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Company: "nous-research"
not much happened this weekend
jamba-1.5 dream-machine-1.5 ideogram-v2 mistral-nemo-minitron-8b mistral-7b llama-3-8b nous-research cursor-ai gdm george-hotz agibot unitree eth-zurich disney uc-san-diego ai21-labs luma-labs ideogram nvidia mistral-ai meta-ai-fair distributed-ai optimizer inter-gpu-communication low-latency-training open-source humanoid-robots robotics physics-based-motion teleoperation multilingual-models long-context text-to-video text-to-image model-performance george-hotz adcock_brett aman
Nous Research announced DisTrO, a new optimizer that drastically reduces inter-GPU communication by 1000x to 10,000x enabling efficient training on slow networks, offering an alternative to GDM's DiLoCo. Cursor AI gained viral attention from an 8-year-old user and announced a new fundraise, with co-host Aman returning to their podcast. George Hotz launched tinybox for sale. In robotics, AGIBOT revealed 5 new humanoid robots with open-source plans, and Unitree showcased its G1 humanoid robot nearing mass production at $16,000. ETH Zurich and Disney developed an AI system for physics-based robot motion generation from text or images. UC San Diego released ACE, an open-source teleoperation system for controlling multiple robots. AI21 Labs unveiled Jamba 1.5, a multilingual model with 256k context length and permissive licensing. Luma Labs released Dream Machine 1.5 for improved text-to-video generation. Ideogram launched v2 of its text-to-image model with near-perfect text generation. Nvidia and Mistral released Mistral-NeMo-Minitron 8B, a small model outperforming Mistral-7B and llama-3-8b on the Open LLM leaderboard.
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
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.
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.
ALL of AI Engineering in One Place
claude-3-sonnet claude-3 openai google-deepmind anthropic mistral-ai cohere hugging-face adept midjourney character-ai microsoft amazon nvidia salesforce mastercard palo-alto-networks axa novartis discord twilio tinder khan-academy sourcegraph mongodb neo4j hasura modular cognition anysphere perplexity-ai groq mozilla nous-research galileo unsloth langchain llamaindex instructor weights-biases lambda-labs neptune datastax crusoe covalent qdrant baseten e2b octo-ai gradient-ai lancedb log10 deepgram outlines crew-ai factory-ai interpretability feature-steering safety multilinguality multimodality rag evals-ops open-models code-generation gpus agents ai-leadership
The upcoming AI Engineer World's Fair in San Francisco from June 25-27 will feature a significantly expanded format with booths, talks, and workshops from top model labs like OpenAI, DeepMind, Anthropic, Mistral, Cohere, HuggingFace, and Character.ai. It includes participation from Microsoft Azure, Amazon AWS, Google Vertex, and major companies such as Nvidia, Salesforce, Mastercard, Palo Alto Networks, and more. The event covers 9 tracks including RAG, multimodality, evals/ops, open models, code generation, GPUs, agents, AI in Fortune 500, and a new AI leadership track. Additionally, Anthropic shared interpretability research on Claude 3 Sonnet, revealing millions of interpretable features that can be steered to modify model behavior, including safety-relevant features related to bias and unsafe content, though more research is needed for practical applications. The event offers a discount code for AI News readers.
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.
A quiet weekend
llama-3 dolphin-2.9 pixart-sigma llama-3-70b microsoft coca-cola uber lmsys nous-research mistral-ai ar-interfaces transformers algorithmic-tasks turing-test graph-algorithms embeddings generative-ai model-optimization llm-inference quantization model-deployment yann-lecun
Yann LeCun predicts a shift to AR interfaces with AI assistants in 10-15 years, moving away from smartphones. The Dolphin-2.9 model based on Llama-3 was released, improving quality issues. PixArt Sigma, a 0.6B parameter model, achieves Stable Diffusion 3.0 level performance with complete prompt adherence and local usability. Research shows transformers can use meaningless filler tokens for algorithmic tasks with dense supervision. AI-generated restaurant reviews can pass the Turing test, fooling humans and AI detectors. Uber uses graph algorithms and learned embeddings for ETA prediction. Coca-Cola and Microsoft announced a 5-year AI partnership to accelerate cloud and generative AI initiatives. The Llama-3 70B model can run on a single 4GB GPU using AirLLM optimization without quantization but is slow. Mistral.rs is introduced as a fast LLM inference platform with quantization and OpenAI API compatibility. Only 5% of LLMs make it from prototype to production due to challenges, especially in enterprise. EXL2 and GGUF quantization methods for Llama models show similar perplexity vs model size, with Llama-3 and Llama-2 degrading more under quantization compared to full precision.
World_sim.exe
gpt-4 gpt-4o grok-1 llama-cpp claude-3-opus claude-3 gpt-5 nvidia nous-research stability-ai hugging-face langchain anthropic openai multimodality foundation-models hardware-optimization model-quantization float4 float6 retrieval-augmented-generation text-to-video prompt-engineering long-form-rag gpu-optimization philosophy-of-ai agi-predictions jensen-huang yann-lecun sam-altman
NVIDIA announced Project GR00T, a foundation model for humanoid robot learning using multimodal instructions, built on their tech stack including Isaac Lab, OSMO, and Jetson Thor. They revealed the DGX Grace-Blackwell GB200 with over 1 exaflop compute, capable of training GPT-4 1.8T parameters in 90 days on 2000 Blackwells. Jensen Huang confirmed GPT-4 has 1.8 trillion parameters. The new GB200 GPU supports float4/6 precision with ~3 bits per parameter and achieves 40,000 TFLOPs on fp4 with 2x sparsity.
Open source highlights include the release of Grok-1, a 340B parameter model, and Stability AI's SV3D, an open-source text-to-video generation solution. Nous Research collaborated on implementing Steering Vectors in Llama.CPP.
In Retrieval Augmented Generation (RAG), a new 5.5-hour tutorial builds a pipeline using open-source HF models, and LangChain released a video on query routing and announced integration with NVIDIA NIM for GPU-optimized LLM inference.
Prominent opinions include Yann LeCun distinguishing language from other cognitive abilities, Sam Altman predicting AGI arrival in 6 years with a leap from GPT-4 to GPT-5 comparable to GPT-3 to GPT-4, and discussions on the philosophical status of LLMs like Claude. There is also advice against training models from scratch for most companies.
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.
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.
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. 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.
1/16/2024: ArtificialAnalysis - a new model/host benchmark site
mixtral hermes-2-mixtral openchat-7b byte-mistral nous-research nvidia hugging-face summarization fine-tuning byte-level-tokenization multimodality inference-speed-optimization dataset-sharing quantization swyx gabriel_syme manojbh carsonpoole fullstack6209
Artificial Analysis launched a new models and hosts comparison site, highlighted by swyx. Nous Research AI Discord discussed innovative summarization techniques using NVIDIA 3090 and 2080ti GPUs for processing around 100k tokens, and adapting prompts for smaller models like OpenChat 7B. The availability of Hermes 2 Mixtral on Huggingface's HuggingChat was noted, alongside fine-tuning challenges with Mixtral using Axolotl. Discussions included byte-level tokenization experiments with Byte Mistral, multimodal training on COCO image bytes, and inference speed improvements using vllm and llama.cpp. Calls for transparency in data sharing and open-sourcing the Hermes 2 Mixtral dataset were emphasized, with comparisons of dpo and sft methods and quantized LLM use on M1 MacBook Pro.
1/16/2024: TIES-Merging
mixtral-8x7b nous-hermes-2 frankendpo-4x7b-bf16 thebloke hugging-face nous-research togethercompute oak-ridge-national-laboratory vast-ai runpod mixture-of-experts random-gate-routing quantization gptq exl2-quants reinforcement-learning-from-human-feedback supercomputing trillion-parameter-models ghost-attention model-fine-tuning reward-models sanjiwatsuki superking__ mrdragonfox _dampf kaltcit rombodawg technotech
TheBloke's Discord community actively discusses Mixture of Experts (MoE) models, focusing on random gate routing layers for training and the challenges of immediate model use. There is a robust debate on quantization methods, comparing GPTQ and EXL2 quants, with EXL2 noted for faster execution on specialized hardware. A new model, Nous Hermes 2, based on Mixtral 8x7B and trained with RLHF, claims benchmark superiority but shows some inconsistencies. The Frontier supercomputer at Oak Ridge National Laboratory is highlighted for training a trillion-parameter LLM with 14TB RAM, sparking discussions on open-sourcing government-funded AI research. Additionally, the application of ghost attention in the academicat model is explored, with mixed reactions from the community. "Random gate layer is good for training but not for immediate use," and "EXL2 might offer faster execution on specialized hardware," are key insights shared.
1/12/2024: Anthropic coins Sleeper Agents
nous-mixtral 120b anthropic openai nous-research hugging-face reinforcement-learning fine-tuning backdoors model-security adversarial-training chain-of-thought model-merging dataset-release security-vs-convenience leo-gao andrej-karpathy
Anthropic released a new paper exploring the persistence of deceptive alignment and backdoors in models through stages of training including supervised fine-tuning and reinforcement learning safety training. The study found that safety training and adversarial training did not eliminate backdoors, which can cause models to write insecure code or exhibit hidden behaviors triggered by specific prompts. Notable AI figures like leo gao and andrej-karpathy praised the work, highlighting its implications for future model security and the risks of sleeper agent LLMs. Additionally, the Nous Research AI Discord community discussed topics such as the trade-off between security and convenience, the Hulk Dataset 0.1 for LLM fine-tuning, curiosity about a 120B model and Nous Mixtral, debates on LLM leaderboard legitimacy, and the rise of Frankenmerge techniques for model merging and capacity enhancement.
1/11/2024: Mixing Experts vs Merging Models
gpt-4-turbo gpt-4-0613 mixtral deepseekmoe phixtral deepseek-ai hugging-face nous-research teenage-engineering discord mixture-of-experts model-merging fine-tuning rag security discord-tos model-performance prompt-engineering function-calling semantic-analysis data-frameworks ash_prabaker shacrw teknium 0xevil everyoneisgross ldj pramod8481 mgreg_42266 georgejrjrjr kenakafrosty
18 guilds, 277 channels, and 1342 messages were analyzed with an estimated reading time saved of 187 minutes. The community switched to GPT-4 turbo and discussed the rise of Mixture of Experts (MoE) models like Mixtral, DeepSeekMOE, and Phixtral. Model merging techniques, including naive linear interpolation and "frankenmerges" by SOLAR and Goliath, are driving new performance gains on open leaderboards. Discussions in the Nous Research AI Discord covered topics such as AI playgrounds supporting prompt and RAG parameters, security concerns about third-party cloud usage, debates on Discord bots and TOS, skepticism about Teenage Engineering's cloud LLM, and performance differences between GPT-4 0613 and GPT-4 turbo. The community also explored fine-tuning strategies involving DPO, LoRA, and safetensors, integration of RAG with API calls, semantic differences between MoE and dense LLMs, and data frameworks like llama index and SciPhi-AI's synthesizer. Issues with anomalous characters in fine-tuning were also raised.
1/9/2024: 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.
1/4/2024: Jeff Bezos backs Perplexity's $520m Series B.
wizardcoder-33b-v1.1 mobilellama-1.4b-base shearedllama tinyllama mixtral-8x7b perplexity anthropic google nous-research mistral-ai hugging-face document-recall rnn-memory synthetic-data benchmarking multi-gpu-support context-length model-architecture sliding-window-attention model-parallelism gpu-optimization jeff-bezos
Perplexity announced their Series B funding round with notable investor Jeff Bezos, who previously invested in Google 25 years ago. Anthropic is raising $750 million, projecting at least $850 million in annualized revenue next year and implementing "brutal" changes to their Terms of Service. Discussions in Nous Research AI Discord cover topics such as document recall limits from gigabytes of data, RNN memory and compute trade-offs, synthetic datasets, and benchmarking of models like WizardCoder-33B-V1.1, MobileLLaMA-1.4B-Base, ShearedLLaMA, and TinyLLaMA. Other highlights include UnsLOTH optimizations for multi-GPU systems, AI rap voice models, context-extending code, and architectural innovations like applying Detectron/ViT backbones to LLMs, sliding window attention in Mistral, and parallelizing Mixtral 8x7b with FSDP and HF Accelerate.
12/30/2023: Mega List of all LLMs
deita-v1.0 mixtral amazon-titan-text-express amazon-titan-text-lite nous-research hugging-face amazon mistral-ai local-attention computational-complexity benchmarking model-merging graded-modal-types function-calling data-contamination training-methods stella-biderman euclaise joey00072
Stella Biderman's tracking list of LLMs is highlighted, with resources shared for browsing. The Nous Research AI Discord discussed the Local Attention Flax module focusing on computational complexity, debating linear vs quadratic complexity and proposing chunking as a solution. Benchmark logs for various LLMs including Deita v1.0 with its SFT+DPO training method were shared. Discussions covered model merging, graded modal types, function calling in AI models, and data contamination issues in Mixtral. Community insights were sought on Amazon Titan Text Express and Amazon Titan Text Lite LLMs, including a unique training strategy involving bad datasets. Several GitHub repositories and projects like DRUGS, MathPile, CL-FoMo, and SplaTAM were referenced for performance and data quality evaluations.
12/28/2023: Smol Talk updates
tinyllama-1.1b mixtral tinygpt-v nous-research tyrannosaurus latex benchmarking knowledge-graphs model-finetuning tokenization decentralized-computation philosophy-of-ai multimodality vision open-source-models gary-marcus
Nous Research AI Discord discussions covered topics such as AI placement charts, ChatGPT's issues with Latex math format compatibility with Obsidian, and performance metrics of the TinyLlama 1.1B model on various benchmarks. Users shared resources including the math-centric corpus MathPile, knowledge graph building methods, and open-source large language model repositories. Technical discussions included decentralized computation feasibility for models like Mixtral, philosophical debates on AI sentience, and strategies for model finetuning and token counting. The community also discussed the Obsidian model, vision model training, and the release of the multimodal TinyGPT-V model by Tyrannosaurus. "ChatGPT not generating Latex math format compatible with Obsidian" and "optimistic about human-level AI within our lifetime" were notable quotes.
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.
12/23/2023: NeurIPS Best Papers of 2023
gpt-4 palm2 hermes-2.5 mistral-7b nous-research hugging-face apple context-length malware-security video-content music-content linear-layers api-access large-language-models embedding vector-databases model-merging model-interpretability striped-hyena-architecture quantization rmsnorm attention-mechanisms
The Latent Space Pod released a 3-hour recap of the best NeurIPS 2023 papers. The Nous Research AI Discord community discussed optimizing AI performance with shorter context lengths, malware security concerns linked to HuggingFace, and shared insights on video and music content. Technical discussions included the DYAD research paper proposing a faster alternative to linear layers, Apple's ML Ferret machine learning tool, and accessing PALM2 via API. The community also explored Large Language Models focusing on specialized models, data scaling, embedding/vector databases, model merging, and interpretability, with mentions of Hermes 2.5, GPT-4, and Mistral. Additionally, there were conversations on the Striped Hyena Architecture, quantization challenges, and fixes related to RMSNorm and the "Attention is All You Need" paper.
12/20/2023: Project Obsidian - Multimodal Mistral 7B from Nous
gpt-4 gpt-3.5 dall-e-3 nous-research teknim openai multimodality image-detection security-api bias facial-recognition healthcare-ai gpu-optimization prompt-engineering vision
Project Obsidian is a multimodal model being trained publicly, tracked by Teknium on the Nous Discord. Discussions include 4M: Massively Multimodal Masked Modeling and Reason.dev, a TypeScript framework for LLM applications. The OpenAI Discord community discussed hardware specs for running TensorFlow JS for image detection, security API ideas for filtering inappropriate images, and concerns about racial and cultural bias in AI, especially in facial recognition and healthcare. Challenges with GPT-3.5 and GPT-4 in word puzzle games were noted, along with GPU recommendations prioritizing VRAM for AI inference. Users also debated GPT-4's vision capabilities, limitations of DALL·E 3, platform access issues, and prompting strategies for better outputs.
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.
12/10/2023: 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.