All tags
Company: "huggingface"
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
deepseek-r1-0528 pali-gemma-2 gemma-3 shieldgemma-2 txgemma gemma-3-qat gemma-3n-preview medgemma dolphingemma signgemma claude-4 opus-4 claude-sonnet-4 codestral-embed bagel qwen nemotron-cortexa gemini-2.5-pro deepseek-ai huggingface gemma claude bytedance qwen nemotron sakana-ai-labs benchmarking model-releases multimodality code-generation model-performance long-context reinforcement-learning model-optimization open-source yuchenj_uw _akhaliq clementdelangue osanseviero alexalbert__ guillaumelample theturingpost lmarena_ai epochairesearch scaling01 nrehiew_ ctnzr
DeepSeek R1 v2 model released with availability on Hugging Face and inference partners. The Gemma model family continues prolific development including PaliGemma 2, Gemma 3, and others. Claude 4 and its variants like Opus 4 and Claude Sonnet 4 show top benchmark performance, including new SOTA on ARC-AGI-2 and WebDev Arena. Codestral Embed introduces a 3072-dimensional code embedder. BAGEL, an open-source multimodal model by ByteDance, supports reading, reasoning, drawing, and editing with long mixed contexts. Benchmarking highlights include Nemotron-CORTEXA topping SWEBench and Gemini 2.5 Pro performing on VideoGameBench. Discussions on random rewards effectiveness focus on Qwen models. "Opus 4 NEW SOTA ON ARC-AGI-2. It's happening - I was right" and "Claude 4 launch has dev moving at a different pace" reflect excitement in the community.
Granola launches team notes, while Notion launches meeting transcription
gpt-4.1 gpt-4o-mini gpt-4.1-mini claude-opus claude-sonnet claude-o3 qwen3 seed1.5-vl llama-4 am-thinking-v1 openai anthropic alibaba meta-ai-fair huggingface granola coding instruction-following benchmarking model-releases reasoning image-generation collaborative-software model-performance kevinweil scaling01 steph_palazzolo andersonbcdefg reach_vb yuchenj_uw qtnx_ _akhaliq risingsayak
GPT-4.1 is now available in ChatGPT for Plus, Pro, and Team users, focusing on coding and instruction following, with GPT 4.1 mini replacing GPT 4o mini. Anthropic is releasing new Claude models including Claude Opus and Claude Sonnet, though some criticism about hallucinations in Claude O3 was noted. Alibaba shared the Qwen3 Technical Report with strong benchmark results from Seed1.5-VL. Meta FAIR announced new models and datasets but faced criticism on Llama 4. AM-Thinking-v1 launched on Hugging Face as a 32B scale reasoning model. Granola raised $43M in Series B and launched Granola 2.0 with a Notion-like UI. The AI ecosystem shows rapid iteration and cloning of ideas, emphasizing execution and distribution.
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.
The Ultra-Scale Playbook: Training LLMs on GPU Clusters
deepseek-native-sparse-attention r1-1776 paligemma-2-mix muse baichuan-m1-14b stripedhyena-2 huggingface deepseek perplexity-ai google-deepmind microsoft baichuan stripedhyena gpu-training scaling multimodality vision model-training foundation-models medical-llm genome-modeling robotic-manipulation interactive-content eliebakouch nouamanetazi lvwerra thom-wolf proftomyeh alex-wang aravsrinivas _akhaliq _philschmid mervenoyann reach_vb arankomatsuzaki maximelabonne
Huggingface released "The Ultra-Scale Playbook: Training LLMs on GPU Clusters," an interactive blogpost based on 4000 scaling experiments on up to 512 GPUs, providing detailed insights into modern GPU training strategies. DeepSeek introduced the Native Sparse Attention (NSA) model, gaining significant community attention, while Perplexity AI launched R1-1776, an uncensored and unbiased version of DeepSeek's R1 model. Google DeepMind unveiled PaliGemma 2 Mix, a multi-task vision-language model available in 3B, 10B, and 28B sizes. Microsoft introduced Muse, a generative AI model trained on the game Bleeding Edge, and presented Magma, a foundation model for multimodal AI agents excelling in UI navigation and robotic manipulation. Baichuan-M1-14B was announced as a state-of-the-art medical LLM trained on 20T tokens, and a fully open-source 40B genome modeling model using StripedHyena 2 architecture was also released. "Making your own gaming experience is coming sooner than you'd think," noted in relation to Muse.
Project Stargate: $500b datacenter (1.7% of US GDP) and Gemini 2 Flash Thinking 2
gemini-2.0-flash deepseek-r1 qwen-32b openai softbank oracle arm microsoft nvidia huggingface deepseek-ai long-context quantization code-interpretation model-distillation open-source agi-research model-performance memory-optimization noam-shazeer liang-wenfeng
Project Stargate, a US "AI Manhattan project" led by OpenAI and Softbank, supported by Oracle, Arm, Microsoft, and NVIDIA, was announced with a scale comparable to the original Manhattan project costing $35B inflation adjusted. Despite Microsoft's reduced role as exclusive compute partner, the project is serious but not immediately practical. Meanwhile, Noam Shazeer revealed a second major update to Gemini 2.0 Flash Thinking, enabling 1M token long context usable immediately. Additionally, AI Studio introduced a new code interpreter feature. On Reddit, DeepSeek R1, a distillation of Qwen 32B, was released for free on HuggingChat, sparking discussions on self-hosting, performance issues, and quantization techniques. DeepSeek's CEO Liang Wenfeng highlighted their focus on fundamental AGI research, efficient MLA architecture, and commitment to open-source development despite export restrictions, positioning DeepSeek as a potential alternative to closed-source AI trends.
not much happened today
helium-1 qwen-2.5 phi-4 sky-t1-32b-preview o1 codestral-25.01 phi-3 mistral llama-3 gpt-3.5 llama-3 gpt-3.5 llmquoter kyutai-labs lmstudio mistralai llamaindex huggingface langchainai hyperbolic-labs replit fchollet philschmid multilinguality token-level-distillation context-windows model-performance open-source reasoning coding retrieval-augmented-generation hybrid-retrieval multiagent-systems video large-video-language-models dynamic-ui voice-interaction gpu-rentals model-optimization semantic-deduplication model-inference reach_vb awnihannun lior_on_ai sophiamyang omarsar0 skirano yuchenj_uw fchollet philschmid
Helium-1 Preview by kyutai_labs is a 2B-parameter multilingual base LLM outperforming Qwen 2.5, trained on 2.5T tokens with a 4096 context size using token-level distillation from a 7B model. Phi-4 (4-bit) was released in lmstudio on an M4 max, noted for speed and performance. Sky-T1-32B-Preview is a $450 open-source reasoning model matching o1's performance with strong benchmark scores. Codestral 25.01 by mistralai is a new SOTA coding model supporting 80+ programming languages and offering 2x speed.
Innovations include AutoRAG for optimizing retrieval-augmented generation pipelines, Agentic RAG for autonomous query reformulation and critique, Multiagent Finetuning using societies of models like Phi-3, Mistral, LLaMA-3, and GPT-3.5 for reasoning improvements, and VideoRAG incorporating video content into RAG with LVLMs.
Applications include a dynamic UI AI chat app by skirano on Replit, LangChain tools like DocTalk for voice PDF conversations, AI travel agent tutorials, and news summarization agents. Hyperbolic Labs offers competitive GPU rentals including H100, A100, and RTX 4090. LLMQuoter enhances RAG accuracy by identifying key quotes.
Infrastructure updates include MLX export for LLM inference from Python to C++ by fchollet and SemHash semantic text deduplication by philschmid.
ChatGPT Canvas GA
llama-3-70b llama-3-1-8b tgi-v3 deepseek-v2.5-1210 coconut openai deepseek-ai meta-ai-fair huggingface cognition-labs hyperbolic google-deepmind code-execution gpt-integration model-finetuning gradient-checkpointing context-length latent-space-reasoning performance-optimization gpu-memory-optimization kubernetes gpu-marketplace ai-capabilities employment-impact neurips-2024 ai-scaling humor arav_srinivas sama jonathan-frankle dylan
OpenAI launched ChatGPT Canvas to all users, featuring code execution and GPT integration, effectively replacing Code Interpreter with a Google Docs-like interface. Deepseek AI announced their V2.5-1210 update improving performance on MATH-500 (82.8%) and LiveCodebench. Meta AI Fair introduced COCONUT, a new continuous latent space reasoning paradigm. Huggingface released TGI v3, processing 3x more tokens and running 13x faster than vLLM on long prompts. Cognition Labs released Devin, an AI developer building Kubernetes operators. Hyperbolic raised $12M Series A to build an open AI platform with an H100 GPU marketplace. Discussions included AI capabilities and employment impact, and NeurIPS 2024 announcements with Google DeepMind demos and a debate on AI scaling. On Reddit, Llama 3.3-70B supports 90K context length finetuning using Unsloth with gradient checkpointing and Apple's Cut Cross Entropy (CCE) algorithm, fitting on 41GB VRAM. Llama 3.1-8B reaches 342K context lengths with Unsloth, surpassing native limits.
not much happened today
o1-full sora gpt-4.5 gpt-4 claude-3.5-sonnet llama-3-1-nemotron-51b llama-3-1 llama-3 nemotron-51b openai google-deepmind anthropic nvidia huggingface vision model-performance neural-architecture-search model-optimization multimodality model-release model-training reinforcement-learning image-generation lucas-beyer alexander-kolesnikov xiaohua-zhai aidan_mclau giffmana joannejang sama
OpenAI announced their "12 Days of OpenAI" event with daily livestreams and potential releases including the O1 full model, Sora video model, and GPT-4.5. Google DeepMind released the GenCast weather model capable of 15-day forecasts in 8 minutes using TPU chips, and launched Genie 2, a model generating playable 3D worlds from single images. Leading vision researchers Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai moved from DeepMind to OpenAI, which is opening a Zürich office. Criticism arose over OpenAI's strategy and model quality compared to Anthropic and Claude 3.5 Sonnet. On Reddit, a modified llama.cpp supports Nvidia's Llama-3_1-Nemotron-51B, matching performance of larger 70B models via NAS optimization.
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.
Common Corpus: 2T Open Tokens with Provenance
qwen-2.5-coder claude-3.5-sonnet janusflow-1.3b ocronos-vintage pleais huggingface langchainai deepseek alibaba anthropic provenance ocr multilingual-datasets prompt-engineering multimodality image-generation code-generation quantization model-scaling inference-efficiency tim-dettmers tom-doerr omarsar0 swyx madiator reach_vb
Pleais via Huggingface released Common Corpus, the largest fully open multilingual dataset with over 2 trillion tokens including detailed provenance information. They also introduced OCRonos-Vintage, a 124M-parameter OCR correction model that efficiently fixes digitization errors on CPU and GPU, unlocking knowledge from PDFs. On AI tools, LangChainAI launched Prompt Canvas for collaborative prompt engineering, while DeepSeek released JanusFlow 1.3B, a unified multimodal LLM integrating autoregressive and rectified flow models for enhanced image understanding and generation. Alibaba Cloud announced Qwen2.5-Coder, a code-focused LLM with advanced coding capabilities, and Claude 3.5 Sonnet was highlighted for superior code generation. Discussions on quantization challenges and scaling laws for precision by Tim Dettmers and others emphasized the impact of low-precision training on model scalability and inference efficiency. "Scaling Laws for Precision" paper insights and alternative efficiency methods were also noted.
Nothing much happened today
chameleon-7b chameleon-30b xlam-1b gpt-3.5 phi-3-mini mistral-7b-v3 huggingface truth_terminal microsoft apple openai meta-ai-fair yi axolotl amd salesforce function-calling multimodality model-releases model-updates model-integration automaticity procedural-memory text-image-video-generation
HuggingFace released a browser-based timestamped Whisper using transformers.js. A Twitter bot by truth_terminal became the first "semiautonomous" bot to secure VC funding. Microsoft and Apple abruptly left the OpenAI board amid regulatory scrutiny. Meta is finalizing a major upgrade to Reddit comments addressing hallucination issues. The Yi model gained popularity on GitHub with 7.4K stars and 454 forks, with potential integration with Axolotl for pregeneration and preprocessing. AMD technologies enable household/small business AI appliances. Meta released Chameleon-7b and Chameleon-30b models on HuggingFace supporting unified text and image tokenization. Salesforce's xLAM-1b model outperforms GPT-3.5 in function calling despite its smaller size. Anole pioneered open-source multimodal text-image-video generation up to 720p 144fps. Phi-3 Mini expanded from 3.8B to 4.7B parameters with function calling, competing with Mistral-7b v3. "System 2 distillation" in humans relates to automaticity and procedural memory.
Problems with MMLU-Pro
mmlu-pro llama-3-8b-q8 gpt4all-3.0 chatgpt claude llama gemini mobilellm runway-gen-3-alpha meta-3d-gen huggingface meta-ai-fair salesforce runway nomic-ai pineapple argil-ai benchmarking prompt-engineering model-evaluation model-performance multimodality automated-dataset-generation video-generation open-source-models ai-assistants text-to-3d deepfake transformers reasoning wenhu-chen danhendrycks clementine ylecun adcock_brett svpino rohanpaul_ai
MMLU-Pro is gaining attention as the successor to MMLU on the Open LLM Leaderboard V2 by HuggingFace, despite community concerns about evaluation discrepancies and prompt sensitivity affecting model performance, notably a 10-point improvement in Llama-3-8b-q8 with simple prompt tweaks. Meta's MobileLLM research explores running sub-billion parameter LLMs on smartphones using shared weights and deeper architectures. Salesforce's APIGen introduces an automated dataset generation system for function-calling tasks outperforming larger models. Runway Gen-3 Alpha launches an AI video generator for paid users creating realistic 10-second clips. Nomic AI's GPT4All 3.0 offers an open-source desktop app supporting thousands of local models. AI assistants with multimodal capabilities and affordable access to multiple LLMs like ChatGPT, Claude, Llama, and Gemini are emerging. Meta 3D Gen advances text-to-3D asset generation, while Argil AI enables deepfake video creation from text threads. Research on transformer grokking and reasoning highlights advances in robust reasoning capabilities.
Gemini Nano: 50-90% of Gemini Pro, <100ms inference, on device, in Chrome Canary
gemini-nano gemini-pro claude-3.5-sonnet gpt-4o deepseek-coder-v2 glm-0520 nemotron-4-340b gpt-4-turbo-0409 google gemini huggingface anthropic deepseek zhipu-ai tsinghua nvidia model-quantization prompt-api optimization model-weights benchmarking code-generation math synthetic-data automatic-differentiation retrieval-augmented-generation mitigating-memorization tree-search inference-time-algorithms adcock_brett dair_ai lmsysorg
The latest Chrome Canary now includes a feature flag for Gemini Nano, offering a prompt API and on-device optimization guide, with models Nano 1 and 2 at 1.8B and 3.25B parameters respectively, showing decent performance relative to Gemini Pro. The base and instruct-tuned model weights have been extracted and posted to HuggingFace. In AI model releases, Anthropic launched Claude 3.5 Sonnet, which outperforms GPT-4o on some benchmarks, is twice as fast as Opus, and is free to try. DeepSeek-Coder-V2 achieves 90.2% on HumanEval and 75.7% on MATH, surpassing GPT-4-Turbo-0409, with models up to 236B parameters and 128K context length. GLM-0520 from Zhipu AI/Tsinghua ranks highly in coding and overall benchmarks. NVIDIA announced Nemotron-4 340B, an open model family for synthetic data generation. Research highlights include TextGrad, a framework for automatic differentiation on textual feedback; PlanRAG, an iterative plan-then-RAG decision-making technique; a paper on goldfish loss to mitigate memorization in LLMs; and a tree search algorithm for language model agents.
Clémentine Fourrier on LLM evals
claude-3-opus huggingface meta-ai-fair llm-evaluation automated-benchmarking human-evaluation model-bias data-contamination elo-ranking systematic-annotations preference-learning evaluation-metrics prompt-sensitivity clem_fourrier
Clémentine Fourrier from Huggingface presented at ICLR about GAIA with Meta and shared insights on LLM evaluation methods. The blog outlines three main evaluation approaches: Automated Benchmarking using sample inputs/outputs and metrics, Human Judges involving grading and ranking with methods like Vibe-checks, Arena, and systematic annotations, and Models as Judges using generalist or specialist models with noted biases. Challenges include data contamination, subjectivity, and bias in scoring. These evaluations help prevent regressions, rank models, and track progress in the field.
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.
FineWeb: 15T Tokens, 12 years of CommonCrawl (deduped and filtered, you're welcome)
llama-3-70b llama-3 wizardlm-2-8x22b claude-opus mistral-8x7b gpt-4 huggingface meta-ai-fair dbrx reka-ai mistral-ai lmsys openai datasets benchmarking quantization zero-shot-learning reasoning code-error-detection token-generation security
2024 has seen a significant increase in dataset sizes for training large language models, with Redpajama 2 offering up to 30T tokens, DBRX at 12T tokens, Reka Core/Flash/Edge with 5T tokens, and Llama 3 trained on 15T tokens. Huggingface released an open dataset containing 15T tokens from 12 years of filtered CommonCrawl data, enabling training of models like Llama 3 if compute resources are available. On Reddit, WizardLM-2-8x22b outperformed other open LLMs including Llama-3-70b-instruct in reasoning and math benchmarks. Claude Opus demonstrated strong zero-shot code error spotting, surpassing Llama 3. Benchmarks revealed limitations in the LMSYS chatbot leaderboard due to instruction-tuned models gaming the system, and a new RAG benchmark showed Llama 3 70B underperforming compared to GPT-4, while Mistral 8x7B remained strong. Efficient quantized versions of Llama 3 models are available on Huggingface, with users reporting token generation limits around 9600 tokens on a 3090 GPU. Safety concerns include a UK sex offender banned from AI tool usage and GPT-4 demonstrating an 87% success rate exploiting real vulnerabilities, raising security concerns.
Anime pfp anon eclipses $10k A::B prompting challenge
command-r-plus-104b stable-diffusion-1.5 openai ollama huggingface quantization model-optimization streaming prompt-engineering self-prompting image-composition character-lora-training model-size open-source-licenses memes humor victor-taelin futuristfrog
Victor Taelin issued a $10k challenge to GPT models, initially achieving only 10% success with state-of-the-art models, but community efforts surpassed 90% success within 48 hours, highlighting GPT capabilities and common skill gaps. In Reddit AI communities, Command R Plus (104B) is running quantized on M2 Max hardware via Ollama and llama.cpp forks, with GGUF quantizations released on Huggingface. Streaming text-to-video generation is now available through the st2v GitHub repo. WD Tagger v3 was released for mass auto-captioning datasets with a WebUI. Lesser-known prompting techniques like self-tagging and generational frameworks produced thought-provoking outputs in OpenAI discussions, including experiments with self-evolving system prompts. Stable Diffusion users discussed image composition importance for training character LoRAs and best checkpoints for video game character generation. Discussions also covered scarcity of 5B parameter models and open(ish) licenses for open source AI. Memes included jokes about ChatGPT and Gemini training data differences.
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.
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.
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.
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.
12/25/2023: Nous Hermes 2 Yi 34B for Christmas
nous-hermes-2 yi-34b nucleusx yayi-2 ferret teknim nous-research apple mixtral deepseek qwen huggingface wenge-technology quantization model-optimization throughput-metrics batch-processing parallel-decoding tensor-parallelization multimodality language-model-pretraining model-benchmarking teknium carsonpoole casper_ai pradeep1148 osanseviero metaldragon01
Teknium released Nous Hermes 2 on Yi 34B, positioning it as a top open model compared to Mixtral, DeepSeek, and Qwen. Apple introduced Ferret, a new open-source multimodal LLM. Discussions in the Nous Research AI Discord focused on AI model optimization and quantization techniques like AWQ, GPTQ, and AutoAWQ, with insights on proprietary optimization and throughput metrics. Additional highlights include the addition of NucleusX Model to transformers, a 30B model with 80 MMLU, and the YAYI 2 language model by Wenge Technology trained on 2.65 trillion tokens. "AutoAWQ outperforms vLLM up to batch size 8" was noted, and proprietary parallel decoding and tensor parallelization across GPUs were discussed for speed improvements.
12/15/2023: Mixtral-Instruct beats Gemini Pro (and matches GPT3.5)
mixtral gemini-pro gpt-3.5 gpt-4.5 gpt-4 chatgpt lmsys openai deepseek cloudflare huggingface performance context-window prompt-engineering privacy local-gpu cloud-gpu code-generation model-comparison model-usage api-errors karpathy
Thanks to a karpathy shoutout, lmsys now has enough data to rank mixtral and gemini pro. The discussion highlights the impressive performance of these state-of-the-art open-source models that can run on laptops. In the openai Discord, users compared AI tools like perplexity and chatgpt's browsing tool, favoring Perplexity for its superior data gathering, pricing, and usage limits. Interest was shown in AI's ability to convert large code files with deepseek coder recommended. Debates on privacy implications for AI advancement and challenges of running LLMs on local and cloud GPUs were prominent. Users reported issues with chatgpt including performance problems, loss of access to custom GPTs, and unauthorized access. Discussions also covered prompt engineering for large context windows and speculations about gpt-4.5 and gpt-4 future developments.
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.