA multimodal model" and "most users will be best served by free/Plus tiers" were notable sentiments.
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.
OLMo 2 - new SOTA Fully Open LLM
llama-3-1-8b olmo-2 qwen2-5-72b-instruct smolvlm tulu-3 ai2 huggingface intel reinforcement-learning quantization learning-rate-annealing ocr fine-tuning model-training vision
AI2 has updated OLMo-2 to roughly Llama 3.1 8B equivalent, training with 5T tokens and using learning rate annealing and new high-quality data (Dolmino). They credit Tülu 3 and its "Reinforcement Learning with Verifiable Rewards" approach. On Reddit, Qwen2.5-72B instruct model shows near lossless performance with AutoRound 4-bit quantization, available on HuggingFace in 4-bit and 2-bit versions, with discussions on MMLU benchmark and quantization-aware training. HuggingFace released SmolVLM, a 2B parameter vision-language model running efficiently on consumer GPUs, supporting fine-tuning on Google Colab and demonstrating strong OCR capabilities with adjustable resolution and quantization options.
Vision Everywhere: Apple AIMv2 and Jina CLIP v2
aimv2-3b jina-clip-v2 tulu-3 llama-3-1 claude-3-5 llama-3-1-70b apple jina allen_ai autoregressive-objectives vision multilinguality multimodality image-generation model-training model-optimization reinforcement-learning fine-tuning model-benchmarking
Apple released AIMv2, a novel vision encoder pre-trained with autoregressive objectives that achieves 89.5% accuracy on ImageNet and integrates joint visual and textual objectives. Jina launched Jina CLIP v2, a multimodal embedding model supporting 89 languages and high-resolution images with efficient Matryoshka embeddings reducing dimensions by 94% with minimal accuracy loss. Allen AI introduced Tülu 3 models based on Llama 3.1 with 8B and 70B parameters, offering 2.5x faster inference and alignment via SFT, DPO, and RLVR methods, competing with Claude 3.5 and Llama 3.1 70B. These developments highlight advances in autoregressive training, vision encoders, and multilingual multimodal embeddings.
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.
Tencent's Hunyuan-Large claims to beat DeepSeek-V2 and Llama3-405B with LESS Data
claude-3.5-haiku llama-3-1 llama-3-2 mlx-lm tencent anthropic meta-ai-fair togethercompute llamaindex mixture-of-experts synthetic-data model-scaling model-architecture model-optimization kv-cache-quantization react fine-tuning scaling-laws model-efficiency model-deployment multimodality
Tencent released a notable >300B parameter MoE model pretrained on 7T tokens, including 1.5T synthetic data generated via Evol-Instruct. The model introduces novel techniques like "recycle routing" and expert-specific learning rates, alongside a compute-efficient scaling law for MoE active parameters. However, its custom license restricts use in the EU and by companies with over 100M MAU, and it avoids China-sensitive queries. Meanwhile, Anthropic launched Claude 3.5 Haiku, now available on multiple platforms, praised for intelligence and speed but criticized for a 10x price increase. Meta opened Llama AI to the U.S. defense sector, and a Llama Impact Hackathon offers a $15K prize for projects using Llama 3.1 & 3.2 Vision. LlamaIndex released a React chat UI component with Tailwind CSS and LLM backend integrations. The MLX LM model advances text generation speed and efficiency with KV cache quantization.
The AI Search Wars Have Begun — SearchGPT, Gemini Grounding, and more
gpt-4o o1-preview claude-3.5-sonnet universal-2 openai google gemini nyt perplexity-ai glean nvidia langchain langgraph weights-biases cohere weaviate fine-tuning synthetic-data distillation hallucinations benchmarking speech-to-text robotics neural-networks ai-agents sam-altman alexalbert__ _jasonwei svpino drjimfan virattt
ChatGPT launched its search functionality across all platforms using a fine-tuned version of GPT-4o with synthetic data generation and distillation from o1-preview. This feature includes a Chrome extension promoted by Sam Altman but has issues with hallucinations. The launch coincides with Gemini introducing Search Grounding after delays. Notably, The New York Times is not a partner due to a lawsuit against OpenAI. The AI search competition intensifies with consumer and B2B players like Perplexity and Glean. Additionally, Claude 3.5 Sonnet achieved a new benchmark record on SWE-bench Verified, and a new hallucination evaluation benchmark, SimpleQA, was introduced. Other highlights include the Universal-2 speech-to-text model with 660M parameters and HOVER, a neural whole-body controller for humanoid robots trained in NVIDIA Isaac simulation. AI hedge fund teams using LangChain and LangGraph were also showcased. The news is sponsored by the RAG++ course featuring experts from Weights & Biases, Cohere, and Weaviate.
not much happened today
claude-3.5-sonnet claude-3.5-haiku o1-preview mochi-1 stable-diffusion-3.5 embed-3 kerashub differential-transformer anthropic openai cohere microsoft computer-use coding-performance video-generation fine-tuning multimodality transformers attention-mechanisms model-optimization alexalbert fchollet rasbt
Anthropic released upgraded Claude 3.5 Sonnet and Claude 3.5 Haiku models featuring a new computer use capability that allows interaction with computer interfaces via screenshots and actions like mouse movement and typing. The Claude 3.5 Sonnet achieved state-of-the-art coding performance on SWE-bench Verified with a 49% score, surpassing OpenAI's o1-preview. Anthropic focuses on teaching general computer skills rather than task-specific tools, with expected rapid improvements. Other releases include Mochi 1, an open-source video generation model, Stable Diffusion 3.5 with Large and Medium variants, and Embed 3 by Cohere, a multimodal embedding model for text and image search. KerasHub was launched by François Chollet, unifying KerasNLP and KerasCV with 37 pretrained models. Microsoft introduced the Differential Transformer to reduce attention noise via differential attention maps, and research on transformer attention layers was shared by Rasbt.
DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing
bitnet-b1.58 llama-3.1-nemotron-70b-instruct gpt-4o claude-3.5-sonnet uc-berkeley deepmind openai microsoft nvidia archetype-ai boston-dynamics toyota-research google adobe openai mistral tesla meta-ai-fair model-optimization on-device-ai fine-tuning large-corpus-processing gpu-acceleration frameworks model-benchmarking rohanpaul_ai adcock_brett david-patterson
UC Berkeley's EPIC lab introduces innovative LLM data operators with projects like LOTUS and DocETL, focusing on effective programming and computation over large data corpora. This approach contrasts GPU-rich big labs like Deepmind and OpenAI with GPU-poor compound AI systems. Microsoft open-sourced BitNet b1.58, a 1-bit ternary parameter LLM enabling 4-20x faster training and on-device inference at human reading speeds. Nvidia released Llama-3.1-Nemotron-70B-Instruct, a fine-tuned open-source model outperforming GPT-4o and Claude-3.5-sonnet. These developments highlight advances in model-optimization, on-device-ai, and fine-tuning.
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.
not much happened today
claudette llama-3-1 yi-lightning gpt-4o claude-3.5-sonnet answer-ai tencent notebooklm motherduck perplexity dropbox openai meta-ai-fair yi-ai zyphra-ai anthropic langchain openai synthetic-data fine-tuning sql audio-processing on-device-ai dataset-release transformer llm-reasoning ai-safety code-generation ai-pricing ai-job-market fchollet aravsrinivas svpino swyx
Answer.ai launched fastdata, a synthetic data generation library using
claudette and Tencent's Billion Persona paper. NotebookLM became customizable, and Motherduck introduced notable LLMs in SQL implementations. Perplexity and Dropbox announced competitors to Glean. OpenAI unveiled audio chat completions priced at 24 cents per minute. Meta AI released Llama 3.1, powering Lenovo AI Now's on-device agent. Yi-Lightning model ranked #6 globally, surpassing GPT-4o. Zyphra AI released the large Zyda-2 dataset with 5 trillion tokens. François Chollet clarified transformer architecture as set-processing, not sequence-processing. Research suggests memorization aids LLM reasoning. Anthropic updated its Responsible Scaling Policy for AI safety. Tools like Perplexity Finance, Open Canvas by LangChain, and AlphaCodium code generation tool were highlighted. Approximately $500 million was raised for AI agent startups, with ongoing discussions on AI's job market impact. Combining prompt caching with the Batches API can yield a 95% discount on Claude 3.5 Sonnet tokens. The AI Nobel Prize
claude-3.5-sonnet reka-flash got openai anthropic reka-ai zep artificial-neural-networks nobel-prize knowledge-graphs memory-layers real-time-voice-api vision fine-tuning prompt-caching multimodality function-calling ocr open-source single-sign-on software-testing ai-assisted-coding ai-ethics geoff-hinton john-hopfield philschmid alexalbert mervenoyann clementdelangue svpino bindureddy ylecun rohanpaul_ai
Geoff Hinton and John Hopfield won the Nobel Prize in Physics for their work on Artificial Neural Networks. The award citation spans 14 pages highlighting their contributions. Zep released a new community edition of their low-latency memory layer for AI agents, emphasizing knowledge graphs for memory. At OpenAI's DevDay, new features like real-time voice API, vision model fine-tuning, and prompt caching with a 50% discount on reused tokens were introduced. Anthropic's Claude 3.5 Sonnet was recognized as the best model currently. Reka AI Labs updated their Reka Flash model with enhanced multimodal and function calling capabilities. The GOT (Generic OCR Transformer) achieved 98.79% accuracy on OCR benchmarks. Discussions on open-source AI models highlighted their role in fostering competition and decentralization. Software development insights included the importance of Single Sign-On (SSO), thorough testing, and AI-assisted coding workflows. Ethical and societal topics covered critiques of tax policies and the appointment of France's first Minister of AI.
Not much technical happened today
whisper-v3-turbo llama-3 llamaindex openai poolside liquidai perplexity-ai meta-ai-fair cohere fujitsu mixture-of-experts context-windows model-optimization fine-tuning quantization model-training alignment synthetic-data model-architecture agentic-ai nick-turley arav-srinivas francois-fleuret finbarr-timbers lewtun francois-chollet jerry-j-liu mmitchell-ai jxnlco
OpenAI announced raising $6.6B in new funding at a $157B valuation, with ChatGPT reaching 250M weekly active users. Poolside raised $500M to advance AGI development. LiquidAI introduced three new MoE models (1B, 3B, 40B) with a 32k context window and efficient token handling. OpenAI released Whisper V3 Turbo, an open-source multilingual model with significant speed improvements. Meta AI FAIR is hiring research interns focusing on LLM reasoning, alignment, synthetic data, and novel architectures. Cohere partnered with Fujitsu to launch Takane, a custom Japanese model. Technical discussions included challenges in LoRA fine-tuning, float8 quantization in Keras, and new tools like create-llama for agent templates. Industry commentary raised concerns about AI development priorities and highlighted freelancing opportunities in AI.
a quiet weekend
o1 datagemma aloha demostart firefly-ai-video-model pixtral-12b gamegen-o openai google-deepmind adobe mistral-ai tencent supermaven 11x cohere anthropic latent-space-university stanford microsoft mila notre-dame reinforcement-learning chain-of-thought reasoning robotics diffusion-models multimodality video-generation model-training reflection-tuning mathematical-reasoning model-benchmarking fine-tuning george-hotz terence-tao adcock_brett rohanpaul_ai bindureddy fchollet philschmid
OpenAI released the new o1 model, leveraging reinforcement learning and chain-of-thought prompting to excel in reasoning benchmarks, achieving an IQ-like score of 120. Google DeepMind introduced DataGemma to reduce hallucinations by connecting LLMs with real-world data, and unveiled ALOHA and DemoStart for robot dexterity using diffusion methods. Adobe previewed its Firefly AI Video Model with text-to-video and generative extend features. Mistral launched the multimodal Pixtral 12B model, and Tencent presented the GameGen-O open-world video game generation model. Several research papers from Stanford, OpenAI, Microsoft, Mila, and Notre Dame focus on advanced reasoning, self-verification, and reflection tuning techniques. Experts like Terence Tao and George Hotz have shared mixed but optimistic views on o1's capabilities. Seed funding rounds include Supermaven ($12M) and 11x ($24M).
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.
Reflection 70B, by Matt from IT Department
llama-3.1-70b llama-3 claude-3.5-sonnet hyperwrite glaive fine-tuning chain-of-thought instruction-following synthetic-data quantization model-evaluation prompt-engineering matt-shumer sahil-chaudhary
Reflection Tuning technique has been used by a two-person team from Hyperwrite and Glaive to finetune llama-3.1-70b, showing strong performance improvements with minimal synthetic data. The approach builds on the concept of adding
thinking and reflection steps to outputs, related to the Chain of Thought method. Despite some criticisms like contamination concerns, worse coding performance, and reliance on system prompts, the model has received positive reception and comparisons to claude-3.5-sonnet. The work highlights efficient instruction tuning and synthetic data generation for large models. Everybody shipped small things this holiday weekend
gpt-4o-voice gemini claude jamba-1.5 mistral-nemo-minitron-8b xai google anthropic openai cognition ai21-labs nvidia langchain fine-tuning long-context parameter-efficient-fine-tuning latex-rendering real-time-audio virtual-try-on resource-tags low-code ai-agents workspace-organization model-benchmarking dario-amodei scott-wu fchollet svpino
xAI announced the Colossus 100k H100 cluster capable of training an FP8 GPT-4 class model in 4 days. Google introduced Structured Output for Gemini. Anthropic discussed Claude's performance issues possibly due to API prompt modifications. OpenAI enhanced controls for File Search in their Assistants API. Cognition and Anthropic leaders appeared on podcasts. The viral Kwai-Kolors virtual try-on model and the open-source real-time audio conversational model Mini-Omni (similar to gpt-4o-voice) were released. Tutorials on parameter-efficient fine-tuning with LoRA and QLoRA, long-context embedding challenges, and Claude's LaTeX rendering feature were highlighted. AI21 Labs released Jamba 1.5 models with a 256K context window and faster long-context performance. NVIDIA debuted Mistral-Nemo-Minitron-8B on the Open LLM Leaderboard. LangChain introduced resource tags for workspace organization, and a low-code AI app toolkit was shared by svpino. Legal AI agents and financial agent evaluations using LangSmith were also featured.
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.
The DSPy Roadmap
dspy litel-lm gemini chatgpt-4o grok-2 hermes-3 databricks mit google openai x-ai nous-research astribot apple sakana-ai model-optimization fine-tuning optimizers interactive-optimization robotics autonomous-systems voice image-generation open-source-models scientific-research streaming caching omar-khattab giffmana
Omar Khattab announced joining Databricks before his MIT professorship and outlined the roadmap for DSPy 2.5 and 3.0+, focusing on improving core components like LMs, signatures, optimizers, and assertions with features such as adopting LiteLLM to reduce code and enhance caching and streaming. The roadmap also includes developing more accurate, cost-effective optimizers, building tutorials, and enabling interactive optimization tracking. On AI Twitter, Google launched Gemini Live, a mobile conversational AI with voice and 10 voices, alongside Pixel Buds Pro 2 with a custom Tensor A1 chip. OpenAI updated ChatGPT-4o, reclaiming the top spot on LMSYS Arena. xAI released Grok-2 in beta, achieving SOTA in image generation with FLUX 1. Nous Research released open-source Hermes 3 models in 8B, 70B, and 405B sizes, with the 405B model achieving SOTA. Robotics updates include Astribot's humanoid robot and Apple's tabletop robot with Siri voice commands. Sakana AI introduced "The AI Scientist," an autonomous AI research system.
not much happened today
grok-2 claude-3.5-sonnet claude-3.5 gpt-4 chatgpt-4o-latest anthropic x-ai google-deepmind openai mistral-ai meta-ai-fair salesforce box prompt-caching model-performance vision fine-tuning multilinguality ai-safety design-automation document-processing ai-agents ai-integration ai-job-market ai-acceleration humor demis-hassabis francois-chollet
Anthropic rolled out prompt caching in its API, reducing input costs by up to 90% and latency by 80%, enabling instant fine-tuning with longer prompts. xAI released Grok-2, a new model competing with frontier models from Google DeepMind, OpenAI, Anthropic, Mistral AI, and Meta AI Fair, supporting vision and text inputs and integrating external image generation models. Claude 3.5 Sonnet is reported to outperform GPT-4 in coding and reasoning, while ChatGPT-4o-latest shows reasoning improvements. François Chollet proposed a theory defining intelligence as the efficiency of operationalizing past information for future tasks. The Aya project involves 3000 collaborators building multilingual AI datasets. Demis Hassabis discussed AI hype and safe AI development in a podcast. Tools like Dora AI for Figma and Box's AI API enhance design automation and document processing. Salesforce released DEI, an open AI software engineering agents framework with a 55% resolve rate on SWE-Bench Lite. Industry trends highlight rapid AI integration, networking importance in the AI job market, and potential OpenAI GPT-4 expansion in response to competitors. Memes include humor about Apple Vision Pro.
not much happened today
llama-3 llama-3-1 grok-2 claude-3.5-sonnet gpt-4-turbo nous-research nvidia salesforce goodfire-ai anthropic x-ai google-deepmind box langchain fine-tuning prompt-caching mechanistic-interpretability model-performance multimodality agent-frameworks software-engineering-agents api document-processing text-generation model-releases vision image-generation efficiency scientific-discovery fchollet demis-hassabis
GPT-5 delayed again amid a quiet news day. Nous Research released Hermes 3 finetune of Llama 3 base models, rivaling FAIR's instruct tunes but sparking debate over emergent existential crisis behavior with 6% roleplay data. Nvidia introduced Minitron finetune of Llama 3.1. Salesforce launched a DEI agent scoring 55% on SWE-Bench Lite. Goodfire AI secured $7M seed funding for mechanistic interpretability work. Anthropic rolled out prompt caching in their API, cutting input costs by up to 90% and latency by 80%, aiding coding assistants and large document processing. xAI released Grok-2, matching Claude 3.5 Sonnet and GPT-4 Turbo on LMSYS leaderboard with vision+text inputs and image generation integration. Claude 3.5 Sonnet reportedly outperforms GPT-4 in coding and reasoning. François Chollet defined intelligence as efficient operationalization of past info for future tasks. Salesforce's DEI framework surpasses individual agent performance. Google DeepMind's Demis Hassabis discussed AGI's role in scientific discovery and safe AI development. Dora AI plugin generates landing pages in under 60 seconds, boosting web team efficiency. Box AI API beta enables document chat, data extraction, and content summarization. LangChain updated Python & JavaScript integration docs.
not much happened today
qwen2-math-72b gpt-4o claude-3.5-sonnet gemini-1.5-pro llama-3.1-405b idefics3-llama-8b anthropic google mistral-ai llamaindex math fine-tuning synthetic-data reinforcement-learning bug-bounty visual-question-answering open-source retrieval-augmented-generation agentic-ai ai-safety policy rohanpaul_ai anthropicai mervenoyann jeremyphoward omarsar0 ylecun bindureddy
Qwen2-Math-72B outperforms GPT-4o, Claude-3.5-Sonnet, Gemini-1.5-Pro, and Llama-3.1-405B on math benchmarks using synthetic data and advanced optimization techniques. Google AI cuts pricing for Gemini 1.5 Flash by up to 78%. Anthropic expands its bug bounty program targeting universal jailbreaks in next-gen safety systems. Tutorial on QLoRA fine-tuning of IDEFICS3-Llama 8B for visual question answering released. A Chinese open weights model surpasses previous MATH benchmark records. Surveys on Mamba models and LLM-based agents for software engineering highlight advancements and applications. Open-source tools like R2R RAG engine and LlamaIndex Workflows simplify building complex AI applications. Mistral AI introduces customizable AI agents. Concerns raised about California bill SB 1047's focus on existential risk and debates on banning open-source AI. Memes and humor continue in AI communities.
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.
Llama 3.1: The Synthetic Data Model
llama-3-405b llama-3-1 llama-3 meta-ai-fair groq fireworks synthetic-data fine-tuning reinforcement-learning multilinguality long-context tool-use code-generation math model-licensing inference-speed model-deployment bindureddy thomas
Meta AI has released Llama 3.1, including a 405B parameter model that triggers regulatory considerations like the EU AI Act and SB 1047. The model incorporates extensive synthetic data techniques for code, math, multilinguality, long context, and tool use fine-tuning, with RLHF using synthetic preference data from Llama 2. The launch was coordinated across major inference providers, with Groq demonstrating 750 tokens per second inference speed and Fireworks leading in pricing. The updated license explicitly allows synthetic data generation, marking a significant step in open frontier-class LLMs and cost-efficiency improvements since March.
Llama 3.1 Leaks: big bumps to 8B, minor bumps to 70b, and SOTA OSS 405b model
llama-3-1-405b llama-3-8b llama-3-70b llama-3-1-8b gpt-4o gpt-4o-mini claude-3-5 qwen-2 meta-ai-fair openai alibaba multilinguality code-generation context-windows model-training synthetic-data benchmarking reasoning fine-tuning model-performance dataset-release swyx philschmid jjitsev lewtun teknium1 adcock_brett
Llama 3.1 leaks reveal a 405B dense model with 128k context length, trained on 39.3M GPU hours using H100-80GB GPUs, and fine-tuned with over 25M synthetic examples. The model shows significant benchmark improvements, especially for the 8B and 70B variants, with some evals suggesting the 70B outperforms GPT-4o. GPT-4o Mini launched as a cost-efficient variant with strong performance but some reasoning weaknesses. Synthetic datasets like NuminaMath enable models such as Alibaba Qwen 2 to surpass GPT-4o and Claude 3.5 in math competitions. Discussions include reasoning task benchmarks and dataset building for improved reasoning.
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.
Gemma 2 tops /r/LocalLlama vibe check
gemma-2-9b gemma-2-27b llama-3 mistral-7b phi-3 qwen gemma llamaindex mistral-ai cohere deepseek-ai nous-research eureka-labs model-comparison local-llms multilinguality model-efficiency fine-tuning ai-education ai-teaching-assistants andrej-karpathy
Gemma 2 (9B, 27B) is highlighted as a top-performing local LLM, praised for its speed, multilingual capabilities, and efficiency on consumer GPUs like the 2080ti. It outperforms models like Llama 3 and Mistral 7B in various tasks, including non-English text processing and reasoning. The community discussion on /r/LocalLlama reflects strong preference for Gemma 2, with 18 mentions, compared to 10 mentions for Llama 3 and 9 mentions for Mistral. Other models like Phi 3 and Qwen also received mentions but are considered surpassed by Gemma 2. Additionally, Andrej Karpathy announced the launch of Eureka Labs, an AI+Education startup aiming to create an AI-native school with AI Teaching Assistants, starting with the LLM101n course to teach AI training fundamentals. This initiative is seen as a significant development in AI education.
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.
Gemma 2: The Open Model for Everyone
gemma-2 qwen-72b mixtral-8x22b-instruct claude-3.5-sonnet google-deepmind alibaba mistral-ai anthropic knowledge-distillation attention-mechanisms multilingual-models multimodality model-training model-optimization memory-optimization fine-tuning kathleen-kenealy daniel-han
Gemma 2, a 27B parameter model from google-deepmind, was released with innovations like 1:1 local-global attention alternation and logit soft-capping, leveraging knowledge distillation to train smaller models on over 50× the compute-optimal token quantity. The model supports multilingual and multimodal capabilities, with fine-tuning success on over 200 Indic language variants. The Open LLM Leaderboard highlights alibaba's Qwen 72B as the top model, with mistral-ai's Mixtral-8x22B-Instruct also ranking highly. Anthropic launched Claude 3.5 Sonnet, improving intelligence at mid-tier cost and speed. Research on eliminating matrix multiplication in LLMs promises significant memory savings without performance loss. Kathleen Kenealy and Daniel Han provided insights on Gemma 2's tokenizer and attention scaling respectively.
Claude Crushes Code - 92% HumanEval and Claude.ai Artifacts
claude-3.5-sonnet claude-3-opus gpt-4o anthropic openai cognition benchmarking model-performance coding model-optimization fine-tuning instruction-following model-efficiency model-release api performance-optimization alex-albert
Claude 3.5 Sonnet, released by Anthropic, is positioned as a Pareto improvement over Claude 3 Opus, operating at twice the speed and costing one-fifth as much. It achieves state-of-the-art results on benchmarks like GPQA, MMLU, and HumanEval, surpassing even GPT-4o and Claude 3 Opus on vision tasks. The model demonstrates significant advances in coding capabilities, passing 64% of test cases compared to 38% for Claude 3 Opus, and is capable of autonomously fixing pull requests. Anthropic also introduced the Artifacts feature, enabling users to interact with AI-generated content such as code snippets and documents in a dynamic workspace, similar to OpenAI's Code Interpreter. This release highlights improvements in performance, cost-efficiency, and coding proficiency, signaling a growing role for LLMs in software development.
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.
Is this... OpenQ*?
deepseek-coder-v2 llama-3-8b nemotron-4-340b stable-diffusion-3-medium deepseek_ai anthropic runwayml openai apple nvidia stability-ai luma-labs reward-tampering test-time-search mathematical-reasoning process-supervision fine-tuning on-device-ai video-generation cost-efficiency context-length coding image-understanding multimodality adcock_brett clementdelangue svpino
DeepSeekCoder V2 promises GPT4T-beating performance at a fraction of the cost. Anthropic released new research on reward tampering. Runway launched their Sora response and Gen-3 Alpha video generation model. A series of papers explore "test-time" search techniques improving mathematical reasoning with models like LLaMa-3 8B. Apple announced Apple Intelligence with smarter Siri and image/document understanding, partnered with OpenAI to integrate ChatGPT into iOS 18, and released 20 new CoreML models with LoRA fine-tuning for specialization. NVIDIA released Nemotron-4 340B, an open model matching GPT-4 performance. DeepSeek-Coder-V2 excels in coding and math with 338 programming languages and 128K context length. Stability AI released Stable Diffusion 3 Medium weights. Luma Labs launched Dream Machine for 5-second video generation from text and images.
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.
Hybrid SSM/Transformers > Pure SSMs/Pure Transformers
mamba-2-hybrid gpt-4 qwen-72b table-llava-7b nvidia lamini-ai sakana-ai luma-labs mixture-of-experts benchmarking fine-tuning multimodality text-to-video model-performance memory-optimization preference-optimization video-understanding multimodal-tables bryan-catanzaro bindureddy ylecun ctnzr corbtt realsharonzhou andrew-n-carr karpathy _akhaliq omarsar0
NVIDIA's Bryan Catanzaro highlights a new paper on Mamba models, showing that mixing Mamba and Transformer blocks outperforms either alone, with optimal attention below 20%. Mixture-of-Agents (MoA) architecture improves LLM generation quality, scoring 65.1% on AlpacaEval 2.0 versus GPT-4 Omni's 57.5%. The LiveBench AI benchmark evaluates reasoning, coding, writing, and data analysis. A hybrid Mamba-2-Hybrid model with 7% attention surpasses a Transformer on MMLU accuracy, jumping from 50% to 53.6%. GPT-4 performs better at temperature=1. Qwen 72B leads open-source models on LiveBench AI. LaminiAI Memory Tuning achieves 95% accuracy on a SQL agent task, improving over instruction fine-tuning. Sakana AI Lab uses evolutionary strategies for preference optimization. Luma Labs Dream Machine demonstrates advanced text-to-video generation. The MMWorld benchmark evaluates multimodal video understanding, and Table-LLaVa 7B competes with GPT-4V on multimodal table tasks.
The Last Hurrah of Stable Diffusion?
llama-3-8b llama-3 qwen-2 gpt-4 gpt-4o stability-ai togethercompute model-architecture fine-tuning benchmarks dataset-release model-evaluation reasoning model-training retrieval-augmented-generation multimodality emad-mostaque rohanpaul_ai fchollet mikeknoop micahgoldblum teknium1 rasbt percyliang
Stability AI launched Stable Diffusion 3 Medium with models ranging from 450M to 8B parameters, featuring the MMDiT architecture and T5 text encoder for image text rendering. The community has shown mixed reactions following the departure of key researchers like Emad Mostaque. On AI models, Llama 3 8B Instruct shows strong evaluation correlation with GPT-4, while Qwen 2 Instruct surpasses Llama 3 on MMLU benchmarks. The Mixture of Agents (MoA) framework outperforms GPT-4o on AlpacaEval 2.0. Techniques like Spectrum and QLoRA enable efficient fine-tuning with less VRAM. Research on grokking reveals transformers can transition from memorization to generalization through extended training. Benchmark initiatives include the $1M ARC Prize Challenge for AGI progress and LiveBench, a live LLM benchmark to prevent dataset contamination. The Character Codex Dataset offers open data on over 15,000 characters for RAG and synthetic data. The MLX 0.2 tool enhances LLM experience on Apple Silicon Macs with improved UI and faster retrieval-augmented generation.
HippoRAG: First, do know(ledge) Graph
qwen-2 gpt-4 hipporag alibaba openai knowledge-graphs personalized-pagerank multi-hop-retrieval chain-of-thought implicit-reasoning sparse-autoencoders model-interpretability model-efficiency model-architecture fine-tuning reinforcement-learning rohanpaul_ai omarsar0 nabla_theta huybery
Alibaba released new open-source Qwen2 models ranging from 0.5B to 72B parameters, achieving SOTA results on benchmarks like MMLU and HumanEval. Researchers introduced Sparse Autoencoders to interpret GPT-4 neural activity, improving feature representation. The HippoRAG paper proposes a hippocampus-inspired retrieval augmentation method using knowledge graphs and Personalized PageRank for efficient multi-hop reasoning. New techniques like Stepwise Internalization enable implicit chain-of-thought reasoning in LLMs, enhancing accuracy and speed. The Buffer of Thoughts (BoT) method improves reasoning efficiency with significant cost reduction. A novel scalable MatMul-free LLM architecture competitive with SOTA Transformers at billion-parameter scale was also presented. "Single-Step, Multi-Hop retrieval" is highlighted as a key advancement in retrieval speed and cost.
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.
Somebody give Andrej some H100s already
gpt-2 openai fineweb meta-ai-fair nvidia tesla cuda fine-tuning training-time gpu-acceleration convolutional-neural-networks real-time-processing ai-safety ai-regulation andrej-karpathy yann-lecun elon-musk francois-chollet svpino mervenoyann
OpenAI's GPT-2 sparked controversy five years ago for being "too dangerous to release." Now, with FineWeb and llm.c, a tiny GPT-2 model can be trained in 90 minutes for $20 using 8xA100 GPUs, with the full 1.6B model estimated to take 1 week and $2.5k. The project is notable for its heavy use of CUDA (75.8%) aiming to simplify the training stack. Meanwhile, a Twitter debate between Yann LeCun and Elon Musk highlighted the importance of convolutional neural networks (CNNs) in real-time image processing for autonomous driving, with LeCun emphasizing scientific research's role in technological progress. LeCun also criticized AI doomsday scenarios, arguing for cautious optimism about AI safety and regulation.
Ten Commandments for Deploying Fine-Tuned Models
claude-3-opus claude-3 gpt-4o anthropic google openai fine-tuning prompt-engineering model-evaluation feature-alteration benchmarking model-performance open-source-models kyle-corbitt bindureddy alexalbert__
Gemini-in-Google-Slides is highlighted as a useful tool for summarizing presentations. Kyle Corbitt's talk on deploying fine-tuned models in production emphasizes avoiding fine-tuning unless necessary, focusing on prompting, data quality, appropriate model choice, and thorough evaluation. Anthropic showcased feature alteration in Claude AI, demonstrating control over model behavior and increased understanding of large language models. Open-source models like GPT-4o are approaching closed-source performance on benchmarks like MMLU for simple tasks, though advanced models remain necessary for complex automation.
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.
Cursor reaches >1000 tok/s finetuning Llama3-70b for fast file editing
gpt-4 gpt-4o gpt-4-turbo gpt-4o-mini llama bloom stable-diffusion cursor openai anthropic google-deepmind huggingface speculative-decoding code-edits multimodality image-generation streaming tool-use fine-tuning benchmarking mmlu model-performance evaluation synthetic-data context-windows sama abacaj imjaredz erhartford alexalbert svpino maximelabonne _philschmid
Cursor, an AI-native IDE, announced a speculative edits algorithm for code editing that surpasses GPT-4 and GPT-4o in accuracy and latency, achieving speeds of over 1000 tokens/s on a 70b model. OpenAI released GPT-4o with multimodal capabilities including audio, vision, and text, noted to be 2x faster and 50% cheaper than GPT-4 turbo, though with mixed coding performance. Anthropic introduced streaming, forced tool use, and vision features for developers. Google DeepMind unveiled Imagen Video and Gemini 1.5 Flash, a small model with a 1M-context window. HuggingFace is distributing $10M in free GPUs for open-source AI models like Llama, BLOOM, and Stable Diffusion. Evaluation insights highlight challenges with LLMs on novel problems and benchmark saturation, with new benchmarks like MMLU-Pro showing significant drops in top model performance.
Google I/O in 60 seconds
gemini-1.5-pro gemini-flash gemini-ultra gemini-pro gemini-nano gemma-2 llama-3-70b paligemma imagen-3 veo google google-deepmind youtube tokenization model-performance fine-tuning vision multimodality model-release model-training model-optimization ai-integration image-generation watermarking hardware-optimization voice video-understanding
Google announced updates to the Gemini model family, including Gemini 1.5 Pro with 2 million token support, and the new Gemini Flash model optimized for speed with 1 million token capacity. The Gemini suite now includes Ultra, Pro, Flash, and Nano models, with Gemini Nano integrated into Chrome 126. Additional Gemini features include Gemini Gems (custom GPTs), Gemini Live for voice conversations, and Project Astra, a live video understanding assistant. The Gemma model family was updated with Gemma 2 at 27B parameters, offering near-llama-3-70b performance at half the size, plus PaliGemma, a vision-language open model inspired by PaLI-3. Other launches include DeepMind's Veo, Imagen 3 for photorealistic image generation, and a Music AI Sandbox collaboration with YouTube. SynthID watermarking now extends to text, images, audio, and video. The Trillium TPUv6 codename was revealed. Google also integrated AI across its product suite including Workspace, Email, Docs, Sheets, Photos, Search, and Lens. "The world awaits Apple's answer."
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.
DeepSeek-V2 beats Mixtral 8x22B with >160 experts at HALF the cost
deepseek-v2 llama-3-120b llama-3-400b gpt-4 mistral phi claude gemini mai-1 med-gemini deepseek-ai mistral-ai microsoft openai scale-ai tesla nvidia google-deepmind mixture-of-experts multi-head-attention model-inference benchmarking overfitting robotics teleoperation open-source multimodality hallucination-detection fine-tuning medical-ai model-training erhartford maximelabonne bindureddy adcock_brett drjimfan clementdelangue omarsar0 rohanpaul_ai
DeepSeek V2 introduces a new state-of-the-art MoE model with 236B parameters and a novel Multi-Head Latent Attention mechanism, achieving faster inference and surpassing GPT-4 on AlignBench. Llama 3 120B shows strong creative writing skills, while Microsoft is reportedly developing a 500B parameter LLM called MAI-1. Research from Scale AI highlights overfitting issues in models like Mistral and Phi, whereas GPT-4, Claude, Gemini, and Llama maintain benchmark robustness. In robotics, Tesla Optimus advances with superior data collection and teleoperation, LeRobot marks a move toward open-source robotics AI, and Nvidia's DrEureka automates robot skill training. Multimodal LLM hallucinations are surveyed with new mitigation strategies, and Google's Med-Gemini achieves SOTA on medical benchmarks with fine-tuned multimodal models.
$100k to predict LMSYS human preferences in a Kaggle contest
llama-3-70b llama-3 gpt-4 claude-3-opus prometheus-2 groq openai lmsys scale-ai ai2 nvidia benchmarking datasets fine-tuning reinforcement-learning model-alignment hallucination parameter-efficient-fine-tuning scalable-training factuality chatbot-performance bindureddy drjimfan percyliang seungonekim mobicham clefourrier
Llama 3 models are making breakthroughs with Groq's 70B model achieving record low costs per million tokens. A new Kaggle competition offers a $100,000 prize to develop models predicting human preferences from a dataset of over 55,000 user-LLM conversations. Open source evaluator LLMs like Prometheus 2 outperform proprietary models such as GPT-4 and Claude 3 Opus in judgment tasks. New datasets like WildChat1M provide over 1 million ChatGPT interaction logs with diverse and toxic examples. Techniques like LoRA fine-tuning show significant performance gains, and NVIDIA's NeMo-Aligner toolkit enables scalable LLM alignment across hundreds of GPUs. Factuality-aware alignment methods are proposed to reduce hallucinations in LLM outputs.
Evals: The Next Generation
gpt-4 gpt-5 gpt-3.5 phi-3 mistral-7b llama-3 scale-ai mistral-ai reka-ai openai moderna sanctuary-ai microsoft mit meta-ai-fair benchmarking data-contamination multimodality fine-tuning ai-regulation ai-safety ai-weapons neural-networks model-architecture model-training model-performance robotics activation-functions long-context sam-altman jim-fan
Scale AI highlighted issues with data contamination in benchmarks like MMLU and GSM8K, proposing a new benchmark where Mistral overfits and Phi-3 performs well. Reka released the VibeEval benchmark for multimodal models addressing multiple choice benchmark limitations. Sam Altman of OpenAI discussed GPT-4 as "dumb" and hinted at GPT-5 with AI agents as a major breakthrough. Researchers jailbroke GPT-3.5 via fine-tuning. Global calls emerged to ban AI-powered weapons, with US officials urging human control over nuclear arms. Ukraine launched an AI consular avatar, while Moderna partnered with OpenAI for medical AI advancements. Sanctuary AI and Microsoft collaborate on AI for general-purpose robots. MIT introduced Kolmogorov-Arnold networks with improved neural network efficiency. Meta AI is training Llama 3 models with over 400 billion parameters, featuring multimodality and longer context.
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.
Llama-3-70b is GPT-4-level Open Model
llama-3-70b llama-3-8b llama-3 llama-2-70b mistral-7b grok-3 stable-diffusion-3 vasa-1 meta-ai-fair groq nvidia amazon microsoft benchmarking model-performance fine-tuning function-calling arithmetic image-generation video-generation energy-usage gpu-demand political-bias ai-safety scaling context-windows tokenization elon-musk
Meta has released Llama 3, their most capable open large language model with 8B and 70B parameter versions supporting 8K context length and outperforming previous models including Llama 2 and Mistral 7B. Groq serves the Llama 3 70B model at 500-800 tokens/second, making it the fastest GPT-4-level token source. Discussions highlight AI scaling challenges with Elon Musk stating that training Grok 3 will require 100,000 Nvidia H100 GPUs, and AWS planning to acquire 20,000 B200 GPUs for a 27 trillion parameter model. Microsoft unveiled VASA-1 for lifelike talking face generation, while Stable Diffusion 3 and its extensions received mixed impressions. Concerns about AI energy usage and political bias in AI were also discussed.
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.
Cohere Command R+, Anthropic Claude Tool Use, OpenAI Finetuning
c4ai-command-r-plus claude-3 gpt-3.5-turbo gemini mistral-7b gemma-2 claude-3-5 llama-3 vicuna cohere anthropic openai microsoft stability-ai opera-software meta-ai-fair google-deepmind mistral-ai tool-use multilingual-models rag fine-tuning quantum-computing audio-generation local-inference context-windows model-size-analysis model-comparison
Cohere launched Command R+, a 104B dense model with 128k context length focusing on RAG, tool-use, and multilingual capabilities across 10 key languages. It supports Multi-Step Tool use and offers open weights for research. Anthropic introduced tool use in beta for Claude, supporting over 250 tools with new cookbooks for practical applications. OpenAI enhanced its fine-tuning API with new upgrades and case studies from Indeed, SK Telecom, and Harvey, promoting DIY fine-tuning and custom model training. Microsoft achieved a quantum computing breakthrough with an 800x error rate improvement and the most usable qubits to date. Stability AI released Stable Audio 2.0, improving audio generation quality and control. The Opera browser added local inference support for large language models like Meta's Llama, Google's Gemma, and Vicuna. Discussions on Reddit highlighted Gemini's large context window, analysis of GPT-3.5-Turbo model size, and a battle simulation between Claude 3 and ChatGPT using local 7B models like Mistral and Gemma.
Evals-based AI Engineering
jamba bamboo qwen-1.5-moe grok-1.5 llama2-7b openai mistral-ai x-ai llamaindex evaluation fine-tuning prompt-engineering voice-cloning quantization model-optimization code-generation context-windows hamel-husain alec-radford
Hamel Husain emphasizes the importance of comprehensive evals in AI product development, highlighting evaluation, debugging, and behavior change as key iterative steps. OpenAI released a voice engine demo showcasing advanced voice cloning from small samples, raising safety concerns. Reddit discussions introduced new models like Jamba (hybrid Transformer-SSM with MoE), Bamboo (7B LLM with high sparsity based on Mistral), Qwen1.5-MoE (efficient parameter activation), and Grok 1.5 (128k context length, surpassing GPT-4 in code generation). Advances in quantization include 1-bit Llama2-7B models outperforming full precision and the QLLM quantization toolbox supporting GPTQ/AWQ/HQQ methods.
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.
Claude 3 is officially America's Next Top Model
claude-3-opus claude-3-sonnet claude-3-haiku gpt-4o-mini mistral-7b qwen-72b anthropic mistral-ai huggingface openrouter stable-diffusion automatic1111 comfyui fine-tuning model-merging alignment ai-ethics benchmarking model-performance long-context cost-efficiency model-evaluation mark_riedl ethanjperez stuhlmueller ylecun aravsrinivas
Claude 3 Opus outperforms GPT4T and Mistral Large in blind Elo rankings, with Claude 3 Haiku marking a new cost-performance frontier. Fine-tuning techniques like QLoRA on Mistral 7B and evolutionary model merging on HuggingFace models are highlighted. Public opinion shows strong opposition to ASI development. Research supervision opportunities in AI alignment are announced. The Stable Diffusion 3 (SD3) release raises workflow concerns for tools like ComfyUI and automatic1111. Opus shows a 5% performance dip on OpenRouter compared to the Anthropic API. A new benchmark stresses LLM recall at long contexts, with Mistral 7B struggling and Qwen 72b performing well.
Andrew likes Agents
gpt-3.5 gpt-4 cyberrealistic_v40 platypus-xl sdxl-lightning openai stability-ai agents human-eval-benchmark fine-tuning local-llm-deployment inference-speed image-generation lora upscaling workflow-optimization andrew-ng lilian-weng emad
Andrew Ng's The Batch writeup on Agents highlighted the significant improvement in coding benchmark performance when using an iterative agent workflow, with GPT-3.5 wrapped in an agent loop achieving up to 95.1% correctness on HumanEval, surpassing GPT-4 zero-shot at 67.0%. The report also covers new developments in Stable Diffusion models like Cyberrealistic_v40, Platypus XL, and SDXL Lightning for Naruto-style image generation, alongside innovations in LoRA and upscaling techniques. Discussions on local LLM deployment and optimization focus on hardware setups and finetuning strategies for efficient inference and multi-user serving. Emad's departure from Stability AI and new Sora videos from OpenAI were also noted.
not much happened today
llama-2-70b llama-2-7b mistral-7b qwen-1.5 llava microsoft mistral-ai ollama fine-tuning synthetic-data retrieval-augmented-generation embeddings hardware-optimization performance-benchmarks model-memory multimodality
The Reddit community /r/LocalLlama discusses fine-tuning and training LLMs, including tutorials and questions on training models with specific data like dictionaries and synthetic datasets with 25B+ tokens. Users explore retrieval-augmented generation (RAG) challenges with models like mistral-7b and embedding generation for EEG brain activity. Discussions include hardware optimization for running llama-2-70b locally under budget constraints, and performance benchmarks for qwen-1.5 models. There is interest in extending LLM capabilities, such as converting llama-2-7b into a vision-capable model like llava and improving model memory for longer context retention.
Welcome /r/LocalLlama!
cerebrum-8x7b mixtral-7b gpt-3.5-turbo gemini-pro moistral-11b-v1 claude-opus qwen-vl-chat sakana openinterpreter reddit aether-research mistral-ai nvidia lmdeploy model-merging benchmarking quantization performance-optimization deployment vision fine-tuning training-data synthetic-data rag gui
Sakana released a paper on evolutionary model merging. OpenInterpreter launched their O1 devkit. Discussions highlight Claude Haiku's underrated performance with 10-shot examples. On Reddit's IPO, AINews introduces Reddit summaries starting with /r/LocalLlama, covering upcoming subreddits like r/machinelearning and r/openai. Aether Research released Cerebrum 8x7b based on Mixtral, matching GPT-3.5 Turbo and Gemini Pro on reasoning tasks, setting a new open-source reasoning SOTA. Moistral 11B v1 finetuned model from Cream-Phi-2 creators was released. A creative writing benchmark uses Claude Opus as judge. Hobbyists explore 1.58 BitNet ternary quantization and 1-bit LLMs training. Nvidia's Blackwell (h200) chip supports FP4 precision quantization. LMDeploy v0.2.6+ enables efficient vision-language model deployment with models like Qwen-VL-Chat. Users seek GUIs for LLM APIs with plugin and RAG support. Pipelines for synthetic training data generation and fine-tuning language models for chat are discussed.
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.
The world's first fully autonomous AI Engineer
gpt-4 devin cognition-labs openai reinforcement-learning fine-tuning long-term-reasoning planning ai-agents software-engineering model-integration asynchronous-chat ide agentic-ai patrick-collison fred-ehrsam tim-dettmers
Cognition Labs's Devin is highlighted as a potentially groundbreaking AI software engineer agent capable of learning unfamiliar technologies, addressing bugs, deploying frontend apps, and fine-tuning its own AI models. It integrates OpenAI's GPT-4 with reinforcement learning and features tools like asynchronous chat, browser, shell access, and an IDE. The system claims advanced long-term reasoning and planning abilities, attracting praise from investors like Patrick Collison and Fred Ehrsam. The technology is noted for its potential as one of the most advanced AI agents, sparking excitement about agents and AGI.
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.
Stable Diffusion 3 — Rombach & Esser did it again!
stable-diffusion-3 claude-3 orca dolphincoder-starcoder2-15b stability-ai anthropic microsoft latitude perplexity-ai llamaindex tripo-ai diffusion-models multimodality benchmarking human-evaluation text-generation image-generation 3d-modeling fine-tuning roleplay coding dataset-release soumith-chintala bill-peebles swyx kevinafischer jeremyphoward akhaliq karinanguyen_ aravsrinivas
Over 2500 new community members joined following Soumith Chintala's shoutout, highlighting growing interest in SOTA LLM-based summarization. The major highlight is the detailed paper release of Stable Diffusion 3 (SD3), showcasing advanced text-in-image control and complex prompt handling, with the model outperforming other SOTA image generation models in human-evaluated benchmarks. The SD3 model is based on an enhanced Diffusion Transformer architecture called MMDiT. Meanwhile, Anthropic released Claude 3 models, noted for human-like responses and emotional depth, scoring 79.88% on HumanEval but costing over twice as much as GPT-4. Microsoft launched new Orca-based models and datasets, and Latitude released DolphinCoder-StarCoder2-15b with strong coding capabilities. Integration of image models by Perplexity AI and 3D CAD generation by PolySpectra powered by LlamaIndex were also highlighted. "SD3's win rate beats all other SOTA image gen models (except perhaps Ideogram)" and "Claude 3 models are very good at generating d3 visualizations from text descriptions."
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.
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.
Ring Attention for >1M Context
gemini-pro gemma-7b gemma-2b deepseek-coder-6.7b-instruct llama-cpp google cuda-mode nvidia polymind deepseek ollama runpod lmstudio long-context ringattention pytorch cuda llm-guessing-game chatbots retrieval-augmented-generation vram-optimization fine-tuning dynamic-prompt-optimization ml-workflows gpu-scaling model-updates liu zaharia abbeel
Google Gemini Pro has sparked renewed interest in long context capabilities. The CUDA MODE Discord is actively working on implementing the RingAttention paper by Liu, Zaharia, and Abbeel, including extensions from the World Model RingAttention paper, with available PyTorch and CUDA implementations. TheBloke Discord discussed various topics including LLM guessing game evaluation, chatbot UX comparisons between Nvidia's Chat with RTX and Polymind, challenges in retrieval-augmented generation (RAG) integration, VRAM optimization, fine-tuning for character roleplay using Dynamic Prompt Optimization (DPO), and model choices like deepseek-coder-6.7B-instruct. There was also discussion on ML workflows on Mac Studio, with preferences for llama.cpp over ollama, and scaling inference cost-effectively using GPUs like the 4090 on Runpod. LM Studio users face manual update requirements for version 0.2.16, which includes support for Gemma models and bug fixes, especially for MacOS. The Gemma 7B model has had performance issues, while Gemma 2B received positive feedback.
Karpathy emerges from stealth?
mistral-7b mixtral-8x7b zephyr-7b gpt-4 llama-2 intel mistral-ai audiogen thebloke tokenization quantization model-optimization fine-tuning model-merging computational-efficiency memory-optimization retrieval-augmented-generation multi-model-learning meta-reasoning dataset-sharing open-source ethical-ai community-collaboration andrej-karpathy
Andrej Karpathy released a comprehensive 2-hour tutorial on tokenization, detailing techniques up to GPT-4's tokenizer and noting the complexity of Llama 2 tokenization with SentencePiece. Discussions in AI Discord communities covered model optimization and efficiency, focusing on quantization of models like Mistral 7B and Zephyr-7B to reduce memory usage for consumer GPUs, including Intel's new weight-only quantization algorithm. Efforts to improve computational efficiency included selective augmentation reducing costs by 57.76% and memory token usage versus kNN for Transformers. Challenges in hardware compatibility and software issues were shared, alongside fine-tuning techniques such as LoRA and model merging. Innovative applications of LLMs in retrieval-augmented generation (RAG), multi-model learning, and meta-reasoning were explored. The community emphasized dataset sharing, open-source releases like SDXL VAE encoded datasets and Audiogen AI codecs, and ethical AI use with censorship and guardrails. Collaboration and resource sharing remain strong in these AI communities.
Companies liable for AI hallucination is Good Actually for AI Engineers
mistral-next large-world-model sora babilong air-canada huggingface mistral-ai quantization retrieval-augmented-generation fine-tuning cuda-optimization video-generation ai-ethics dataset-management open-source community-driven-development andrej-karpathy
Air Canada faced a legal ruling requiring it to honor refund policies communicated by its AI chatbot, setting a precedent for corporate liability in AI engineering accuracy. The tribunal ordered a refund of $650.88 CAD plus damages after the chatbot misled a customer about bereavement travel refunds. Meanwhile, AI community discussions highlighted innovations in quantization techniques for GPU inference, Retrieval-Augmented Generation (RAG) and fine-tuning of LLMs, and CUDA optimizations for PyTorch models. New prototype models like Mistral-Next and the Large World Model (LWM) were introduced, showcasing advances in handling large text contexts and video generation with models like Sora. Ethical and legal implications of AI autonomy were debated alongside challenges in dataset management. Community-driven projects such as the open-source TypeScript agent framework bazed-af emphasize collaborative AI development. Additionally, benchmarks like BABILong for up to 10M context evaluation and tools from karpathy were noted.
Sora pushes SOTA
gemini-1.5 sora h20-gpt mistral-7b llama-13b mistralcasualml mixtral-instruct yi-models openai google-deepmind nvidia mistral-ai h2oai multimodality gpu-power-management long-context model-merging fine-tuning retrieval-augmented-generation role-play-model-optimization cross-language-integration training-loss synthetic-data-generation coding-support
Discord communities analyzed over 20 guilds, 312 channels, and 10550 messages reveal intense discussions on AI developments. Key highlights include the Dungeon Master AI assistant for Dungeons and Dragons using models like H20 GPT, GPU power supply debates involving 3090 and 3060 GPUs, and excitement around Google's Gemini 1.5 with its 1 million token context window and OpenAI's Sora model. Challenges with large world models (LWM) multimodality, GPT-assisted coding, and role-play model optimization with Yi models and Mixtral Instruct were discussed. Technical issues like model merging errors with MistralCasualML, fine-tuning scripts like AutoFineTune, and cross-language engineering via JSPyBridge were also prominent. NVIDIA's Chat with RTX feature leveraging retrieval-augmented generation (RAG) on 30+ series GPUs was compared to LMStudio's support for Mistral 7b and Llama 13b models. The community is cautiously optimistic about these frontier models' applications in media and coding.
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.
AI2 releases OLMo - the 4th open-everything LLM
olmo-1b olmo-7b olmo-65b miqu-70b mistral-medium distilbert-base-uncased ai2 allenai mistral-ai tsmc asml zeiss fine-tuning gpu-shortage embedding-chunking json-generation model-optimization reproducible-research self-correction vram-constraints programming-languages nathan-lambert lhc1921 mrdragonfox yashkhare_ gbourdin
AI2 is gaining attention in 2024 with its new OLMo models, including 1B and 7B sizes and a 65B model forthcoming, emphasizing open and reproducible research akin to Pythia. The Miqu-70B model, especially the Mistral Medium variant, is praised for self-correction and speed optimizations. Discussions in TheBloke Discord covered programming language preferences, VRAM constraints for large models, and fine-tuning experiments with Distilbert-base-uncased. The Mistral Discord highlighted challenges in the GPU shortage affecting semiconductor production involving TSMC, ASML, and Zeiss, debates on open-source versus proprietary models, and fine-tuning techniques including LoRA for low-resource languages. Community insights also touched on embedding chunking strategies and JSON output improvements.
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.
GPT4Turbo A/B Test: gpt-4-1106-preview
gpt-4-turbo gpt-4 gpt-3.5 openhermes-2.5-mistral-7b-4.0bpw exllamav2 llama-2-7b-chat mistral-instruct-v0.2 mistrallite llama2 openai huggingface thebloke nous-research mistral-ai langchain microsoft azure model-loading rhel dataset-generation llm-on-consoles fine-tuning speed-optimization api-performance prompt-engineering token-limits memory-constraints text-generation nlp-tools context-window-extension sliding-windows rope-theta non-finetuning-context-extension societal-impact
OpenAI released a new GPT-4 Turbo version, prompting a natural experiment in summarization comparing the November 2023 and January 2024 versions. The TheBloke Discord discussed troubleshooting model loading errors with OpenHermes-2.5-Mistral-7B-4.0bpw and exllamav2, debates on RHEL in ML, dataset generation for understanding GPT flaws, and running LLMs like Llama and Mistral on consoles. LangChain fine-tuning challenges for Llama2 were also noted. The OpenAI Discord highlighted GPT-4 speed inconsistencies, API vs web performance, prompt engineering with GPT-3.5 and GPT-4 Turbo, and DALL-E typo issues in image text. Discussions included NLP tools like semantic-text-splitter and collaboration concerns with GPT-4 Vision on Azure. The Nous Research AI Discord focused on extending context windows with Mistral instruct v0.2, MistralLite, and LLaMA-2-7B-Chat achieving 16,384 token context, plus alternatives like SelfExtend for context extension without fine-tuning. The societal impact of AI technology was also considered.
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.
Google Solves Text to Video
mistral-7b llava google-research amazon-science huggingface mistral-ai together-ai text-to-video inpainting space-time-diffusion code-evaluation fine-tuning inference gpu-rentals multimodality api model-integration learning-rates
Google Research introduced Lumiere, a text-to-video model featuring advanced inpainting capabilities using a Space-Time diffusion process, surpassing previous models like Pika and Runway. Manveer from UseScholar.org compiled a comprehensive list of code evaluation benchmarks beyond HumanEval, including datasets from Amazon Science, Hugging Face, and others. Discord communities such as TheBloke discussed topics including running Mistral-7B via API, GPU rentals, and multimodal model integration with LLava. Nous Research AI highlighted learning rate strategies for LLM fine-tuning, issues with inference, and benchmarks like HumanEval and MBPP. RestGPT gained attention for controlling applications via RESTful APIs, showcasing LLM application capabilities.
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.
1/16/2024: 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.
1/12/2024: 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.
1/9/2024: Nous Research lands $5m for Open Source AI
qlora phi-3 mixtral ollama nous-research openai rabbit-tech context-window fine-tuning synthetic-data activation-beacon transformer-architecture seed-financing real-time-voice-agents trillion-parameter-models kenakafrosty _stilic_ teknium
Nous Research announced a $5.2 million seed financing focused on Nous-Forge, aiming to embed transformer architecture into chips for powerful servers supporting real-time voice agents and trillion parameter models. Rabbit R1 launched a demo at CES with mixed reactions. OpenAI shipped the GPT store and briefly leaked an upcoming personalization feature. A new paper on Activation Beacon proposes a solution to extend LLMs' context window significantly, with code to be released on GitHub. Discussions also covered QLORA, fine-tuning, synthetic data, and custom architectures for LLMs.
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.
1/6-7/2024: LlaMA Pro - an alternative to PEFT/RAG??
llama-3 llama-3-1-1b llama-3-8-3b gpt-4 gpt-3.5 dall-e openai mistral-ai llamaindex langchain fine-tuning model-expansion token-limits privacy multilinguality image-generation security custom-models model-training yannic-kilcher
New research papers introduce promising Llama Extensions including TinyLlama, a compact 1.1B parameter model pretrained on about 1 trillion tokens for 3 epochs, and LLaMA Pro, an 8.3B parameter model expanding LLaMA2-7B with additional training on 80 billion tokens of code and math data. LLaMA Pro adds layers to avoid catastrophic forgetting and balances language and code tasks but faces scrutiny for not using newer models like Mistral or Qwen. Meanwhile, OpenAI Discord discussions reveal insights on GPT-4 token limits, privacy reassurances, fine-tuning for GPT-3.5, challenges with multi-language image recognition, custom GPT creation requiring ChatGPT Plus, and security concerns in GPT deployment. Users also share tips on dynamic image generation with DALL-E and logo creation.
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.
12/27/2023: NYT vs OpenAI
phi2 openhermes-2.5-mistral-7b llama-2-7b llama-2-13b microsoft-research mistral-ai apple amd model-performance fine-tuning llm-api gpu-optimization hardware-configuration multi-gpu inference-speed plugin-release conversation-history
The LM Studio Discord community extensively discussed model performance comparisons, notably between Phi2 by Microsoft Research and OpenHermes 2.5 Mistral 7b, with focus on U.S. history knowledge and fine-tuning for improved accuracy. Technical challenges around LLM API usage, conversation history maintenance, and GPU optimization for inference speed were addressed. Hardware discussions covered DDR4 vs DDR5, multi-GPU setups, and potential of Apple M1/M3 and AMD AI CPUs for AI workloads. The community also announced the ChromaDB Plugin v3.0.2 release enabling image search in vector databases. Users shared practical tips on running multiple LM Studio instances and optimizing resource usage.
12/24/2023: Dolphin Mixtral 8x7b is wild
dolphin glm3 chatglm3-ggml mistral-ai ollama google openai fine-tuning hardware-compatibility gpu-inference local-model-hosting model-integration rocm-integration performance-issues autogen linux model-training eric-hartford
Mistral models are recognized for being uncensored, and Eric Hartford's Dolphin series applies uncensoring fine-tunes to these models, gaining popularity on Discord and Reddit. The LM Studio Discord community discusses various topics including hardware compatibility, especially GPU performance with Nvidia preferred, fine-tuning and training models, and troubleshooting issues with LM Studio's local model hosting capabilities. Integration efforts with GPT Pilot and a beta release for ROCm integration are underway. Users also explore the use of Autogen for group chat features and share resources like the Ollama NexusRaven library. Discussions highlight challenges with running LM Studio on different operating systems, model performance issues, and external tools like Google Gemini and ChatGLM3 compilation.
12/13/2023 SOLAR10.7B upstages Mistral7B?
solar-10.7b llama-2 mistral-7b phi-2 gpt-4 gemini upstage nous-research openai mistral-ai microsoft depth-up-scaling pretraining synthetic-data gpu-training api-usage model-integration agi asi chat-models vision model-performance fine-tuning
Upstage released the SOLAR-10.7B model, which uses a novel Depth Up-Scaling technique built on the llama-2 architecture and integrates mistral-7b weights, followed by continued pre-training. The Nous community finds it promising but not exceptional. Additionally, weights for the phi-2 base model were released, trained on 1.4 trillion tokens including synthetic texts created by GPT-3 and filtered by GPT-4, using 96 A100 GPUs over 14 days. On OpenAI's Discord, users discussed challenges with various GPT models, including incoherent outputs, API usage limitations, and issues with GPT-4 Vision API. Conversations also covered understanding AGI and ASI, concerns about OpenAI's partnership with Axel Springer, and pricing changes for GPT Plus. Discussions included the Gemini chat model integrated into Bard and comparisons with GPT-4 performance.
12/11/2023: Mixtral beats GPT3.5 and Llama2-70B
mixtral-8x7b gpt-4 gpt-3.5-turbo llama-3 openhermes-2.5 llava-v1.5-13b-gptq mistral-ai openai huggingface sparse-mixture-of-experts fine-tuning quantization gpu-hardware transformers model-deployment open-source coding-datasets
Mistral AI announced the Mixtral 8x7B model featuring a Sparse Mixture of Experts (SMoE) architecture, sparking discussions on its potential to rival GPT-4. The community debated GPU hardware options for training and fine-tuning transformer models, including RTX 4070s, A4500, RTX 3090s with nvlink, and A100 GPUs. Interest was expressed in fine-tuning Mixtral and generating quantized versions, alongside curating high-quality coding datasets. Resources shared include a YouTube video on open-source model deployment, an Arxiv paper, GitHub repositories, and a blog post on Mixture-of-Experts. Discussions also touched on potential open-source releases of GPT-3.5 Turbo and llama-3, and running OpenHermes 2.5 on Mac M3 Pro with VRAM considerations.
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.