> AI News for 3/2/2024-3/4/2024. We checked [**356** Twitters](https://twitter.com/i/lists/1585430245762441216) and **22** Discords (**352** channels, and **9688** messages) for you. Estimated reading time saved (at 200wpm): **984 minutes**.

Claude 3 is here! Nothing else from the weekend matters in comparison, which is awfully nice for weekday newsletter writers.

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TLDR:

  • Claude now comes in 3 sizes - the smallest two (Haiku - unreleased, Sonnet - default, on claude.ai, AWS and GCP) are fast (2x faster than Claude 2) and cheap (half the cost of GPT4T) and good, and the big one (Opus, on Claude Pro, but slower and more expensive) appears to beat GPT4 on every benchmark that matters. Sometimes, like in GPQA, a LOT better, impressing the GPQA benchmark author.
  • They’re all multimodal, specifically vision and convincingly turned a 2hr Karpathy video into a blogpost.
  • Better alignment - fewer bad refusals, and improved accuracy on hard questions
  • 200k token context, can extend up to 1m tokens, with Gemini 1.5-like perfect recal;

Our full notes below:

As a bonus, Noah did 2 runs of Claude 3 (Sonnet) vs GPT4 on the same Twitter data scrapes you see below. We think Claude 3’s summarization capabilities are way, way better.

Table of Contents

[TOC]

PART X: AI Twitter

Compare Claude 3 vs GPT4T

AI Progress and Capabilities

AI Investments and Business

  • Softbank sold all its Nvidia shares in 2019 for $3.6B, which would be worth $93B today. Investing in AI was one of the primary goals of Softbank’s Vision Fund.
  • Nvidia’s early years involved relentlessly improving despite competitors having advantages. Their differentiator was taking software more seriously, building the CUDA ecosystem.
  • Google faces a problem with the likes of OpenAI and Perplexity showing that many “search” tasks are better served through conversational AI, similar to how Google disrupted with PageRank and links 25 years ago.
  • Compute and data are the currency of the future according to Alexandr Wang.

AI Safety and Regulation

Memes and Humor

Other Relevant Tweets for AI Engineers

PART 0: Summary of Summaries of Summaries

This is now also driven by Claude 3, which is way better than OpenAI’s output.

Got it, here's the summary in bullet point markdown format:

AI Model Performance and Comparisons

  • The release of Claude 3 by Anthropic sparked extensive discussions and benchmarking comparisons against GPT-4 across multiple Discord servers, with users claiming superior performance on tasks like math and coding. Claude 3's ~60% accuracy on GPQA was highlighted.
  • Debates arose around the Mistral Large model's performance versus GPT-4 for coding tasks, with some claiming its superiority despite official benchmarks.
  • The Mamba LM chess model with 11M parameters showed promising results, achieving a 37.7% win rate against Stockfish level 0 as white.

AI Engineering and Deployment Challenges

  • Extensive discussions revolved around the difficulties of deploying large language models (LLMs) like Mistral, with specific focus on VRAM requirements, quantization strategies, and optimal configurations for setups like dual NVIDIA 3090 GPUs.
  • CUDA and GPU optimization were recurring topics, with resources like NVIDIA's cuBLASDx documentation and a lecture on CUDA performance gotchas being shared.
  • The Terminator architecture was introduced, proposing to replace residual learning with a novel approach to full context interaction.

AI Ethics, Privacy, and Regulations

  • Concerns were raised about potential data scraping from personal profiles after an AI model's response contained identifiable personal details, prompting discussions on ethics and legality.
  • India's AI deployment regulations requiring government approval sparked alarms over potential stifling of innovation, as per Martin Casado's tweet.
  • The Open Source Initiative is working on a new draft for an open-source AI definition, with the evolving drafts available here.

Cutting-Edge AI Research and Techniques

  • Aristotelian Rescoring, a concept that could address complex AI challenges, was discussed, with related works like STORIUM, FairytaleQA, and TellMeWhy available on GitHub and Hugging Face.
  • The novel HyperZ⋅Z⋅W Operator was introduced as part of the #Terminator network, blending classic and modern technologies, with the full research available here.
  • RAPTOR, a new technique for Retrieval-Augmented Generation (RAG) introduced by LlamaIndex, aims to improve higher-level context retrieval, as announced on Twitter.

PART 1: High level Discord summaries

TheBloke Discord Summary

  • AI Sensitivity on the Rise: Claude 3 AI’s latest version has heightened sensitivity to potential offensive content and copyright issues, raising questions about safety or over-cautiousness. The mention of Claude 3 was associated with Google-backed Anthropic debuting its most powerful chatbot yet.

  • CUDA Conundrum: There’s concern within the community about NVIDIA’s new licensing terms that restrict the use of CUDA on non-NVIDIA hardware, particularly impacting translation layers. This discussion revolves around the recent update that Nvidia bans the use of translation layers.

  • Stuck in the Game: Skepticism prevails over AI’s near-future role in game development as current AI limitations may not be easily surpassed by brute-forcing with more compute power.

  • Fine-Tuning Frustration: An issue with fine-tuning is reported, specifically with an OpenOrca Mistral 7b model that gives incorrect outputs post gguf conversion. The reported issue can be traced across multiple channels, indicating a broader interest in the problem and potential solutions, with suggestions like checking pre-quantization performance and considering the use of imatrix for outliers.

  • Chess Model Checkmate Performance: Success is seen in the training of a smaller parameter Mamba LM chess model with an 11M parameter count, performing better as white with a 37.7% win rate against Stockfish level 0. Model available at HaileyStorm/MambaMate-Micro · Hugging Face.

  • Code-Capable AI Harnesses New Heights: User @ajibawa_2023 presents their fine-tuned models, notably the OpenHermes-2.5-Code-290k-13B, which demonstrates proficient coding capabilities and the potential for applications in diverse tasks including blogging and story generation.

OpenAI Discord Summary

  • AI Community Finds Alternatives During GPT Outage: Users discussed alternatives to GPT during a recent service downtime, mentioning Bing Chat, Hugginface, Deft GPT, LeChat, together.xyz, and Stable Chat. Anthropic’s Claude 3 was highlighted as a particularly impressive alternative, with one user mentioning experimenting with the free Sonnet model, while others debated the capabilities and cost considerations of AI models like Claude 3 and OpenAI’s offerings.

  • Custom Agents and Optimal Code Generation: Questions arose about whether custom agents could integrate CSV files into their knowledge bases, prompting a technical discussion on file types. User @yuyu1337 explored finding an optimal GPT model for code generation, sparking a conversation about achieving the best time/space complexity and suggestions for using pseudo code.

  • Vision and Humor APIs Puzzle Engineers: Participants grappled with applying humor in their prompts with varying success between ChatGPT and the GPT 3.5 API. The Discord community was also engrossed in a “owl and palm” brain teaser, trying to solve the puzzle using GPT-V with multiple prompting strategies, yet encountering obstacles due to the model’s limitations in interpreting measurements.

  • Community Laughs at and Laments Over Usage Limits: Amid playful banter about AI limitations and usage limits, users exchanged prompt engineering techniques with mixed results. Concerns were raised over server auto-moderation impacting the ability to discuss and share advanced prompts, stirring a call for OpenAI to reconsider prompt restrictions for more effective knowledge sharing.

  • AI Enthusiasts Offer Tips and Seek Training Advice: Newcomers and experienced users alike asked for and provided advice on prompt engineering, discussing the importance of template structuring and the utilization of AI for content creation tasks while adhering to OpenAI’s policies. Discussions highlighted the importance of community and knowledge exchange in the evolving landscape of AI engineering and usage.

Perplexity AI Discord Summary

  • Subscription Snafu Sparks Outrage: @vivekrgopal reported being charged for an annual Perplexity subscription despite attempting cancellation during the trial, seeking a refund through direct messages.

  • AI Integration Fever Rises: Users such as @bioforever and @sebastyan5218 are keenly awaiting the integration of new language models like Claude 3 and Gemini Ultra into Perplexity, signaling a high demand for cutting-edge AI features.

  • Benchmark Bafflements with AI Responses: @dailyfocus_daily delved into the inconsistencies across AI model problem-solving by comparing responses from GPT-4, Claude 3, and others to a benchmark question about sorting labeled balls into boxes.

  • IAM Insights and AI Fundamentals: Users like @riverborse and @krishna08011 shared Perplexity links focusing on insights into identity access management, and the basics of AI, useful for technical professionals looking to deepen their understanding of key concepts.

  • API Discussions Unfold with Concerns and Anticipations: Users discussed the limits of Perplexity API, including time-sensitive query issues and missing YouTube summary features; they also anticipated new features like citation access. A discussion on temperature settings revisited how they affect the naturalness and reliability of language outputs, and a link to assist with API usage was shared by @icelavaman, Perplexity API Info.

Mistral Discord Summary

Hermes 2.5 Takes the Lead: Discussions in the guild revealed that Hermes 2.5 has unexpectedly outperformed Hermes 2 in various benchmarks, with specific reference to the MMLU benchmark performance - a significant point for those considering upgrades or new deployments.

Mistral Deployment and Configuration Insights: Engineers seeking optimal configurations for Mistral deployment gathered valuable advice, with best practices discussed for a dual NVIDIA 3090 setup, VRAM requirements for fp16 precision (~90GB), and quantization strategies. Curious eyes were also pointed towards “thebloke“‘s Discord for additional community support.

Benchmarks Resonating With Personal Experience: A significant number of posts revolved around performance benchmarks and personal experiences with different models. Particularly intriguing was the reported superiority of Mistral Large over GPT-4 for coding tasks, challenging official tests and signaling the need for user-specific benchmarks.

Discussions Hover Around Model Limitations: Technical dialogues converged on the inherent limitations of models such as Mistral and Mixtral, specifically discussing the context size constraints with a 32k token limit for Mistral-7B-Instruct-v0.2, and sliding window functionality issues leading to possible performance degradation.

Fine Tuning and Usage Nuances Explored: Users shared insights on successfully leveraging models for specific tasks, such as sentiment analysis and scientific reasoning. However, concerns about Mixtral’s training implementation and requests for a minimalistic guide suggest a demand for clearer documentation within the community.

Emerging AI Tools and Competitive Landscape: Enthusiasts and practitioners alike have turned their attention to emerging AI tools, including Kubernetes AI tooling and Anthropic’s release of Claude-3, sparking discussions on competitive offerings and the importance of open weights for AI models.

Nous Research AI Discord Summary

  • Phi-2’s Token Limit Hits a Roadblock: Users discussed the limits of the Phi-2 model regarding token expansion, with a suggestion that it might behave like a default transformer beyond its configured limit of 2,048 tokens. Caution was advised when altering Phi-2’s settings to avoid erratic performance. A link to Phi-2’s configuration file was provided here.

  • Mac Setup for Engineers: Community members exchanged a flurry of suggestions for setting up a new Mac, mentioning tools like Homebrew, TG Pro for temperature monitoring, and Time Machine for backups. A YouTube tutorial on Mac setup for Python/ML was highlighted, available here.

  • Scaling AI Model Debate Rages On: There was a heated debate about the benefits of scaling up AI models. Some users argued that post-500B-1T parameters, efficiency gains are more likely from training techniques than sheer size, citing articles critical of the scaling approach. The contention touched upon the practicality of training 100T parameter models and the potential of smaller models, with one side expressing skepticism and another suggesting a sufficient data threshold like Redpajama v2 could still push scaling benefits. Cost-effectiveness and recent comparisons of AI models were also topics of interest.

  • Claude 3 Piques Interest: In the general discussion, Claude 3 captured attention with its potential performance against GPT-4. There was interest in inference platforms for function calling models, and advice exchanged on B2B software sales strategies. Additionally, approaches to building knowledge graphs were discussed, with anticipation for a new model to enhance structured data extraction.

  • Diverse Queries on LLMs Addressed: Questions flew around topics like PPO script availability for LLMs, best platforms for model inference, 1-shot training in ChatML, and fine-tuning AI for customer interactions. A warning against possible model manipulation was shared, along with a Business Insider article for context here.

  • Praise for Moondream In Project Obsidian: Moondream, a tiny vision language model, received praise for its performance in preliminary testing, with a GitHub link provided for those interested in exploring it here.

Eleuther Discord Summary

  • Open Source AI Nears Milestone: The Open Source Initiative (OSI) is working on a new draft for an open-source AI definition with a monthly release cadence, aiming for a version 1.0 by October 2024, as discussions continue in their public forum. The evolving drafts can be reviewed here.

  • EFF’s Legal Stance on DMCA: The Electronic Frontier Foundation (EFF) has initiated a legal challenge, Green v. Department of Justice, against the DMCA’s anti-circumvention provisions, claiming they impede access to legally purchased copyrighted content. Details of the case are documented here.

  • Quantization in AI Comes Under Scrutiny: Debates have risen around quantization in neural networks, especially regarding weights and activations. Researchers have discussed papers like the ‘bitlinear paper’ and the quantization of activation functions, touching upon the concept of epistemic uncertainty.

  • Safety Alert: Compromised Code via GitHub Malware: A malware campaign on GitHub has cloned legitimate repositories to distribute malware. A detailed threat analysis by Apiiro is available here.

  • Challenging Predictive Modeling in Biology: A user claimed that predictive modeling cannot effectively create economically viable biomolecules due to the complexity of biological systems, indicating a contrast with the more predictable physical models used in engineering.

  • Revolutionizing AI with Counterfactuals: A new approach named CounterCurate, combining GPT-4V and DALLE-3, leads to visio-linguistic improvements. CounterCurate uses counterfactual image-caption pairs to boost performance on benchmarks. The paper explaining this is available here.

  • LLMs Overhyped? Functional Benchmarks Suggest So: Discussions arose from a Twitter thread questioning over-reported reasoning capabilities of LLMs, referring to functional benchmarks indicating significant reasoning gaps, available here, with an accompanying GitHub repository.

  • Terminator Architecture Could Replace Residual Learning: The Terminator network architecture could replace residual learning with its new approach to full context interaction. An arXiv paper discusses its potential. Future applications and code release were hinted by community members.

  • AzureML Integration with lm-eval-harness: AzureML users discussed issues and solutions regarding the setup of lm-eval-harness. The talk included dependency, CUDA detection, multi-GPU use, and orchestration across nodes, with insights found here and here.

  • Mamba vs Transformer: A comparison was drawn between Mamba and Transformer models in terms of their ability to learn and generalize on the PARITY task. Concerns over LSTM, Mamba performance, and the mechanism models used to learn PARITY were voiced, along with a shared GitHub script for training Mamba.

  • Advancing Dataset Development: A GitHub repository containing scripts for development of The Pile dataset was shared, particularly useful for those working on training language models. The repository and its README can be accessed here.

  • Figma Meets Imageio in Creative Animation: An innovative workflow was mentioned where animation was achieved by manipulating SVG frames created in Figma into a GIF using imageio.

LM Studio Discord Summary

  • Switch the Bot When Models Misbehave: Users faced issues with the Codellama Python 7B model within LM Studio, and @heyitsyorkie suggested switching to the Magicoder-S-DS-6.7B-GGUF model on Hugging Face to fix a “broken quant” problem. Discussions about model support, such as for LoRAs and QLoRA, indicated they are not yet available, and users cannot upload pdf files directly to LM Studio.

  • Data Privacy Alarm Bells Ringing: Concerns were aired about potential data scraping from personal profiles after an unexpected model response contained identifiable personal details, leading to discussions on the ethics and legality of such practices in training AIs.

  • VRAM: A Hot Topic Among Hardware Geeks: Several threads touched on the necessity of substantial VRAM, with recommendations for a GPU having at least 24 GB for running large language models efficiently. The discussions pointed to resources like Debian on Apple M1, emphasizing the limitations and potential challenges with Apple’s unified memory architecture and using Apple M1 Macs for AI work with Linux.

  • Impending Beta Release Buzz: @heyitsyorkie indicated an upcoming beta release of LM Studio would include integration of Starcoder2-15b. This discussion was backed by a GitHub pull request adding support for it to llama.cpp.

  • The Trials and Errors of Autogen: Users experienced issues with Autogen integration, such as a 401 error and slow model loading times in LM Studio. Suggestions for troubleshooting included reinstalling and using the Docker guide with adjustments for Windows system paths as found on StackOverflow.

  • AI Engineering with Distributed LLMs: A query was raised regarding the development of custom AI agents and running different large language models on various hardware setups, including mentioning specific hardware such as a 3090 GPU, a Jetson Orion, and a 6800XT GPU. However, there was no additional context or detailed discussion provided on these topics.

  • Short Communications: A user confirmed the existence of a package on Arch Linux using yay, and another inquired about Linux support for a feature without additional context.

  • Need for Clarity in AI Discussions: Comments indicated a lack of context and clarity in discussions regarding JavaScript compatibility with crew ai, as well as a mention of Visual Studio Code that required further information.

HuggingFace Discord Summary

  • Model Training Hunger Games: Engineers joked about the hunger of AI models during training, devouring 90GB of memory. Better check those Gradio components for deployment, as outdated versions like ImageEditor might haunt your dreams.

  • AI Learning Ladder: From newbie to pro, members are eager to climb the AI learning curve, sharing resources for CUDA, SASS in Gradio, and PPO theory – no safety ropes attached.

  • Chat Conference Calling: AI community events like conferences in Asheville, NC are the real-world meeting grounds for GenAI aficionados. Meanwhile, collaborations emerge for tasks like TTS and crafting book club questions – who said AI doesn’t have hobbies?

  • Discord Doubles Down on Diffusers: diffusers scheduler naming issues had everyone double-checking their classes post-update until a pull request fix was merged. Inpainting discussions were illustrated, and LoRA adapter implementation advice was dispensed like candy on Halloween.

  • Edgy Bots and Data Detectives: Creative engineers unleashed bots like DeepSeek Coder 33b and V0.4 Proteus on the Poe platform. Others shared breakthroughs in protein anomaly detection and musings on the intersection of AI and music sampling, hinting at an era where AI could be the DJ at your next party.

  • Scheduler Confusion Resolution in Diffusers: A GitHub issue with incorrect scheduler class names in diffusers was resolved by a pull request, improving accuracy for AI engineers needing the right tools without the confusion.

  • NLP Model Deployment Drama: Flask vs. Triton is not an apples-to-apples comparison when deploying NLP models – pick your battle. And if you’re on the hunt for efficiency, Adam optimizer still wears the crown in some circles, but keep an eye on the competition.

  • Building Bridges to Computer Vision: The connection between a georeferenced PDF of a civil drawing and GIS CAD is being explored, while curious minds considered the potential of small Visual Language Models for tasks like client onboarding. Glimpses into the synergy of AI and vision are ever-expanding, just beyond the visible spectrum.

LAION Discord Summary

  • HyperZ⋅Z⋅W Operator Shaking Foundations: @alex_cool6 introduced the #Terminator network, blending classic and modern technologies and utilizing the novel HyperZ⋅Z⋅W Operator, with the full research available here.

  • Claude 3 Attracts Attention: Discussions around the Claude 3 Model are heating up, with its performance benchmarks stirring the community. A Reddit thread showcases the community’s investigation into its capabilities.

  • Claude 3 Outperforms GPT-4: @segmentationfault8268 found Claude 3 to outdo GPT-4 in dynamic response and understanding, potentially stealing users away from their existing ChatGPT Plus subscriptions.

  • CUDA Kernel Challenges Persist with Claude 3: Despite its advancements, Claude 3 seems to lack improvement in non-standard tasks like handling PyTorch CUDA kernels, as pointed out by @twoabove.

  • Sonnet Enters VLM Arena: The conversation has ignited interest in Sonnet, identified as a Visual Language Model (VLM), and its comparative performance with giants like GPT4v and CogVLM.

  • Seeking Aid for DPO Adjustment: @huunguyen made a call for collaboration to refine the Dynamic Programming Optimizer (DPO). Interested collaborators are encouraged to connect via direct message.

CUDA MODE Discord Summary

Swap Space on Speed Dial: Discussion centered on using Linux VRAM as swap space with potential speed advantages over traditional disk paging, although possible demand conflicts were noted. Resources like vramfs on GitHub and ArchLinux documentation were shared.

Rapid Verification and Chat Retrievals: Users sought assistance on accessing previous day’s live chat discussions and queried about Gmail verification times on lightning.ai, highlighting quick resolution times and the ease of accessing recorded sessions.

CUDA Conundrums and Triton Tweaks: Engineers shared insights into CUDA programming difficulties, examining Triton’s relationship to NVCC and asynchronous matrix multiplication in Hopper architecture. Resources such as the unsloth repository and the Triton GitHub page were highlighted.

GPU-Powered Databases: The idea of running databases on GPUs gained traction, with mentions of the cuDF library and reference to a ZDNet article on GPU databases.

Mistral’s Computation Contemplations: Debates arose over Mistral’s computing capabilities, questioning the adequacy of 1.5k H100 GPUs for large-scale model training and discussing asynchronous operations. Links included NVIDIA’s cuBLASDx documentation and a tweet from Arthur Mensch.

PyTorch Developer Podcast Drops New Episode: The podcast’s episode discussing AoTInductor was shared, echoing community enthusiasm for the series.

Ring Attention Rings a Bell: Ring and Striped Attention were hot topics, with references to discussions on the YK Discord and a Together.ai blog post. Various code bases like ring-flash-attention and flash-attention provided implementation insights.

CUDA-MODE Lecture Loaded: Announcement of Lecture 8 on CUDA performance gotchas with a promise of tricks for maximizing occupancy and minimizing issues, set to start promptly for eager learners.

Career Cornerstones: Job postings by Lamini AI and Quadrature aimed at HPC and GPU Optimization Engineers, highlighting opportunities to work on exciting projects such as optimizing LLMs on AMD GPUs and AI workloads in global financial markets. Details can be found on Lamini AI Careers and Quadrature Careers.

Lecture 8 Redux on YouTube: After technical issues with a prior recording, Lecture 8, titled CUDA Performance Checklist, was re-recorded and shared along with corresponding code samples and slides.

LlamaIndex Discord Summary

  • RAPTOR Elevates RAG Retrieval: LlamaIndex introduced RAPTOR, a new technique for Retrieval-Augmented Generation (RAG) that improves the retrieval of higher-level context. Promoting better handling of complex questions, it was announced via Twitter.

  • GAI Enters Urban Planning: LlamaIndex displayed practical applications of RAG, including a GAI-powered ADU planner, aiming to enhance the process of constructing accessory dwelling units Tweet.

  • MongoDB Meets RAG with LlamaIndex’s new reference architecture, developed by @AlakeRichmond, utilizing @MongoDB Atlas for efficient data indexing, vital for building sophisticated RAG systems, as per a Twitter update.

  • Semantic Strategies Sharpen RAG: Semantic chunking is spotlighted for its potential to advance RAG’s retrieval and synthesis capabilities by grouping semantically similar data, an approach shared by Florian June and picked up by LlamaIndex Twitter post.

  • Claude 3’s Triumphant Trio: Claude 3 has been released with different variants, including Claude Opus, surpassing GPT-4’s performance according to LlamaIndex, which has announced immediate support for the model announcement.

  • Leveraging LongContext with LlamaIndex: Integration of LlamaIndex with LongContext shows promise for enhancing RAG, especially with Google’s recent Gemini 1.5 Pro release that features a 1M context window, which could potentially be incorporated Medium article.

  • Community Corner Catches Fire: The LlamaIndex Discord community was complimented as more organized and supportive than others, particularly in terms of API documentation structure insights and practical guides on setting up sophisticated search systems involving hybrid vector and keyword searching Y Combinator news, LlamaIndex Documentation, and multiple other resources listed above.

OpenRouter (Alex Atallah) Discord Summary

  • Claude 3.0 Arrival on OpenRouter: The much-anticipated Claude 3 AI has been released, with an exclusive mention of an experimental self-moderated version being available on OpenRouter, as announced by @alexatallah.

  • LLM Security Game Sparks Caution: @leumon has launched a game on a server attempting to deceive GPT3.5 into exposing a secret key, underlining the significance of handling AI outputs with caution and safeguarding sensitive data. Players can also engage freely with various AI models like Claude-v1, Gemini Pro, Mixtral, Dolphin, and Yi.

  • Claude 3 vs GPT-4 Reactions and Tests: Discussions and reactions to the comparison between Claude 3, including Claude 3 Opus, and GPT-4 are ongoing, with users like @arsoban noting greater text comprehension in Claude 3 Opus in tests, while others express concerns over its pricing.

  • Performance Debate Heats Up Among AIs: The capabilities of different Claude 3 variants spurred debates, with shared observations such as Sonnet sometimes outperforming Opus and plans to test Claude 3 for English-to-code translations in gaming applications.

  • AI Deterioration Detected by Community: @capitaindave pointed out what seems to be a diminishing reasoning ability over time in Gemini Ultra, sparking discussions on the potential deterioration of model performance after initial release.

Links mentioned:

LLM Perf Enthusiasts AI Discord Summary

  • OpenAI Turns a New Page with Browsing Feature: OpenAI has unveiled a browsing feature, prompting excitement for its resemblence to existing tools like Gemini/Perplexity. The announcement was shared via a tweet from OpenAI.

  • Claude 3’s Promising Debut: The new Claude 3 model family is creating buzz for potentially surpassing GPT-4 in tasks involving math and code based on user @res6969’s claims. Discussions concerning its cost-efficiency and the anticipation for the Haiku model highlight user interest in balancing price with performance.

  • Claude 3’s Operative Edge: Experiments referred to by @res6969 point to Claude 3’s latency outperforming others, with first token responses around 4 seconds, demonstrating its operational efficiency in user experiences.

  • Navigating Cost-Effective Embedding Solutions: With a goal of 100 inferences per second in production, @iyevenko explored the most cost-effective embedding models. User @yikesawjeez’s recommendations included Qdrant and Weaviate.

  • Weighing OpenAI’s Embedding Affordability: Despite initial quality concerns, @iyevenko is considering OpenAI’s embedding solutions for cloud infrastructure, which appear to be quite cost-effective, especially in light of improvements to their embeddings.

Interconnects (Nathan Lambert) Discord Summary

  • Anthropic Unveils Claude 3 to Rave Reviews: AnthropicAI announced Claude 3, its latest series of AI models including Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku, challenging benchmarks in AI performance. Users like @sid221134224 and @canadagoose1 expressed their excitement, noting Claude 3’s strengths over GPT-4 and its potential due to no reliance on proprietary data sets.

  • Claude 3 Ignites Misinformation and Drama: The release of Claude 3 catalyzed the spread of problematic tweets, causing @natolambert to intervene directly by addressing misleading posts as “dumb.” @natolambert also humorously rejected the idea of using an alternate account to combat misinformation due to the effort involved.

  • RL Innovations and Discussions: A paper on a foundational model for RL was highlighted, discussing a policy conditioned on embedding of the reward function for adaptable generalization (Sergey Levine’s tweet). Concurrently, the community explored the Cohere PPO paper’s claim that corrections for Policy Gradient Optimization (PPO) may not be required for Large Language Models (LLMs), sparking interest in verification from other research groups.

  • From Silver Screen to AI Dreams: @natolambert is seeking a video editing partner to create a trailer, possibly inspired by the film Her, emphasizing AI themes. Additionally, @natolambert teased upcoming content and mentioned possible collaboration with Hugging Face’s CTO, linking to a discussion about the benefits of open source AI (Logan.GPT’s tweet).

  • The AI Community Embraces Julia: Amid discussions, @xeophon. focused on the merits of the Julia programming language for AI development, providing a link to JuliaLang for those interested. The conversation indicated a growing engagement with Julia within the engineering community.

LangChain AI Discord Summary

  • Deciphering Tokenizer Mechanics: A YouTube tutorial was shared by @lhc1921, offering insights into constructing a tokenizer for Large Language Models (LLMs), highlighting its significance in converting strings to tokens.

  • Galaxy AI Proposes Free API Access: Galaxy AI was introduced by @white_d3vil which offers complimentary API services for high-caliber AI models, including GPT-4, GPT-4-1106-PREVIEW, and GPT-3.5-turbo-1106.

  • Tech Stack Advice for Scalable LLM Web Apps: Mixed suggestions were made on building a scalable LLM web application, ranging from using Python 3.11 with FastAPI and Langchain to leveraging Next.js with Langserve.js on Vercel. Langchain’s production readiness and customization for commercial use were queried, expressing a preference for custom code in production settings.

  • Beware of Potential Spam Links: Users are warned against a suspicious link shared by @teitei40 across multiple channels, claiming to offer a $50 Steam gift card but raising concerns about its legitimacy and potential as a phishing attempt.

  • Innovative Projects and Educational Resources: The community has showcased a variety of works, including Devscribe AI’s YouTube video chat tool, a guide on using generative AI for asset-liability management, and a Next.js 14+ starter template for modern web development. Additionally, discussions on enhancing Langchain’s retrieval-augmented generation and the efficacy of the Feynman Technique for learning were highlighted.

Latent Space Discord Summary

  • Overflowing with AI Knowledge: Gemini for Google Cloud is set for a boost with the integration of Stack Overflow’s knowledge via OverflowAPI, aiming to sharpen AI assistance directly within the cloud console.

  • Brin Banks on AGI Breakthroughs: Google’s co-founder, Sergey Brin, has sparked discussions by suggesting initiatives like Gemini could lead Google’s artificial intelligence towards AGI, as flaunted in a circulating tweet about his insights.

  • Perfecting Digital Reality: LayerDiffusion envisions a new horizon for AI creativity, offering tools to seamlessly insert items with realistic reflections into photos, a promising venture for Stable Diffusion aficionados.

  • Claude 3 Makes a Splash: Anthropic’s announcement of its Claude 3 model family stirs the AI community with discussions on its advanced metadata awareness and impact on current AI models, with important benchmarks being shared, such as Claude 3’s ~60% accuracy on GPQA.

  • India’s AI Regulatory Chokepoint: Martin Casado’s tweet on India’s AI deployment regulations has raised alarms over potential stifling of innovation due to the required government approval, stirring debate among the tech community about the balance between oversight and progress.

OpenAccess AI Collective (axolotl) Discord Summary

  • Resolved: Hugging Face Commit Chaos: @giftedgummybee reported that the Hugging Face KTO issue was resolved by identifying a commit version mix-up. This primarily concerned the Hugging Face transformers library, relevant to Axolotl’s deployment.

  • Axolotl Staying Put on Hugging Face: @nanobitz clarified that there are no plans to port Axolotl to Tinygrad, citing dependency on the Hugging Face transformers library, and reminded users to keep configuration questions to appropriate help channels.

  • Optuna CLI Consideration for Axolotl: @casper_ai suggested the integration of a CLI tool for hyperparameter optimization using Optuna, referencing a GitHub issue for context.

  • Deep Learning GPU Conundrums and Fixes: Various GPU-related issues were surfaced, including a python vs python3 conflict and a glitch with deepspeed’s final save; however, @rtyax did not experience issues with deepspeed 0.13.4’s final save function.

  • Mixtral vs. Mistral: Model Preference Showdown: A discussion was initiated by @dctanner comparing Mixtral to Mistral Large for synthetic data generation, with @le_mess expressing a preference for personal models over Mixtral, suggesting nuanced performance outcomes for different use-cases.

DiscoResearch Discord Summary

  • Aristotelian AI Models Entering the Stage: @crispstrobe discussed the potential of “Aristotelian Rescoring”, a concept that could address complex AI challenges, highlighting related works such as STORIUM, FairytaleQA, and TellMeWhy, with resources available on GitHub and Hugging Face.
  • German Semantics Leaping Forward: @sten6633 improved German semantic similarity calculations by fine-tuning Deepset’s gbertlarge with domain-specific texts and Telekom’s paraphrase dataset, and turning it into a sentence transformer.
  • Eager for AI-Production Know-how Sharing: @dsquared70 invited individuals working with Generative AI in production to speak at an upcoming conference in Asheville, NC, with applications open until April 30.
  • Aligning German Data Delicately: @johannhartmann pointed out a translation error in a dataset but managed to integrate the fixed dataset into FastEval, following a bug fix in their evaluation using ./fasteval.
  • Brezn’s Bilingual Breakthrough: @thomasrenkert lauded Brezn-7b’s performance in German, spurred by model merging and aligned with 3 DPO datasets, while @johannhartmann proposed potentially using ChatML by default to improve Brezn’s benchmark scores.

Datasette - LLM (@SimonW) Discord Summary

  • Stable Diffusion Goes Extra Large: Stable Diffusion XL Lightning impresses users with its capabilities, as highlighted in the shared demo link: fastsdxl.ai.
  • Claude 3 Interaction Now Simplified: SimonW released a new plugin for the Claude 3 models, with the repository available on GitHub.
  • Artichoke Naming Gets Creative: One user infuses humor into the discussion by suggesting whimsical names for artichokes such as “Choke-a-tastic” and “Arti-party.”
  • Mistral Model Prices Noticed: The Mistral large model earns praise for its data extraction performance, but also noted for its higher-than-desired cost.
  • Plugin Development Speed Wins Applause: The development pace of the new plugin for interacting with the Claude 3 model garners quick commendation from the community.

Alignment Lab AI Discord Summary

  • New Collaboration Opportunity: @wasooli has shown a keen interest to collaborate within the Alignment Lab AI community, with @taodoggy open to further discussions through direct messaging.
  • GenAI Conference Call: @dsquared70 has announced a GenAI in production conference, encouraging submissions by April 30th. More information and application details can be found at AI in Production.

Skunkworks AI Discord Summary

  • Call for AI Integration Expertise: Developers actively integrating GenAI into production systems are invited to share their insights at a conference in Asheville, NC. Interested parties can submit their papers by April 30 at AI in Production Call for Presentations.

  • A Comical Start to the Day: An attempt to brighten the chat with “good morning yokks” brought a humorous start to the discussions despite the typo corrected to “yolks”.

AI Engineer Foundation Discord Summary

  • Hackathon Hierarchy Explained: @needforspeed4 raised a question about whether the Agape hackathon is related to the AI Engineer Foundation managing the Discord server, and the use of separate Discords for different hackathons. @hackgoofer clarified that the AI Engineer Foundation Hackathons are held within this Discord, but pointed out that the Agape hackathon operates independently.

PART 2: Detailed by-Channel summaries and links

TheBloke ▷ #general (994 messages🔥🔥🔥):

  • Claude 3 AI, Safety or Over-Caution?: Claude 3, a new AI model, is showcased by users highlighting its heightened sensitivity to potentially offensive content or copyright concerns.
  • Rumination Over AI’s Role in Game Development: Some users predict AI will be involved in the rendering and creation of future video games; however, netrve voices skepticism about the capability to bruteforce current AI limitations with raw compute.
  • Contentious NVIDIA Licensing Tactics: Nvidia’s efforts to restrict the use of CUDA on non-NVIDIA hardware through licensing terms spurs discussion on the legality and impact on developers, especially regarding translation layers.
  • Benchmarks and OpenAI’s Future: Models like Phind 70b are discussed while users question the reliability of benchmarks and the significance of ongoing AI model releases with the anticipation of GPT-5.
  • Technical Deep Dive into GPU Technologies: Netrve discusses the complexities and advancements in game rendering, including Epic’s Nanite system in Unreal Engine 5, while others lament restrictive moves by NVIDIA.

Links mentioned:

TheBloke ▷ #characters-roleplay-stories (379 messages🔥🔥):

  • Understanding Llama.cpp Limitations: @pri9278 noted that while SD (Sparse Diffusion) and Lookup decoding are implemented in llama.cpp, they are not integrated into server APIs, which limits the capabilities of the server-sided implementation of the model.
  • Model Performance and Hardcoding: @superking__ discussed the complexity of hardcoding models, noting the difficulty when using transformers and the possibilities when using strict formats for model prompting.
  • Discussions on Roleplay and Story Generation: Chat members, including @gamingdaveuk, @netrve, @lisamacintosh, and @concedo, engaged in complex discussions about using AI models for roleplaying and story generation, exploring topics like context caching for optimization, front-end/user interface quirks, and specific use cases for chatbots in roleplay scenarios.
  • Sharing Experiences with Fine-tuned Models: @c.gato shared their experience testing the Thespis-CurtainCall Mixtral model, commenting on its performance with complex tasks like playing tic-tac-toe and generating prompts based on greentext stories.
  • Engaging with AutoGPT and DSPY: @sunija inquired about the status of AutoGPT and its applications in roleplay, prompting replies from @wolfsauge and @maldevide discussing alternative methods, such as DSPY, for optimized prompt generation and automatic evaluation of response variations.

Links mentioned:

TheBloke ▷ #training-and-fine-tuning (39 messages🔥):

  • Fine-tuning Troubles: @coldedkiller experienced issues fine-tuning an OpenOrca Mistral 7b model; after converting to ‘gguf’ format the model failed to give correct outputs for both its own data and the data it was fine-tuned on.
  • Cosine Similarity Cutoffs in Training Models: @gt9393 inquired about the appropriate cosine similarity cutoff for models, leading @dirtytigerx to respond that it depends on various factors, and no hard cutoff can be provided.
  • Use of Special Tokens and Model Training: @gt9393 discussed uncertainties regarding the inclusion of start and end of sequence tokens in datasets. @dirtytigerx recommended having these tokens, but appending them after the prompt has been encoded.
  • Chess Model Training Achievement: @.haileystorm shared their success training an 11M parameter Mamba LM chess model, offering links to the relevant resources, training code, and indicating it plays better as white. The model’s training was compared to a larger parameter model and showcased a 37.7% win rate against Stockfish level 0.
  • Seeking Fine-Tuning Guidance for Small to Medium LLMs: Users @coldedkiller and @zelrik sought advice for fine-tuning language models, being directed to resources by Jon Durbin and a guide from UnslothAI. Discussions covered format, special tokens, and hardware requirements with @maldevide providing insights on preprocessing book texts, hardware capacities, and tools for parameter-efficient fine-tuning (PEFT).

Links mentioned:

TheBloke ▷ #model-merging (1 messages):

  • Fine-tuning Woes with OpenOrca: @coldedkiller is experiencing issues with a fine-tuned OpenOrca Mistral 7b model. After converting it to gguf format, the model fails to produce proper output on both original and fine-tuned datasets.

TheBloke ▷ #coding (11 messages🔥):

  • OpenOrca Fine-tuning Woes: User @coldedkiller is facing issues where their fine-tuned OpenOrca Mistral 7b model isn’t outputting expected answers post conversion to gguf format. @spottyluck suggests checking the model’s performance pre-quantization and considering the use of imatrix if there’s a problem with outliers.

  • GPTQ Out of the Spotlight: @yeghro queries if GPTQ is no longer in focus since TheBloke has stopped releasing more about it, and @_._pandora_._ hints at rumors that TheBloke is missing, contributing to no recent releases.

  • Model Test Dilemma: @gamingdaveuk seeks the smallest possible model to load on a 6GB VRAM laptop for API call tests. They mention finding an answer on Reddit suggesting the use of Mistral instruct v0.2, and @dirtytigerx advocates for any gguf quant model as long as it’s around 4GB in size.

  • Coldedkiller’s Model Mishap: In a follow-up, @coldedkiller elaborates on the issue with their fine-tuned model not providing answers from their trained Q&A dataset after format conversion. They observe the model gives irrelevant responses when queried.

  • Ajibawa_2023 Showcases Enhanced Models: User @ajibawa_2023 shares links to their fine-tuned models boasting enhanced coding capabilities. One model, OpenHermes-2.5-Code-290k-13B, incorporates their dataset, performs well in coding rankings, and can handle various tasks including blogging and story generation.

Links mentioned:

OpenAI ▷ #ai-discussions (128 messages🔥🔥):

  • GPT Alternatives Discussed Amidst Downtime: User @whodidthatt12 expressed frustration with GPT being down and inquired about alternative AI writing assistants. Suggestions included Bing Chat, Hugginface, Deft GPT, LeChat, together.xyz, and Stable Chat.

  • Claude 3 AI Impressions: @glamrat mentioned testing Anthropic’s Claude 3, finding it impressive, especially the free Sonnet model. Various users are discussing their experiences and expectations, from using Claude 3 for math tutoring (@reynupj) to potentially dropping a GPT Plus subscription in favor of Claude (@treks1766).

  • Enthusiasm for AI Competition: Users like @treks1766 and @lolrepeatlol expressed their excitement about the competition between AI services like Claude 3 and GPT-4, anticipating benefits for consumers and advancements in the AI field.

  • Debate Over AI Model Capabilities: Some users argued over the reported superiority of Claude 3 over OpenAI’s models (@darthcourt., @hanah_34414, @cosmosraven), with comments ranging from skepticism (@drinkoblog.weebly.com) to anticipation for the next big release by OpenAI.

  • Cost Considerations and Availability: Concerns were raised about the cost of using Claude 3’s API (@dezuzel) and the availability of different models in various regions. There is anticipation around how existing services like Perplexity AI Pro will integrate with new models like Claude 3 (@hugovranic).

Links mentioned:

OpenAI ▷ #gpt-4-discussions (38 messages🔥):

  • GPT Alternatives Sought Amidst Downtime: User @whodidthatt12 is seeking alternative AI options for writing assignments due to GPT being down.
  • Custom CSVs for AI Knowledge Bases: @.bren_._ inquired if custom agents could utilize CSV files as part of their knowledge bases and was experiencing technical difficulties confirming if it’s a valid file type for use.
  • File Types and Technical Support in Custom Agents: @.bren_._ shared an error message about accessing system root directories, while @darthgustav. suggested using row-separated values in plain text files as a more successful approach.
  • Finding the Most Optimal GPT for Code: @yuyu1337 is searching for a GPT model that generates code with optimal time/space complexity, with other users like @eskcanta and @beanz_and_rice contributing to the discussion on achieving optimality and providing creative pseudo code.
  • GPT Store Publishing Paths Clarified: @bluenail65 queries about the necessity of a website to list GPTs in the store, to which @solbus clarifies the options for publishing, including using a billing name or sharing privately via a link.

OpenAI ▷ #prompt-engineering (506 messages🔥🔥🔥):

  • Humor Struggles in API: @dantekavala is experiencing a discrepancy where tests in ChatGPT work well for prompting a humorous writing style, but the same approach fails when used with GPT 3.5 API; the API’s output remains consistent, unaffected by the requested style. They’ve tried various styles and reached out for guidance in the Developers Corner.

  • Owl and Palm Puzzle Persists: Many participants, including @madame_architect, @aminelg, and @eskcanta, have engaged in a lively exploration of the “owl and palm” brain teaser. While they have all attempted various prompting strategies to accurately solve the puzzle using GPT-V, none have achieved consistent success.

  • Prompt Engineering Tactics Discussed: User @madame_architect suggests using multiple prompting tactics like the “take a deep breath” trick from the system 2 thinking paper and points from emotionprompt (tm) to tackle the problem. However, @eskcanta notes that the core issue might be with the Vision model’s training, not so much the prompting methods themselves.

  • Vision Model’s Limitations: Despite testing various prompts and theories about the Vision model’s understanding of image measurement, users like @eskcanta and @darthgustav. highlight that the model’s failure to consistently interpret measurements correctly may stem from the need for additional training, rather than prompting inadequacies.

  • Feedback on Personal Creations: Newcomer @dollengo inquires about creating and training AI for educational purposes, with an intention to publish, but there is a focus on staying within OpenAI’s dialogue and sharing policies. Users @eskcanta and @aminelg give advice respecting the platforms terms of service and prompt-writing practices for the AI models.

Links mentioned:

  • Terms of use: no description found
  • DALL·E 3: DALL·E 3 understands significantly more nuance and detail than our previous systems, allowing you to easily translate your ideas into exceptionally accurate images.

OpenAI ▷ #api-discussions (506 messages🔥🔥🔥):

  • Puzzle Prompt Engineering Saga Continues: Users @aminelg, @eskcanta, @darthgustav., and @madame_architect continued their efforts to craft the perfect advanced prompt for an AI vision puzzle involving an owl and a tree. Despite various strategies, issues persisted with GPT-V accurately interpreting the image, leading to discussions about the model’s limitations and potential need for retraining.

  • The Highs and Lows of Model Behavior: Across multiple attempts with nuanced prompts (like @madame_architect’s which achieved a singular success), GPT-V consistently misinterpreted the measurement of the 200 units on the right side of the image, often confusing it with the full height of the tree, making it an observable weakness in the model’s capabilities.

  • Playful Competition Heats Up: Discussions turned humorous as @aminelg and @spikyd exchanged jests about reaching their usage limits and joked about generating prompts that would outperform the AI’s current understanding of the complex image teasing out the occasional correct response as a “10 points to GPT V” moment.

  • Sharing Knowledge Comes at a Cost: @darthgustav. expressed frustration with the Discord server’s auto-moderation, which limited his ability to discuss certain details and share prompts, triggering calls for OpenAI to revise system prompt restrictions for a more transparent and conducive prompt engineering discussion.

  • Newcomer Queries and Tips Exchange: New participants like @snkrbots, @chenzhen0048, and @dollengo sought advice on prompt engineering and AI training, eliciting responses from veteran contributors. Ideas exchanged included improving prompts with template structuring, asking GPT for refinement aid, and the potential for AI to assist in content creation tasks.

Links mentioned:

  • Terms of use: no description found
  • DALL·E 3: DALL·E 3 understands significantly more nuance and detail than our previous systems, allowing you to easily translate your ideas into exceptionally accurate images.

Perplexity AI ▷ #general (618 messages🔥🔥🔥):

  • Potential Perplexity Subscription Issue: User @vivekrgopal expressed frustration about being charged for an annual subscription after attempting to cancel during the trial period. They requested assistance for a refund through direct messages.
  • Users Eager for New AI Integrations: There’s anticipation among users like @bioforever and @sebastyan5218 for Perplexity to integrate new language models such as Claude 3 and Gemini Ultra, highlighting the community’s desire for the latest AI advancements.
  • Discussion on Perplexity AI’s Effectiveness: User @names8619 cheered on Perplexity Pro’s performance, comparing it favorably against YouTube for research without clickbait, while others mentioned challenges with OpenAI’s GPT-3 results needing to switch to models like Mistral for certain topics.
  • Uncertainty Over AI Model Availability: Users @gooddawg10 and @fluxkraken discussed the availability of certain AI models (Gemini Ultra, Claude 3) within Perplexity, with some confusion about which models are accessible to users.
  • Comparison of AI Models and Their Responses: User @dailyfocus_daily shared a benchmark question regarding sorting labeled balls into boxes and compared the varied answers given by different AI models including GPT-4, Claude 3, and others, illustrating the inconsistencies in their problem-solving abilities.

Links mentioned:

Perplexity AI ▷ #sharing (20 messages🔥):

  • Exploring Identity Access Management: User @riverborse shared a link diving into what identity access management (IAM) entails.
  • Understanding Perplexity v2: @scarey022 provided a link to learn more about the concept of perplexity in language models.
  • In Search of Optimal Solutions: User @dtyler10 posted a link that leads to discussions about creating optimal settings, environments, or outcomes.
  • Technical Insights Offered: A technical explanation was the focus of a link shared by @imigueldiaz.
  • AI Basics Explored: @krishna08011 and @elpacon64 shared links (link1, link2) discussing what AI is and its various aspects.

Perplexity AI ▷ #pplx-api (27 messages🔥):

  • Confusion over Random Number Generator Ethics: User @moistcornflake expressed amusement and confusion over codellama providing an ethical warning when asked to create a random number generator. The bot response suggested prioritizing content that promotes positive values and ethical considerations.

  • Performance Issues Noted for Time-Sensitive Queries: @brknclock1215 observed an improvement in general quality but reported continued failures in time-sensitive queries and reminisced that it used to perform better in such tasks.

  • Missing Feature for YouTube Summarization: @rexx.0569 highlighted the absence of a feature that summarized YouTube videos, which seemed to have been a native function of Perplexity. They noted that the feature isn’t accessible on different devices.

  • Inquiry About Perplexity API Usage: @marvin_luck sought help on how to achieve the same effects as a web request through Perplexity API. To which @icelavaman shared a discord link, presumably with relevant information, Perplexity API Info.

  • Users Anticipate Citation Feature Access: @_samrat and @brknclock1215 are waiting to gain access to citations in the API, and @icelavaman mentioned that this process might take 1-2 weeks or more. @brknclock1215 later confirmed seeing improvement in response quality and eagerly awaits the addition of citations.

  • Temperature Settings Discussion: @brknclock1215, @thedigitalcat, and @heathenist engaged in a discussion about how temperature settings in AI models affect the naturalness and reliability of language outputs. They suggested that lower temperature settings don’t always guarantee more reliable outputs and touched upon the complexity of natural language and self-attention mechanisms.

Links mentioned:

Perplexity Blog): Explore Perplexity’s blog for articles, announcements, product updates, and tips to optimize your experience. Stay informed and make the most of Perplexity.

Mistral ▷ #general (213 messages🔥🔥):

  • Broken Discord Links: Users @v01338 and _._pandora_._ mentioned that both Discord and LinkedIn links on the Mistral AI website are broken. _._pandora_._ confirmed this by checking HTML source.
  • Discussion on Model Lock-In Scenarios: @justandi asked if migrating from one model to another in an enterprise context could lock in a specific implementation. @mrdragonfox chimes in saying that the inference API is similar across platforms, hinting at seamless migration.
  • Concerns Over Model Benchmarking Transparency: @i_am_dom expressed concerns about the lack of published scores for specific Mistral model benchmarks, suggesting that transparency is essential, especially from benchmark owners.
  • Ollama and VLLM Discussion for Mixtral Inference: @distro1546 inquired about achieving sub-second inference times with Mixtral using an A100 server and was advised by @mrdragonfox to consider exllamav2 or vLLM deployment with 6bpw instead of using llama.cpp, which doesn’t fully utilize GPU capabilities.
  • Clarification on Mixtral’s Context Window: @_._pandora_._ and @i_am_dom discuss confusion regarding Mistral and Mixtral’s context sizes and sliding window functionality. A Reddit update and documentation inaccuracies in Hugging Face were mentioned, highlighting the need for HF to update their documents.

Links mentioned:

Mistral ▷ #models (79 messages🔥🔥):

  • Mistral Large Surprises in Coding: @claidler reported better performance with Mistral Large than GPT-4 for coding tasks, despite official tests suggesting GPT-4’s superiority. They observed Mistral Large providing correct solutions where GPT-4 failed repeatedly, raising questions about the tests’ accuracy or applicability in certain scenarios.

  • Personal Benchmarks Matter Most: @tom_lrd advised that personal experience with models should be considered the best benchmark and recommended trying different models with the same input to see their performance on specific use cases.

  • Mistral Next’s Speed Questioned: @nezha___ inquired if Mistral Next is smaller than Mistral Large, noting its quicker responses, and wondering if its speed is due to being a Mixture of Experts (MoE) model.

  • Context Size Limit Clarified: Conversation between @fauji2464, @mrdragonfox, and ._pandora_._ discussed warnings about exceeding the model’s maximum length when using Mistral-7B-Instruct-v0.2. It was clarified that the model will ignore content beyond the 32k token limit, leading to performance issues.

  • LLM Context Windows Explained: ._pandora_._ explained Large Language Models (LLMs) like Mistral and Mixtral have a “narrow vision” and can only consider up to 32k tokens for their current context in each inference cycle. If input exceeds this, extra content is ignored, but the model will still produce output based on the last 32k tokens.

Links mentioned:

LLM Visualization: no description found

Mistral ▷ #deployment (17 messages🔥):

  • Seeking Mistral Deployment on Dual 3090s: User @generalenthu inquired about the best approach for setting up Mistral on a system with 2x NVIDIA 3090 GPUs, aiming for minimal quantization and seeking advice on managing the trade-off between speed and using GPU vs RAM.
  • VRAM Requirements for fp16: @mrdragonfox informed that using fp16 precision would require approximately 90GB of VRAM for running the model.
  • Model Run with Exllama: @mrdragonfox mentioned that on a 48GB VRAM setup, one can run Mistral with about 5-6 bits per word (bpw) using exllama configuration just fine.
  • How to Start Setup and Use Quants: @mrdragonfox advised @generalenthu to start with a “regular oobabooga” as a default setup, access N inferences, and use quantization models from lonestriker and turboderp available on Hugging Face.
  • Additional Resources and Community Support: @mrdragonfox suggested that @generalenthu join “thebloke“‘s Discord for further support from a community that assists with local model deployment, noting that it could be a supplement to the current community for this specific use case.

Mistral ▷ #ref-implem (1 messages):

  • Request for Minimalistic Mistral Training Guide: User @casper_ai mentioned that the community faces challenges in achieving optimal results with the Mixtral model. They referenced previous conversations which suggest an implementation discrepancy in the Huggingface trainer, and asked for a minimalistic reference implementation of Mixtral training.

Mistral ▷ #finetuning (1 messages):

  • Smaug-Mixtral Outperforms Mixtral-8x7b: @bdambrosio mentioned that Smaug-Mixtral surpasses mixtral-8x7b-instruct-v0.1 in 8bit exl2 quant tests, specifically for applications in long-context scientific reasoning and medium length report writing. Exact performance metrics were not provided, but outcomes may vary based on use case.

Mistral ▷ #showcase (3 messages):

  • Collaborative AI for Offline LLM Agents: User @yoan8095 shared their work on using Mistral 7b for LLM Agents that operate offline, coupling it with a neuro-symbolic system for better planning. The repository available at HybridAGI on GitHub allows for Graph-based Prompt Programming to program AI behavior.
  • Feature-Rich Discord Bot Announcement: @jakobdylanc promotes their Discord bot capable of interfacing with over 100 LLMs, offering features such as collaborative prompting, vision support, and streamed responses, all within 200 lines of code. The project is outlined on GitHub.
  • Mistral-Large’s Formatting Flaws: @fergusfettes reports that while Mistral-large produces good results, it struggles with formatting and switching between completion mode and chat mode. They shared a video demonstrating how loomed integration of different LLMs can work at Multiloom Demo: Fieldshifting Nightshade.

Links mentioned:

Mistral ▷ #random (13 messages🔥):

  • Kubernetes AI Tooling Made Easy: @alextreebeard shared their open-sourced package meant to simplify setting up AI tools on Kubernetes, inviting users for feedback. The tool can be found at GitHub - treebeardtech/terraform-helm-kubeflow.
  • The Arrival of Claude-3: @benjoyo. linked the Anthropic AI’s announcement of their new model family, Claude-3, and hinted a query about when a comparable “mistral-huge” might be released.
  • Model Training Takes Time: In response to a query related to Mistral’s response to new competition, @mrdragonfox explained that large models take quite a while to train, with large versions only recently coming out.
  • Competition Heating Up: Following early testing, @benjoyo. observed that Anthropic’s new model is “extremely capable and ultra steerable/adherent,” while continuing to champion the value of open weights for differentiation.
  • New AI Model Pricing Discussed: @nunodonato reflected on the costliness of the new models, while @mrdragonfox provided specific pricing for Opus model usage, with input costing $15 per Mega Token (MTok) and output at $75 per MTok.

Links mentioned:

GitHub - treebeardtech/terraform-helm-kubeflow: Kubeflow Terraform Modules - run Jupyter in Kubernetes 🪐: Kubeflow Terraform Modules - run Jupyter in Kubernetes 🪐 - treebeardtech/terraform-helm-kubeflow

Mistral ▷ #la-plateforme (82 messages🔥🔥):

  • Function Calling In NodeJS: @jetset2000 was looking for documentation on using function calling with Mistral in NodeJS. @sophiamyang provided a helpful response with an example in the Mistral AI’s JS client repository.
  • Mistral Medium Model Timeout Issues: @patrice_33841 reported timeouts when making requests to the mistral-medium-latest model. Other users seemed to have no trouble with the medium model, and @mrdragonfox provided contact information for support, suggesting to post in the tech support channel or email support directly.
  • Confusion on Prompt Documentation: @benjoyo expressed confusion about the consistency between user and system messages and actual prompts in Mistral’s documentation, which @sophiamyang acknowledged and promised clarity soon.
  • Response Format Clarifications Needed: @gbourdin encountered issues with new JSON response formats, leading to a discussion about correct prompt settings which @proffessorblue clarified with instructions from the docs, resolving @gbourdin’s problem.
  • Exploring Sentiment Analysis Efficacy: @krangbae shared experiences with using different Mistral models for sentiment analysis, noting that 8x7b seemed more effective than the small model.

Links mentioned:

Mistral ▷ #le-chat (126 messages🔥🔥):

  • Mistral Large Style Praised: @foxalabs_32486 expressed appreciation for Mistral Large’s more natural and less stuffy writing style, while maintaining depth similar to GPT-4.
  • User Interface Quirks: @steelpotato1 reported an issue with the user interface where prompts and responses jump positions during the generation process, creating a disorienting user experience.
  • Rate Limit Woes and Workarounds: Users like @shanman6991 and @tom_lrd encountered rate limits when using the chat API, leading to discussions about usage limits and suggestions to contact support for adjustments.
  • Hallucination and Misinformation Concerns: @godefv pointed out that Le Chat sometimes provides incorrect information or generates content based on hallucinations rather than actual knowledge, like claiming details about a non-existent PhD thesis.
  • API Usage Puzzle: @sim3239 struggled with differences in API and Le Chat responses, inquiring about parameters used by Le Chat to replicate its complete responses in their own Python application.

Links mentioned:

Mistral ▷ #failed-prompts (13 messages🔥):

  • Mistral Model Math Mishap: @propheticus_05547 discovered that Mistral Instruct 7B v0.2 Q4_K_M incorrectly calculated 10+3-9+33 as 22 instead of the correct answer, 37, when run in Jan with Vulkan acceleration, questioning the model’s arithmetic capabilities.
  • Learning Curve for Running Models Locally: Responding to @_._pandora_._’s explanation about LLMs’ poor math skills, @propheticus_05547 noted improvement when the prompts were limited to knowledge and language-based questions and shared success with a different version, Q5_K_M, that could handle simple math.
  • Mistral Model Resists System Prompts: @jakobdylanc reported that Mistral Large resists following its system prompt more than the Mistral Medium model when prompted as a helpful Discord chatbot named Jakobson.
  • Differences with GPT-4 on API Exposure: @benjoyo observed that Mistral Large on the API tends to reveal its functional capabilities more readily than GPT-4, which generally doesn’t expose such technical details to the user.
  • Not All Mistral Behavior Is Predictable: In response to the behavior seen in Mistral Large, @mrdragonfox cautioned against assuming the bot’s responses are always meaningful, suggesting some could be mere hallucinations.

Nous Research AI ▷ #ctx-length-research (5 messages):

  • Phi-2 Token Limit Confusion: @faldore questioned the possibility of using Phi-2 with more than 2k tokens. They pointed to Hugging Face’s Phi-2 model summary which indicates a 2k token limit.
  • Direct Link to Phi-2 Configuration: In the follow-up, @faldore provided a direct link to the Phi-2 configuration file which shows the "max_position_embeddings": 2048 setting.
  • Explanation on Phi-2 Token Extension: @vatsadev responded to the question about extending Phi-2’s tokens by indicating it would act like a default transformer, implying standard transformer behavior beyond configured limits.
  • Caution on Extending Phi-2’s Capabilities: In another message, @vatsadev warned that deviating from Phi-2’s configured settings could either block the model or cause erratic performance.

Links mentioned:

Nous Research AI ▷ #off-topic (31 messages🔥):

  • Mac Set-Up Suggestions Galore: @gabriel_syme is looking for Mac application suggestions. Numerous users like @max_paperclips and @deki04 proposed essentials such as Homebrew, Parallels for ARM-based Macs, temperature monitoring with TG Pro, and using Time Machine for backups, with @deki04 also sharing helpful Mac setup tips for Python/ML from a YouTuber.
  • Better Touch Tool and More: @denovich recommends Better Touch Tool for gesture control, setting up Samba share with Time Machine, and enjoying Windows 11 ARM under Parallels for those who need Windows on their Mac.
  • Handy Apps and Homebrew to the Rescue: @eas2535 highlights the utility of Homebrew, sharing a link to the tool, and lists useful applications like Maccy, Hyperkey, Shortcuts, and more for an efficient Mac experience.
  • Human Genetic Diversity at its Peak: @teknium shares a tweet by @richardfuisz that claims every possible mutation in the human genome now exists in at least 50 people. This led to a request for the related scientific paper by .ben.com, who couldn’t access the Twitter thread.
  • Enthusiasm for tridao: @hexani mentions that “tridao is so goated,” to which @teknium responds with a cat emoji, suggesting agreement.

Links mentioned:

  • no title found): no description found
  • Tweet from Richard Fuisz (@richardfuisz): Every mutation that could exist, does exist. That has only been true for ~200 years. Most beneficial variants just haven’t had the time to become ubiquitous. But now, at least 50 people in the wor…
  • TG Pro: Maximize your Mac’s performance with TG Pro. The ultimate solution for fan control and extensive temperature monitoring: CPU, GPU, SSD, and more.
  • Setting up new MacBook for software development: Here I go through setting up a new MacBook for software development, the way I usually set things up for my own tasks.▶️ Setting up a new M2 Mac Mini - https…

Nous Research AI ▷ #interesting-links (49 messages🔥):

  • Scaling and Efficiency Hot Debate: @ldj argued that compute optimality breaks down after the 500B-1T parameter mark, with efficiency gains from MoE and training techniques rather than scale. They cited Sam Altman suggesting the era of scaling is over, with future gains from architectural innovations, as detailed in this article and a supporting Medium post here.

  • The Skepticism Around 100T Models: @intervitens and @.ben.com were skeptical of the feasibility and practicality of training 100T parameter models, questioning both hardware capabilities and data availability. @euclaise countered, suggesting availability of sufficient data resources like Redpajama v2.

  • Potential of Smaller Models: @ldj further emphasized that bigger models are not necessarily better, pointing out that GPT-4 might have better performance with around 200B active parameters compared to models with over 500B active parameters. @teknium disagreed, suggesting that parameter scaling could still be beneficial if combined with adequate training data.

  • Cost Concerns in AI Scaling: @ldj raised a practical concern about the cost-effectiveness of scaling up models, alluding to the possibility that increasing the number of parameters could result in prohibitively high costs for both training and inference.

  • Reference to AI Model Recent Comparisons: A link to a Reddit post featuring a comparison of 17 new models, adding up to 64 ranked, was shared by @mautonomy, without comments from others in the channel.

Links mentioned:

Nous Research AI ▷ #general (328 messages🔥🔥):

  • AI for Music: User @audaciousd looked forward to new music generative AI, with particular interest in the upcoming release from a company called stabilities. They inquired about others’ knowledge on the topic.
  • Claude 3 Generates Buzz: Discussion on Claude 3’s release: @fibleep referenced an announcement, and users like @4biddden and @mautonomy speculated on its comparison to GPT-4 performance.
  • GPT-4 vs. Claude 3 Opinions: Several users, including @teknium, shared and sought feedback through Twitter polls on whether Claude 3 Opus is actually better than GPT-4.
  • B2B Sales Strategies Shared: User @mihai4256 asked for advice on selling B2B software products, leading @hexani to offer their experience on targeting small businesses and the challenges and strategies involved. Hexani emphasized the necessity of direct engagement and a high bar for product viability.
  • Knowledge Graph Building Resources Explored: Users @mihai4256 and @everyoneisgross discussed models and approaches for creating knowledge graphs, with @max_paperclips suggesting using Hermes for JSON structured triplet extraction. They shared that a new model with improved structured data extraction capabilities is forthcoming.

Links mentioned:

Nous Research AI ▷ #ask-about-llms (32 messages🔥):

  • PPO Script Inquiry: @xela_akwa in search for a PyTorch or PyTorch Lightning script for PPO on LLMs, finds the hf trl (HuggingFace Transformers Reinforcement Learning) limited. A conversation ensued with potential alternatives being suggested, including a GitHub repository by @.mahouko for related work. No definitive solution for PPO was provided.
  • Function Calling Model Server Showdown: @giulio123456 questions about the best inference platform for fastest function calling models. Replies by @sundar_99385 and @dustinwcarr suggested that Anyscale and Deepinfra are some of the platforms supporting Mistral/Mixtral with notable performance, but no direct latency comparisons were provided.
  • Format for 1-Shot in ChatML: @cognitivetech queries about the correct templating for 1-shot training using ChatML with a focus on system-user interactions; @teknium confirms the correct format excludes the ‘name=’ convention and endorses a simpler template.
  • LLaMa Architecture Clarification: @qtnx asks about the specifics of patch embedding conversion in LLaMa 1 and 1.5 architectures, receiving a brief acknowledgement without specific details given from @teknium and a follow-up query by @qnguyen3.
  • AI-Assisted Chat Considerations: @betim01 discusses strategies for fine-tuning an AI model for customer interactions, considering Nous Hermes and RAG. @teknium warned against potential downsides with an example of ChatGPT being tricked in a dealership context, recommending a more reliable RAG approach and listing potential inference platform options.
  • Next Steps for Language Models: @pier1337 speculates on the future of language models, mentioning Sutskever’s views on object-driven AI and the potential application within simulated environments, but there were no direct responses to this prediction within the chat log provided.

Links mentioned:

Nous Research AI ▷ #project-obsidian (2 messages):

  • Exploring Moondream: User @ee.dd shared their positive experience with Moondream, highlighting its speed and effectiveness after some testing. They provided the GitHub link: Moondream - tiny vision language model.

Links mentioned:

GitHub - vikhyat/moondream: tiny vision language model: tiny vision language model. Contribute to vikhyat/moondream development by creating an account on GitHub.

Eleuther ▷ #general (197 messages🔥🔥):

  • AI Alignment in Open-Source: The Open Source Initiative (OSI) will be releasing a new draft of the open-source AI definition monthly, targeting a 1.0 release by the end of October 2024, with discussions in their public forum and draft documents available for review.

  • Legal Battle Against the DMCA: The EFF has filed a lawsuit, Green v. Department of Justice, challenging the anti-circumvention and anti-trafficking provisions of the DMCA for restricting access to purchased copyrighted materials. Full case details.

  • Quantization Debate in Neural Networks: A discussion emerged about the practicality and implications of quantization in neural network weights and activations. Users debated over papers like the bitlinear paper and the notion of quantizing activation functions, invoking concepts such as epistemic uncertainty.

  • GitHub Malware Spread Campaign: A malware distribution campaign on GitHub has resulted in cloning legitimate repositories, injecting malware, and promoting compromised code. Apiiro’s security analysis explains the threat in detail.

  • Discussions on Predictive Modeling Limitations: User @rallio. asserts there’s no capability to de novo create economically viable biomolecules through predictive modeling, arguing the complexities of biological systems make them unpredictable, unlike physical models used for engineering.

Links mentioned:

Eleuther ▷ #research (115 messages🔥🔥):

  • Counterfactual Examples Sharpen AI’s Visio-Linguistic Reasoning: @digthatdata shared a new approach called CounterCurate, detailed in a research paper, which improves visio-linguistic compositional reasoning in multimodal models. CounterCurate employs GPT-4V and DALLE-3 to create counterfactual image-caption pairs, achieving higher performance on benchmarks such as SugarCrepe.

  • Functional Benchmarks Challenge LLMs: @.the_alt_man pointed to a Twitter thread by @_saurabh suggesting that over 50% of the reported reasoning abilities of LLMs might not be true reasoning. The thread discussed a paper introducing functional benchmarks, revealing significant reasoning gaps in state-of-the-art models, with an associated arXiv draft and GitHub repository.

  • Contrastive Learning for Unanswerable Questions in SQuADv2: @paganpegasus queried the best approach for creating negative samples in contrastive learning for unanswerable questions in SQuADv2, suggesting using Spacy to extract noun chunks as potential negatives. @fern.bear proposed using sets of answers with maximum confidence that are exclusive to SQuADv2, labeled by a model, as another evil method.

  • Concerns Over RLHF Impact on Models’ Capabilities: Discussion by @.the_alt_man and @canadagoose1 revolved around the impact of Reinforcement Learning from Human Feedback (RLHF) on models’ abilities, with a suspicion that RLHF might be degrading performance due to poor implementation.

  • Terminator Architecture: Potential Game-Changer for AI?: @fredholm highlighted the Terminator network described in an arXiv paper, which posits a new architecture potentially replacing residual learning with large implicit kernels for full context interaction. In the ensuing conversation, @harvie_zhang_32234 and @alex_cool6 confirmed the unique approach of Terminator, with the latter stating plans to apply it to image generation and release the code in the future.

Links mentioned:

Eleuther ▷ #scaling-laws (1 messages):

  • Creative Use of Figma for Animation: User @kyo_takano described their process of creating an animation: they crafted a template SVG in Figma, manipulated it to compose various frames, and then used imageio to blend these into a GIF animation.

Eleuther ▷ #interpretability-general (50 messages🔥):

  • Mamba vs Transformers on Learning Parity: @dashiell_s reported that a two-layer Mamba model can learn parity for sequences up to length 128, but doesn’t generalize well to longer sequences. Their tests showed Mamba performed much better than a similarly configured transformer, which struggled with sequences longer than 64.

  • Skeptical of Associative Architecture’s Efficiency: @norabelrose expressed skepticism that architectures based on associative recurrence relations can efficiently learn PARITY, suggesting a need for experimentation to compare LSTM and Mamba performance.

  • Possible Misunderstanding of Sensitivity in ML Literature: @stellaathena pointed out that a paper discussing “sensitivity” actually refers to average sensitivity rather than maximum sensitivity, which could imply different theoretical implications.

  • Trained Mamba on PARITY: @dashiell_s shared that they conducted an experiment with Mamba on the PARITY problem, with results and code available on GitHub (train_mamba.py).

  • Debating the Mechanisms of Learning PARITY: Various discussion points emerged around whether a lookup table or actual computation of PARITY was being learned by models (@norabelrose and @dashiell_s). There was also curiosity about whether deeper transformers could find more sophisticated solutions.

Links mentioned:

Eleuther ▷ #lm-thunderdome (71 messages🔥🔥):

  • AzureML Woes for lm-eval-harness: @synthetic_johnny encountered problems setting up lm-eval-harness on an AzureML compute cluster, experiencing dependency and CUDA device detection issues. A discussion unfolded around finding the right environment build, with @hailey_schoelkopf guiding on the specifics of using the tool, including details on multi-GPU use and model compatibility with AzureML.

  • Multi-Machine Parallelism Challenges: @hailey_schoelkopf clarified that lm-eval-harness does not support multi-machine parallelism, which was causing issues for @synthetic_johnny. A workaround was suggested by @rand0mm who shared Ray Serve, which can help orchestrate the execution of lm-eval-harness across various nodes.

  • Handling Large Models on Single Nodes: @synthetic_johnny was advised by @hailey_schoelkopf on evaluating large language models like GPT-J-6B by using model_args parallelize=True and dtype=bfloat16 to spread the model across multiple GPUs in one node, and to start with a batch size of 1 to avoid out-of-memory errors. Discussion touched on the importance of model-parallel over data-parallel configurations when using AzureML.

  • Confusion over LAMBADA Training Data Usage: @smerkyg posed a query regarding the proper use of the LAMBADA dataset for training LLMs. @hailey_schoelkopf clarified that it’s better not to finetune on the LAMBADA training set as the benchmark is now intended to evaluate general-purpose language modeling abilities.

  • Seeking Example for Multi-GPU HELLASWAG on PYTHON: @antonvls inquired about examples or success stories for running HELLASWAG evaluation on multiple GPUs using Python. @stellaathena directed them to the library’s automated multi-GPU handling and provided a GitHub link for further guidance.

Links mentioned:

Eleuther ▷ #multimodal-general (1 messages):

besiktas: havent really seen anything and have wondered/experimented this as well

Eleuther ▷ #gpt-neox-dev (2 messages):

  • Processing Scripts for The Pile: User @catboy_slim_ shared a GitHub link that could be helpful, particularly the README file. The repository contains scripts related to the development of The Pile, a large-scale dataset for training language models.
  • Inquiry on Validation Data Path: User @pietrolesci queried about the validation data file mentioned in the wandb logs for run v2 1.4B deduped_1dhzgs7f. They are seeking to comprehend if the file is a random sample from the deduplicated pile.

Links mentioned:

the-pile/processing_scripts at master · EleutherAI/the-pile: Contribute to EleutherAI/the-pile development by creating an account on GitHub.

LM Studio ▷ #💬-general (155 messages🔥🔥):

  • Model Troubleshooting in LM Studio: @helloxan. encountered an issue with Codellama Python 7B model in LM Studio and sought help on making the bot respond. Assistance was provided by @heyitsyorkie, who suggested using a different model from Hugging Face (Magicoder-S-DS-6.7B-GGUF), and provided guidance on resolving a “broken quant” issue.
  • Questions on Model Support and Features: Users like @ciphersson, @justmarky, and @archi_95 asked about loading specific models such as LoRAs, QLoRA, and starCoder 2 in LM Studio, as well as uploading pdf files. @heyitsyorkie clarified that features such as QLoRA and starCoder2 support are not yet available, and uploading pdfs directly is not possible.
  • Technical Difficulties with LM Studio Discussed: Several users like @sourguava, @shadowdoggie, and @boting_0215 experienced technical issues ranging from models taking a long time to load to encountering errors with no clarification provided.
  • Model Presets and Parameters Explored: Users were seeking and sharing information on obtaining more presets (@techfren shared a YouTube video resource), understanding parameters for model quantization (@unkown101), and the effects of changing randomness settings for code generation (@drawless111).
  • GPU Requirements and Capabilities for LLM: Various users, including @ethanboyle, @broski_1337, and @ocn touched on the required hardware specifications, like GPU offloading and the necessity of a powerful GPU for efficient model utilization. @heyitsyorkie advised that a GPU with at least 24GB of VRAM is necessary for speed and efficiency when running large language models.

Links mentioned:

LM Studio ▷ #🤖-models-discussion-chat (49 messages🔥):

  • Concerns about Model Leaking Personal Data: User @tobitege shared an unexpected and irrelevant response obtained from a model, raising concerns about data privacy. @tay2win speculated that this could be a case of data scraping, like from LinkedIn or GitHub, which they felt should be illegal.
  • Hugging Face Model Sources Questioned: @tobitege shared their unease after finding a real person matching the name given in an irrelevant model response. This led to a discussion about the sources of training data, with @tay2win hoping that emails and chat logs are not used for training AIs.
  • Misunderstandings of Model Overfitting Addressed: @aswarp clarified the concept of “regurgitating” data by AI models when @tay2win suggested overfitting could be the cause. @aswarp indicated the issue is known and occurs when models repeat bits of the training data.
  • Confusion Over Using Grok with LM Studio: In a conversation about integrating Grok with LM Studio, @pandora_box_open provided a link to Groq.com, but clarifications on what was intended led to mixed responses and corrections from @wildcat_aurora and @jedd1.
  • Seeking the Right Model Fit for VRAM and Context Size: @jason_2065 exchanged information with @heyitsyorkie and @vinni_spx about various models suitable for coding, their VRAM usage, context length, and the need for filters on Hugging Face. @jason_2065 also inquired about “mixture of experts” models, citing good speed and VRAM fit with Laser Dolphin Mixtral.

Links mentioned:

LM Studio ▷ #🧠-feedback (5 messages):

  • Seeking Non-API CURL Guidance: @newoperator inquired about making a curl request directly to an LM Studio model without using the OpenAI completion API, noting a lack of documentation. @fabguy responded that CURL can directly interact with the LM Studio server without an additional API.
  • Error Code Conundrum in LM Studio: @instamailing posted an issue with LM Studio characterized by an exit error code (-1073740791) and accompanying JSON data revealing RAM and VRAM details, but no definitive cause.
  • Navigating to the Right Support Channel: @heyitsyorkie directed @instamailing to the appropriate support channel for the issue they’re facing and advised including more information than just the error message to get better assistance.

LM Studio ▷ #🎛-hardware-discussion (114 messages🔥🔥):

  • 16GB VRAM and Debating Mac Pros: @ethanboyle noted the upper end of VRAM in consumer video cards seems to be 16GB. Following up on suggestions for MacBook Pros with Apple Silicon, concerns about non-upgradability of RAM and installing Linux on Macs were discussed, including potential issues highlighted at Debian on Apple M1 and Debian ARM port.
  • Apple’s Unified Memory Architecture in the Hot Seat: Users debated the upgradability and performance aspects of Apple’s M-series chips with unified memory (@heyitsyorkie, @nink1, @wyrath). The architecture, which lacks user-upgradable memory, was contrasted against potential future AMD APUs and CAMM memory modules.
  • Potential Challenges Running LM Studio on Integrated GPUs: @ayyouboss faced issues running LLMs on an integrated VEGA GPU in a Ryzen rig with 16GB of RAM, despite LM Studio running on the CPU. @rewire suggested a VRAM limitation might be at play, and after back-and-forth troubleshooting, they proposed trying out Windows instead of Linux due to probable driver issues.
  • Evaluating Silicon Macs for Linux and AI Use: @ethanboyle and others discussed the use of Macs with Apple silicon for AI work, bearing in mind the challenges of Linux installation and non-upgradable unified memory. Some community knowledge and external links such as Tart virtualization for Apple Silicon were shared, which @wolfspyre reported as a powerful and free tool for running Linux in containers on Mac.
  • A Costly Affair with Groq Chips: In a hardware performance and cost comparison, @nink1 and @wyrath discussed how the Groq chip architecture necessitates clustering many chips for high performance, resulting in a significant cost gap compared to Nvidia solutions. An investment in a Groq cluster to run large models could potentially reach millions of dollars.

Links mentioned:

LM Studio ▷ #🧪-beta-releases-chat (2 messages):

  • Starcoder2-15b Anticipation: User @.bambalejo inquired about when StarCoder2-15b would be available to try in LM Studio, referencing a GitHub pull request that adds support for it to llama.cpp at https://github.com/ggerganov/llama.cpp/pull/5795.
  • LM Studio Update Pending for StarCoder2 Integration: @heyitsyorkie responded that the integration of StarCoder2-15b into LM Studio will likely occur with the next beta release, once LM Studio is updated to the version of llama.cpp that supports this model.

Links mentioned:

LM Studio ▷ #autogen (4 messages):

  • Autogen Integration Troubles: User @sourguava experienced connection errors when testing the model, specifically highlighting a 401 error indicating an “Incorrect API key.” They referenced their API key struggles and provided the link to find the correct key at platform.openai.com/account/api-keys.
  • Reinstalling Autogen Might Help: In response to @sourguava, @thebest6337 suggested reinstalling autogen as a possible fix to the connection issues.
  • LM Studio May Be Experiencing Delays: @sourguava mentioned that LM Studio has been problematic with models loading very slowly, hinting at potential performance issues with the platform.
  • Docker Volume Mounting Error: @remliv attempted to follow AutoGen’s Docker installation guide but encountered an error indicating invalid characters for a local volume name when using the command docker run.
  • Windows Path Challenge with Docker: @remliv found a possible solution for the volume mounting error on StackOverflow, where it was suggested to replace $(pwd) with %cd% for Windows systems, but this led to another error stating the file could not be opened because it was not found.

Links mentioned:

  • Docker | AutoGen: Docker, an indispensable tool in modern software development, offers a compelling solution for AutoGen’s setup. Docker allows you to create consistent environments that are portable and isolated …
  • Mount current directory as a volume in Docker on Windows 10): Description I am using Docker version 1.12.5 on Windows 10 via Hyper-V and want to use container executables as commands in the current path. I built a Docker image that is running fine, but …

LM Studio ▷ #memgpt (1 messages):

triffed.: <@1211375065191682131> it exists i’m on arch i just used yay to get it

LM Studio ▷ #avx-beta (1 messages):

.tntflo: Can we get this for linux too

LM Studio ▷ #crew-ai (5 messages):

  • JavaScript compatibility inquiry: noneofya_business asked if crew ai works with JavaScript, but no additional details or responses were provided.
  • Visual Studio Code mentioned: tobitege mentioned Visual Studio Code (VSC) presumably in response to an earlier query, but the context is unclear. wolfspyre echoed the mention of Visual Studio Code and emphasized the need for clarity.
  • Seeking clarity on confusing topics: wolfspyre commented that navigating these topics can be quite confusing, highlighting a need for further explanation or guidance.
  • Exploring the construction of personalized AI agents: ccarroz inquired about experiences in building custom AI agents (defining role and mission) outside the realm of pre-built examples and the feasibility of leveraging different Language Models (LLMs) on various local devices. They shared an ambitious plan to run diverse LLMs on different hardware, including a 3090 GPU, a Jetson Orion, and a 6800XT GPU.

HuggingFace ▷ #general (121 messages🔥🔥):

  • Local Model Training Memory Woes: @chunkchampion joked about whether local model training was advisable, considering it was using up 90 gigabytes of memory.
  • Gradio Version Issues Trouble Space Deployers: Several users including @ilovesass, @cubietom, and @vipitis discuss issues with deploying spaces, suggesting the need to check for outdated Gradio versions and looking for updated components like ImageEditor.
  • Request for Guidance Ascending the AI Learning Curve: @gschwepp_84093 queried how to progress from introductory AI projects to more complex ones. User @dailafing expressed a desire for advice on the same subject, hoping an experienced member could provide insight.
  • Model Hunting for Specific Use Cases: Users like @pazanchick and @apaz sought advice and suggestions for models suitable for tasks like TTS and generating book club questions, respectively.
  • Share and Seek Opportunities in the AI Community: @dsquared70 promoted a conference in Asheville, NC for developers working with GenAI in production, while @jan_skaryna searched for a senior AI/ML developer.

Links mentioned:

HuggingFace ▷ #today-im-learning (5 messages):

  • Discovering Helix as a ‘novice-friendly’ editor: @ai_noob shared their first-time experience with the Helix editor and pointed out the availability of a comprehensive tutorial using the command helix --tutor.
  • CUDA MODE YT series in the spotlight: @iakhil is dedicating the weekend to exploring the CUDA MODE YouTube series for deeper understanding.
  • Styling Gradio with SASS: @targetdummy5623 is working on a project to swap out the default theming in Gradio by implementing styles with SASS instead of Python.
  • HuggingMod keeps the pace in check: HuggingMod reminded a user (<@500991911650394143>) to slow down their message frequency to maintain the quality of the discussion. 🤗
  • PPO theory on the study table: @0enzi mentioned delving into the theory behind Proximal Policy Optimization (PPO), hinting at deepening their understanding of reinforcement learning.

HuggingFace ▷ #cool-finds (7 messages):

  • Exploring In-The-Stack by Bigcode: User tonic_1 shared a link to In-The-Stack, a space by Bigcode on HuggingFace, and inquired about others’ experiences with it.
  • Brains, Not Computers: markplusai posted an article from The Guardian discussing the intricacies of the human brain and linked to research about manipulating memories in mice, emphasizing that we are in the thick of a significant scientific journey to understand our brains. Here’s the thought-provoking article.
  • LLaMA with Super Saiyan Strength: pacozaa found an informative Medium article about using few-shot prompts with LLaMA2 and improving its performance with assistance from Claude. The article also discusses using large language models (LLMs) to aid in creating macOS agents, which can be read in detail here.
  • Synching Lips with Pika: jacob_f97 shared a YouTube video titled “Introducing Lip Sync on Pika,” which reveals a new feature allowing the synchronization of lip movements with speech in videos on the platform. Watch the feature here.
  • Alibaba Cloud’s AI Innovation: littlehorse posted about Alibaba Cloud launching Tongyi Qianwen 2.0 and a range of industry-specific models to meet the growing generative AI demand. Read more on Alibaba Cloud’s blog.

Links mentioned:

HuggingFace ▷ #i-made-this (8 messages🔥):

  • Introducing a Quartet of Quirky Bots: @samakakreacher launched a set of specialized bots on Poe: DeepSeek Coder 33b, Mistral 0.2 32k, Proetus 0.4, and Shap-E, each with distinct abilities ranging from coding assistance to 3D modeling. An introductory image showcases the diverse functionalities of the new bot family.
  • Protein Anomaly Detection Breakthrough: @grimsqueaker highlights the publication of their paper, “Detecting Anomalous Proteins Using Deep Representations,” in NAR Genomics and Bioinformatics, featuring a combination of protein language models and anomaly detection. The research is accessible via a high-level Twitter thread and the full paper link.
  • Sampling vs. AI in Music: In episode 17 of ‘kevin makes the weirdest dataset,’ @bigdookie reflects on the copyright debates surrounding AI and traditional sampling in music, illustrating their point with musicgen continuations and Ableton in a YouTube video.
  • Transformative Model for AI: @andysingal shared their model, lora_gemma, developed with unsloth’s TRL library that promises faster training, showcased through examples and a notebook available on Hugging Face.
  • AI-Ready Kubernetes with a Module: @alextreebeard created a terraform module to transform a Kubernetes cluster into an AI environment, introducing Jupyter and Kubeflow with gitops, and considering integration of containerised GPUs. The module is available on GitHub.

Links mentioned:

HuggingFace ▷ #reading-group (67 messages🔥🔥):

  • Coordination Sympathy: @tonic_1 apologized for not making @582573083500478464’s life easier when intending to do a PR for some slides, already perfectly crafted by @582573083500478464.
  • Advances in AI Compression and Merging: @nrs9044 sparked a discussion on how improvements in compression could potentially enhance merging algorithms by identifying significant weights more efficiently. They also speculated about the implications of the success of the 1.58bit architecture on the transferability of current algorithms in both domains.
  • Reading Group Event Calendar: @chad_in_the_house responded to questions about attending the reading group, suggesting looking in the announcements/events sections for now and mentioning plans to create a Google Calendar for updates.
  • Seeking Clarification on Diffusion and Consistency Models: @riteshrm sought resources to understand the maths behind diffusion and consistency models. @chad_in_the_house recommended looking into blog posts that explain diffusion models and mentioned the Hugging Face course on the topic, providing a link here.
  • Weekend vs. Friday Reading Group Sessions: A discussion was opened by @shafi8433 proposing to hold reading group sessions on weekends rather than Fridays, spawning a back-and-forth about scheduling preferences and time zones; @lunarflu suggested weekends on Central European Time (CET).

Links mentioned:

HuggingFace ▷ #core-announcements (1 messages):

  • DreamBooth Gets an EDM Beat: @sayakpaul shared that the SDXL LoRA DreamBooth script now includes EDM-style training support. The update also introduces compatibility with the recent Playground model, enhancing the functionality of this script. Check out the pull request for details: Support EDM-style training in DreamBooth LoRA SDXL script.

Links mentioned:

Support EDM-style training in DreamBooth LoRA SDXL script by sayakpaul · Pull Request #7126 · huggingface/diffusers: Command example: CUDA_VISIBLE_DEVICES=1 accelerate launch train_dreambooth_lora_sdxl.py \ —pretrained_model_name_or_path=“playgroundai/playground-v2.5-1024px-aesthetic” \ —instance_da…

HuggingFace ▷ #diffusion-discussions (21 messages🔥):

  • Scheduler Confusion in Diffusers: _vargol encountered an issue where print(pipe.scheduler.config._class_name) was showing the incorrect scheduler class after updating it. A GitHub issue was raised (#7183), and they suggested a temporary fix by printing pipe.scheduler and pipe.scheduler._class_name for the correct values.

  • Bug Fixed in Diffusers Inheritance: After the above problem was flagged, a new pull request (#7192) was merged to correct the ‘from_config’ bug in diffusers, and @_vargol advised on how to install the patch directly from the pull request using pip.

  • Inpainting with Diffusers: @sayakpaul linked to the inpainting documentation for diffusers, prompting @tony_assi to inquire about image-to-image inpainting using an image prompt instead of text.

  • Guide to Image Prompts with IP-Adapter: In response, _homoludens shared a link to the IP-Adapter guide, which allows for image prompting in inpainting tasks.

  • How to Handle LoRA Weights in Diffusers: Enquiry by @crapthings about integrating LoRA weights into diffusers was addressed by @sayakpaul, who guided on using set_adapters() to manage multiple adapters including LoRA for image effects.

  • Handling NSFW Content on HuggingFace Hub: When @pseudoterminalx pointed out a potentially NSFW model on the HuggingFace Hub, @lunarflu instructed that the best protocol is to tag the model with ‘NFAA’ or open a report if necessary, and provided a link (pony-diffusion-v2 discussion) to address the issue.

Links mentioned:

HuggingFace ▷ #computer-vision (7 messages):

  • Curiosity Towards fireche’s Project: User @fireche expressed they could not help but showed interest in another member’s work, leading to @dillonkyle mentioning their concept about converting a georeferenced PDF of a civil engineering drawing into GIS CAD.
  • Installation Assistance Requested for xformers: @sai_nm sought help regarding the installation of xformers, but no further context or details were provided.
  • Introduction of the #Terminator Network: @alex_cool6 shared their recent work on the #Terminator network, which integrates several key technologies and also revisits concepts from the 1990s like slow-fast networks, accompanied by their paper titled “HyperZ⋅Z⋅W Operator Connects Slow-Fast Networks for Full Context Interaction” available at arXiv.org.
  • Exploring Small VLMs for Client Onboarding: @n278jm inquired about the best small Visual Language Model (VLM) to integrate into a client onboarding process for image detail extraction, mentioning they have conducted experiments in the vision arena space.
  • Feedback on VLM Experimentation Sought: Continuing the dialogue, @n278jm communicated their wish for external insights into optimizing their inputs for a small model in a rapidly evolving area, without compromising on effectiveness; @johko990 responded with uncertainty but acknowledged it might be worth exploring.

HuggingFace ▷ #NLP (15 messages🔥):

  • Medical Encyclopedia AI Needs a Consult: @dracula14. has created an encyclopedia AI using Llama 2 and ChromaDB and now seeks advice on how to query from a sqlite file containing embeddings.
  • Adam Optimizer Claims Its Throne: @nrs9044 inquires if the Adam optimizer is still considered state-of-the-art. @lavi_39761 responds by affirming its efficacy for common use and provides a link for further reading.
  • Flask vs Triton in Model Deployment Showdown: @frosty04212 asks about the best method to deploy an NLP model, and @vipitis clarifies that Flask is a web framework while Triton is a machine learning compiler, implying they serve different functions.
  • Molding LLMs to Your Needs: @onedumbdude is enthusiastic about using LLMs for tasks like running scripts and making API calls. @vipitis mentions a technique called function-calling which enables such interactions with models.
  • Inference Time Tug of War: @anna017150 experiences longer inference times with mistral-7b-instruct-v02 compared to bloomz-7b1 on identical inputs and seeks advice for improvement.

Links mentioned:

HuggingFace ▷ #diffusion-discussions (21 messages🔥):

  • Duplicate Scheduler Names Misleading: @_vargol identified a bug with diffusers where scheduler names display incorrectly, showing EulerDiscreteScheduler instead of LCMScheduler after updating the scheduler. The issue was raised on GitHub #7183, and a temporary fix involves using explicit print statements to confirm the correct scheduler class.

  • Bug Fix for Scheduler Misnaming: @sayakpaul shared a GitHub pull request #7192 by yiyixuxu aimed at fixing the scheduler class-naming bug in diffusers. The pull request contains the corrected code for the scheduler issue.

  • How-to for Image-Inpainting with Diffusers: @sayakpaul referenced a guide on image inpainting using Hugging Face’s 🤗 Diffusers, which relies on masks to define the regions to edit. @tony_assi inquired about image-to-image inpainting, to which _homoludens provided additional resources on IP-Adapter for guidance.

  • Installation Directly from Pull Request: In response to @luihis, _vargol suggested a way to install updates directly from a GitHub pull request using the command pip install -U git+https://github.com/huggingface/diffusers@refs/pull/7192/head. This approach allows for upgrading to the latest version even before it’s officially released on PyPi.

  • Confusion Over Setting LoRa Weights: @crapthings asked about implementing specific LoRa weights in diffusers and @sayakpaul provided a solution using set_adapters() from the PEFT guide, allowing to combine and manage adapters for generating unique image effects using LoRAs.

  • Handling NSFW Generation Models on Hugging Face: @pseudoterminalx pointed out the presence of NSFW generative models, prompting @lunarflu to suggest opening a PR to add an NFAA tag and report if necessary. The discussion continued regarding the resolution on AstraliteHeart’s v2 discussion thread #7.

Links mentioned:

LAION ▷ #general (238 messages🔥🔥):

  • Discussions on Model Performance and Training Techniques: Members shared insights on the importance of proper model training, with @thejonasbrothers highlighting challenges with models like Pony and the limitations of NLP understanding. Meanwhile, @pseudoterminalx expressed skepticism about some training approaches and the belief that significant compute scale is not the core issue for certain models. The conversation touched on the peculiarities of finetuning models like Stable Diffusion 2.1, exploring techniques like bias-only training and low-rank methods. A comparison between different finetuning processes and their results on image coherence was debated, with references to academic papers on the subject.

  • Discussions on AI Generated Music and Vocal Quality: Chat participants discussed the quality of vocal synthesis in models like Suno, lamenting the metallic and identical qualities in the voices it produces (@pseudoterminalx). Others discussed the potential of models like Mistral and MusicLM for specific applications, while showing concern over the open-source practices of startups and the desire for improved music generation models. Focus shifted to leveraging intelligently designed backing tracks that can adapt to live play (@top_walk_town), and the anticipation for innovations such as YouTube humming to MIDI conversion (@metal63).

  • Exploring AI-Generated Art and Issues with Data Sets: The conversation touched on the limitations and challenges related to current models dealing with transparency and distinctiveness in AI-generated art (@pseudoterminalx, @chad_in_the_house, @metal63). Sketches of historical data management woes were shared, with @pseudoterminalx recounting a case from 2009 at a university involving a mail server with an 11-day backup cycle plagued by outdated policies and lack of downtime planning. Comparative discussions ensued about the aesthetic output of models like Pony diffusion, including prompts involving characters from different franchises (@thejonasbrothers).

  • Technical and Ethical Challenges of AI Research and Sharing: The chats highlight the importance of understanding technical details, such as tokenization (@pseudoterminalx), and the moral issues surrounding dataset handling (@.undeleted). A pervasive sense of frustration about Twitter’s limitations as a medium for AI dialogue was voiced.

  • Integration of Personal Motivation and Value in AI: One user, @metal63, conveyed a personal testament to the life-saving value they found in AI models like Pony, sparking a discussion around subjective value, utility, and the promotion of such models in the AI community. The conversations also encompassed the broader implications of access to and interactions with AI technologies on individual well-being.

Links mentioned:

LAION ▷ #research (11 messages🔥):

  • The Advent of Terminator Network: @alex_cool6 announced their recent work on the #Terminator network which combines past technologies like ResNet and Self-Attention with concepts from the 1990s like slow-fast networks. They shared a research paper detailing the HyperZ⋅Z⋅W Operator used for full context interaction.
  • Claude 3 Model Buzz: @vrus0188 reported being inundated with mentions of the Claude 3 Model and provided a Reddit link discussing benchmarks related to the model’s performance and the singularity.
  • Comparing Claude 3 to GPT-4: @segmentationfault8268 tested the Claude 3 model and found it superior to GPT-4 in terms of not being lazy and having better understanding, which might lead them to cancel their ChatGPT Plus subscription if this continues to be confirmed.
  • Challenges with PyTorch CUDA Kernels: @twoabove commented on the lack of improvement in Claude 3’s handling of non-common tasks, specifically mentioning PyTorch CUDA kernels as an area where the model still exhibits laziness.
  • The Sonnet is a VLM: @jh0482 entered the conversation noting that Sonnet is classified as a Visual Language Model (VLM) by the bedrock, sparking curiosity about how it stacks up against GPT4v and CogVLM.

Links mentioned:

Reddit - Dive into anything: no description found

LAION ▷ #learning-ml (1 messages):

  • Call for Collaboration on DPO Refinement: User @huunguyen is considering a minor refinement to DPO (Dynamic Programming Optimizer) and is seeking assistance. They have asked interested parties to reach out via direct message.

CUDA MODE ▷ #general (21 messages🔥):

  • Seeking Live Chat Logs: User @le_tech inquired about the location of the previous day’s live chat discussion. @marksaroufim responded with instructions to navigate to the “reading group stage” and click the chat button in the top right of the Discord app.
  • Verification Flash: User @umerha questioned how long it takes to verify on lightning.ai using Gmail, only to find that the verification process was swiftly completed.
  • Wroclaw’s Shashlik Surprise: In a light-hearted exchange, @andreaskoepf shared details and a link to “CUDA NA KIJU,” a renowned grill bar in Wroclaw, clarifying it was not related to the user @umerha’s mention of Münster.
  • Call for GenAI Integration Insights: @dsquared70 announced a conference in Asheville, NC focused on GenAI in production environments, inviting developers to submit papers and presentations through their website.
  • Recording Rerun Required: @marksaroufim stated an intention to upload a recording from a previous session to the channel, but later found out the recording was corrupted. The user planned to redo the recording that evening, indicating a general need for backup recordings, as suggested by _t_vi_.

Links mentioned:

CUDA MODE ▷ #triton (11 messages🔥):

  • Python’s pass keyword discussion: @iron_bound wondered about using pass in Python functions, linking to the unsloth repository. @apaz noted that pass is a no-op and often a readability preference for those from curly-bracket languages, while @andreaskoepf suggested it could act as an “end of block” marker.

  • Bytecode confirmation for Python ‘pass’: In response to @iron_bound’s interest in benchmarking the use of pass, @apaz recommended checking the bytecode for any differences using import dis; dis.dis(fn).

  • Triton vs. CUDA performance query: @piotr.mazurek inquired about the performance differences between kernels written in Triton versus CUDA and whether there is a difference in compiled PTX output. @andreaskoepf clarified the compilation process and likened Triton to NVCC, with a GitHub link to support it.

  • Triton community meetup video share: @andreaskoepf shared a YouTube video titled “Triton Feb community meetup 20240220”, featuring Triton’s February community meetup.

Links mentioned:

CUDA MODE ▷ #cuda (113 messages🔥🔥):

  • Exploring VRAM as Swap Space: @nat.42 found resources indicating the possibility of using Linux VRAM as a swap file with links to vramfs on GitHub and ArchLinux documentation. They suggest VRAM might be faster than disk paging, but recognize that demand for VRAM could complicate its use as swap.

  • GPU Accelerated Databases: @iron_bound jokingly proposed running databases on GPUs, which sparked a conversation about the existing cuDF library for GPU DataFrame manipulation. @vim410 confirmed the serious potential for GPU-accelerated databases and mentioned efforts towards realizing this, including a past ZDNet article highlighted by @jeremyhoward.

  • CUDA Programming Challenges and Solutions: Members, including @zippika and @morousg, discussed the complexities of CUDA programming, cache utilization, and performance of different GPUs like the NVIDIA A100 and 4090 models. @vim410 recommended looking into CUTE within the CUTLASS stack for addressing programming complexities, offering to connect feedback directly to CUTLASS developers.

  • Hopper’s Specialty in Async Operations: @zippika investigated Hopper architecture’s asynchronous matrix multiplication and its impact on performance noting that while hopper boasts async matmuls, the 4090 only supports async loads and stores, which could impact how operations are optimized.

  • Mistral’s Scale of Operations Debated: A discussion emerged around Mistral’s computing resources where @andreaskoepf and others talked about the reported 1.5k H100 GPUs, with skepticism about their sufficiency for large-scale model training in comparison to industry giants. It included links to social media posts and references to academic papers to provide context on Mistral’s strategies and capabilities.

Links mentioned:

CUDA MODE ▷ #torch (3 messages):

  • New PyTorch Dev Podcast Episode Alert: @andreaskoepf shared a link to a new episode of the PyTorch Developer Podcast discussing AoTInductor.
  • Troubleshooting CUDA Kernel for Histograms: @srns27 is seeking help with a CUDA kernel they’ve written; the intended function is to create a parallel histogram, but they’re experiencing inconsistent results with gpuAtomicAdd. They question why atomicAdd is not functioning correctly within their kernel code.
  • Podcast Enthusiasm Shared: @ericauld expressed enjoyment of the new PyTorch Developer Podcast episodes, appreciating their concise format.

Links mentioned:

no title found: no description found

CUDA MODE ▷ #announcements (1 messages):

  • Tune in for CUDA Gotchas: @andreaskoepf alerted @everyone that CUDA-MODE Lecture 8: CUDA performance gotchas is starting soon, promising tips on maximising occupancy, coalescing memory accesses, and minimizing control divergence with live demos included. The lecture is scheduled for <t:1709409600:t>.

CUDA MODE ▷ #suggestions (5 messages):

  • Shrinking SRAM Discussed on Asianometry: User @iron_bound shared a YouTube video titled “Can SRAM Keep Shrinking?” from Asianometry, along with several related links in the video description including a newsletter and Patreon.
  • Praise for Asianometry’s Insightful Content: @apaz praised the Asianometry channel, recommending it after following the content for about a year.
  • CUDA Programming Resource Shared: @ttuurrkkii. posted a GitHub repository link as a helpful resource for beginners in CUDA parallel programming and GPUs.
  • Video Walkthrough of Building GPT: Another contribution from @iron_bound was a YouTube video explaining how to build a GPT model, following important papers and techniques from OpenAI’s research.

Links mentioned:

CUDA MODE ▷ #jobs (4 messages):

  • Join Lamini AI’s Mission to Democratize Generative AI: @muhtasham shared an opportunity with Lamini AI which is seeking HPC Engineers to optimize LLMs on AMD GPUs, noting the company’s commitment to diversity and equal employment. Find out more about the role, which involves working with MPI, ROCe, UCX, and OpenAI Triton, by visiting the job posting at Lamini AI Careers.

  • Quadrature Seeks GPU Optimization Engineer: @d2y.dx2 highlighted an opening at Quadrature for an engineer specialized in optimizing AI workloads on GPUs in either London or New York. Explore the details of this position where you can be part of a research-driven firm and make an impact on global financial markets at Quadrature Careers.

Links mentioned:

CUDA MODE ▷ #beginner (11 messages🔥):

  • CUDA Troubles in Google Colab: User @ttuurrkkii. expressed difficulties in making CUDA work in Google Colab despite following tutorials. @andreaskoepf responded by asking if the Nvidia GPU (A100 or V100) was selected and suggested checking with the !nvidia-smi command.

  • Lightning AI to the Rescue?: In helping @ttuurrkkii., @andreaskoepf recommended trying out Lightning AI studios as a potential solution for CUDA issues on Google Colab.

  • Setting Up CUDA on Kaggle: User ._bob_ mentioned the need to set up CUDA on Kaggle for working with multi-GPU environments. No further details or replies were given in the posted messages.

  • C or CPP for CUDA and Triton?: @pyro99x inquired about the necessity of knowing low-level languages like C or C++ for working with Triton and CUDA. @briggers clarified that while CUDA requires such knowledge, Triton does not, albeit an understanding of lower-level concepts would be beneficial.

  • Triton for Performance Maximization: Following up on the discussion, @briggers suggested that if someone has mastered performance at the Torch/System/nsys level, Triton could be a worthwhile next step to enhance performance.

  • C from Python in CUDA-Mode: To address @pyro99x’s query about Python-friendly ways to work with Triton and CUDA @jeremyhoward mentioned that his CUDA-Mode videos demonstrate how to auto-generate most of the C code from Python.

  • How to Install Cutlass Package: @umerha asked about how to install and include the CUTLASS C++ package, seeking an equivalent of pip install. @andreaskoepf confirmed that the user needs to clone the CUTLASS repo and include the include directory in their project’s path, as CUTLASS is a header-only template library.

Links mentioned:

CUDA MODE ▷ #youtube-recordings (5 messages):

  • Lecture 8 Redux Hits YouTube: @marksaroufim shared a lecture titled CUDA Performance Checklist on YouTube, including the code samples and slides.
  • Gratitude for Re-recording: @andreaskoepf and @ericauld expressed their thanks to @marksaroufim for the time and effort taken to re-record Lecture 8.
  • Rerecording Takes Time: @marksaroufim mentioned the surprise that re-recording the lecture still took 1.5 hours, though it resulted in a clearer presentation.
  • Community Appreciation: @iron_bound also chimed in with thanks for @marksaroufim’s dedicated efforts, punctuated by a celebratory emoji: 🎉.

Links mentioned:

Lecture 8: CUDA Performance Checklist: Code https://github.com/cuda-mode/lectures/tree/main/lecture8Slides https://docs.google.com/presentation/d/1cvVpf3ChFFiY4Kf25S4e4sPY6Y5uRUO-X-A4nJ7IhFE/edit

CUDA MODE ▷ #ring-attention (53 messages🔥):

  • Ring Attention in the Spotlight: @andreaskoepf highlighted a discussion about Ring Attention and Striped Attention on the YK Discord, referencing a link shared by @ykilcher. The discussion can be followed using this link and by joining the Yannic Kilcher Discord server.
  • Exploring Flash Decoding for LLMs: @andreaskoepf expressed interest in trying out Flash Decoding, a method for improving inference efficiency in Large Language Models (LLMs), directing to the Together.ai blog post for more information.
  • Diving into Flash-Decoding and Ring Attention Implementation: @iron_bound and @andreaskoepf delved into the specifics of Flash-Decoding, discussing steps like log-sum-exp, references in the code, and comparing to solutions such as softmax_lse, which they located in GitHub repositories ring-flash-attention and flash-attention.
  • Clarifying Flash-Decoding Details: Discussions by @apaz, @nshepperd, and @andreaskoepf elaborated on the workings of Flash Attention and its return of LogSumExp (lse) values for blockwise attention operation, referencing code and providing explanations for its implementation found here.
  • Collaborative Development and Impromptu Meetups: @andreaskoepf signaled readiness to implement initial Ring-Llama tests, signaling a later arrival due to family commitments, while users like @ericauld and @iron_bound coordinated their participation in voice chats for collaboration and provided insights on their progress.

Links mentioned:

LlamaIndex ▷ #blog (5 messages):

  • Introducing RAPTOR for Advanced RAG: LlamaIndex introduced RAPTOR, a new tree-structured technique for Retrieval-Augmented Generation (RAG), designed to address the limitations of naive top-k RAG in retrieving higher-level context details. It promises better handling of questions over specific facts in a document as Tweeted here.

  • Showcasing RAG in Real-world Applications: A new LlamaIndex webinar showcased projects utilizing RAG in practical applications, including an innovative GAI-powered ADU planner to streamline the process of adding accessory dwelling units, as detailed in their latest Tweet.

  • Build RAG with LlamaIndex + MongoDB: @AlakeRichmond developed a reference architecture using @MongoDB Atlas for data indexing, which LlamaIndex highlighted for its strong emphasis on proper data preparation. This guide is pivotal for those wanting to build RAG systems with MongoDB, as discussed in the shared Twitter post.

  • Semantic Chunking for Enhanced RAG: Florian June’s post on semantic chunking was featured by LlamaIndex as a comprehensive guide promising better retrieval and synthesis for RAG, by grouping semantically similar information. Find out more about this method in their Tweet.

  • Claude 3 Released with Day 0 Support from LlamaIndex: @Llama_Index announces the release of Claude 3 with three variations, including Claude Opus, which claims to surpass GPT-4’s performance. LlamaIndex is ready to integrate this new model, as declared in their enthusiastic announcement.

Links mentioned:

ADU Planner: Revolutionize the ADU construction process with our GAI-powered ADU planner, a brand new solution to provide effortless design, local compliance, and quick supplier connections in one click.

LlamaIndex ▷ #general (178 messages🔥🔥):

  • Local ReacAgents with ollama Trials: @impactframes. shared difficulties in making local ReacAgents work with ollama, while @whitefang_jr suggested verifying if the LLM was deployed and hosted using ollama settings. The conversation evolved around possible deployment issues and configuration setups with @cheesyfishes highlighting that structured output can be challenging for open-source models.
  • ICLR 2024 Papers Prompt Pain Points: @antelope6345 needed a way to query ICLR 2024 papers and faced challenges with certain code examples provided, while @cheesyfishes suggested using a vector index and a sub question query engine or a document summary index for more efficient results.
  • Hints for Hybrid Vector and Keyword Searches: @valu_ inquired about searching for similarity among an array of questions. @cheesyfishes provided advice on setting up hybrid search combining vector and keyword searching, and guided to resources including setting up with Qdrant, Weaviate, or custom BM25 implementations.
  • API Documentation Structure Suggestions: User @tusharganguli raised concerns about the structure of API reference documentation. @cheesyfishes acknowledged that the API reference docs have been neglected but mentioned an upcoming major upgrade.
  • Llama Index Discord Praised: @.tarpus expressed frustration about recent changes in OpenAI’s API, which required updates to their code. They commented that the Llama Index Discord community was more organized and helpful than others.

Links mentioned:

LlamaIndex ▷ #ai-discussion (1 messages):

  • Integration of LlamaIndex with LongContext: @andysingal shared a link discussing the Empowering Long Context RAG through the integration of LlamaIndex with LongContext. The article highlights the release of Google’s Gemini 1.5 Pro with a 1M context window and its potential integration here.

Links mentioned:

Empowering Long Context RAG: The Integration of LlamaIndex with LongContext: Ankush k Singal

OpenRouter (Alex Atallah) ▷ #announcements (1 messages):

  • Claude 3.0 Drops Today: @alexatallah announced that Claude 3 is being released on OpenRouter, including an experimental self-moderated version. The community’s anticipation is finally being met with this latest update.

OpenRouter (Alex Atallah) ▷ #app-showcase (1 messages):

  • LLM Challenge by Leumon: @leumon has set up a server for a fun and educational game that tries to trick GPT3.5 into revealing a secret key. The game highlights the importance of treating AI output cautiously and ensuring there are additional safety measures when dealing with confidential information. The concept was originated by @h43z and has been refined by @leumon with new prompts.
  • Free Conversations with Diverse AIs: Alongside the challenge, @leumon’s server allows users to chat with various AI models like Claude-v1, Gemini Pro, Mixtral, Dolphin, and Yi for free using the openrouter API. This provides a unique opportunity to explore different LLMs’ capabilities and responses.

Links mentioned:

Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

OpenRouter (Alex Atallah) ▷ #general (96 messages🔥🔥):

  • Claude-3 Access and Discussion: @justjumper_ expressed eagerness for Claude3 access shortly after its launch. @louisgv confirmed that all Claude 3 versions were being added, with a special note that the “experimental” version would also go live, while @arsoban shared that in their tests, Claude3 Opus demonstrated greater text comprehension than GPT-4.

  • OpenAI vs Claude Pricing: Members @oti5 and @voidlunaa debated the seemingly high pricing of Anthropic’s Claude 3 compared to GPT-4, with particular perplexity about the cost jump from Claude-3-Sonnet to Claude-3-Opus.

  • Claude Performance and Availability: The performance of Claude 3 variants were discussed, with @arsoban suggesting in some tests that Sonnet outperforms Opus and offering to share their insights in a voice chat. @alexatallah reassured @billbear that Claude 3 was on the way and that the “experimental” version would be available as well.

  • Testing Claude’s Abilities: Users @arsoban and @you.wish planned to conduct Claude 3 tests for English-to-code translation, particularly in the context of game development, despite @arsoban not having a game engine installed for practical implementation.

  • Deteriorating Model Performance over Time: @capitaindave observed a potential decrease in the reasoning capabilities of Gemini Ultra compared to its performance at launch, with the AI exhibiting a stronger pretense of coherence than actual substance.

Links mentioned:

LLM Perf Enthusiasts AI ▷ #general (1 messages):

  • Big News from OpenAI: User @jeffreyw128 excitedly announced that OpenAI has released a browsing feature similar to Gemini/Perplexity. Here’s the tweet with the announcement.

LLM Perf Enthusiasts AI ▷ #claude (71 messages🔥🔥):

  • Claude 3 Takes on GPT-4: Enthusiasts in the #claude channel are abuzz with anticipation over the new Claude 3 model family, which @res6969 claims to outperform GPT-4, especially in math and code tasks.
  • Debating Cost-Effectiveness: While users like @pantsforbirds and @emfastic grapple over the cost of Claude 3 in comparison to GPT-4, with suggestions that pricing might update in the coming months as per @res6969, many remain interested despite the pricing concerns.
  • Synthetic Data Generation: User @edencoder floats the idea that Claude 3’s edge might lie in synthetic data generation, considering the higher cost justified for a model that offers significantly better production rate limits.
  • Anticipation for the Haiku Model: Discussions by @potrock and @pantsforbirds express intrigue about the yet-to-be-released Haiku model, which impresses with its competitive pricing and potential in human eval.
  • Operational Efficiency Queries: @res6969 shares non-scientific team experiments, highlighting Claude 3’s latency performance with a first token response of about 4 seconds and full response times in seconds, indicating the practical operational efficiency experienced by users.

Links mentioned:

  • Introducing the next generation of Claude: Today, we’re announcing the Claude 3 model family, which sets new industry benchmarks across a wide range of cognitive tasks. The family includes three state-of-the-art models in ascending order …
  • Tweet from Anthropic (@AnthropicAI): With this release, users can opt for the ideal combination of intelligence, speed, and cost to suit their use case. Opus, our most intelligent model, achieves near-human comprehension capabilities. I…
  • Model & API Providers Analysis | Artificial Analysis: Comparison and analysis of AI models and API hosting providers. Independent benchmarks across key metrics including quality, price, performance and speed (throughput & latency).

LLM Perf Enthusiasts AI ▷ #embeddings (10 messages🔥):

  • In Search of Cost-Effective Embedding Inference: User @iyevenko inquired about the most cost-effective options for running embedding models, aiming for about 100 inferences per second in a production environment.
  • Vector Database Recommendations Gathered: @iyevenko also showed interest in vector database recommendations and @yikesawjeez suggested databases like Qdrant for speed and Weaviate for hybrid queries, also mentioning pgvector for those familiar with PostgreSQL.
  • Cloud vs Bare Metal for Cost-Effectiveness: @yikesawjeez differentiated between cost-effective solutions on cloud infrastructure versus bare metal, implying that different environments might influence the decision.
  • OpenAI’s Embedding Models Considered Cheap: @iyevenko determined that after calculations, OpenAI’s solutions seemed fairly inexpensive and was considering them for cloud infrastructure solutions.
  • Evaluating OpenAI’s Improved Embeddings: @iyevenko expressed concerns about the quality of embddings in the past but was open to reassessing, especially after @yikesawjeez suggested the newer releases might be worth checking out.

Interconnects (Nathan Lambert) ▷ #news (43 messages🔥):

  • Philpax Dives into RLHF and AI Drama: @philpax shared a YouTube video interview featuring Louis Castricato of Synth Labs, Eleuther AI discussing RLHF, Gemini Drama, DPO, and Carper AI.
  • Anthropic Announces New AI Models: @xeophon. posted about AnthropicAI’s announcement of Claude 3, its next generation of AI models, including Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku that claim to set new benchmarks in AI performance.
  • Claude 3 Model Specifications Revealed: @xeophon. mentioned Claude 3 model specifications including image inputs and a 200K context size at launch which is “up to 1M capable” and boasts efficiency improvements over GPT-4.
  • The Launch of Claude 3 Models API: @xeophon. shared that AnthropicAI’s Claude 3 Opus and Sonnet models are now available through their API, and that Haiku will be released soon, also noting that the EU can now access base Claude without a VPN.
  • Reactions to Claude’s Performance: Various users like @sid221134224 and @canadagoose1 express amazement at Claude 3, comparing it favorably to GPT-4 and discussing the potential of AI models that lack access to proprietary data sets.

Links mentioned:

Interconnects (Nathan Lambert) ▷ #ml-drama (6 messages):

  • Claude 3 Incites Questionable Tweets: @natolambert indicated that the release of Claude 3 has resulted in problematic tweets emerging, which they summarize with “guys Q* tweets are coming out due to claude 3.”
  • Frustration Over User Responses: Expressing frustration, @natolambert describes the situation as “so bad” in reaction to the quality of discourse following Claude 3’s release.
  • Direct Approach to Misinformation: In response to misinformed tweets related to Claude 3, @natolambert mentions taking a direct approach by replying with “you’re being dumb”.
  • Expectations of Sock Puppetry: xeophon. humorously misunderstands @natolambert’s direct replies as something an alternate account (“alt”) might be used for, suggesting a sarcastic strategy for engagement.
  • No Alts, Just Effort: Clarifying the decision not to use an alternate account, @natolambert admits to the disinclination by saying “Too lazy to use the alt” and “Too high activation energy”.

Interconnects (Nathan Lambert) ▷ #random (24 messages🔥):

  • A Cinematic Take on AI: @natolambert expresses enthusiasm for the film Her, contemplating the creation of a mock trailer for an imaginary OpenAI project that mimics the movie’s theme.
  • Seeking a Video Editing Partner: @natolambert is on the lookout for someone with video editing skills to collaborate on a trailer project, potentially related to the previously mentioned Her-inspired idea.
  • Content Anticipation and Hugging Face Buzz: @natolambert hints at some interesting content coming up this week and reveals that the CTO of Hugging Face, Julien, might join the Discord, becoming a new paid supporter of the podcast.
  • Engagement on Open Source AI Discussion: @xeophon. brings attention to a tweet by @OfficialLoganK, leading to a series of reflections by @natolambert and @mike.lambert on OpenAI’s stance on open source AI and its implications.
  • Learning and Discussing Julia Language: After @natolambert inquires about JuliaLang, @sid221134224 provides a detailed overview and link (https://julialang.org/) to resources associated with the Julia programming language.

Links mentioned:

Interconnects (Nathan Lambert) ▷ #memes (1 messages):

natolambert: TBT this was the best meme day

Interconnects (Nathan Lambert) ▷ #rl (5 messages):

  • Foundation Model for Reinforcement Learning: @sid221134224 shared a Twitter link to a new paper about a foundation model for RL that trains a policy conditioned on the embedding of the reward function, enabling generalization to new reward functions at test time.
  • Nat’s Next Interview Target: @natolambert expressed interest in interviewing Sergey, possibly in relation to the discussed foundation model for RL.
  • Cohere’s PPO Paper Discussion: @vj256 asked about additional data or replication studies supporting the Cohere paper’s premise that corrections of PPO are not needed for LLMs due to their stability.
  • Search for Independent Verification: After inquiring about replication by other groups, @vj256 showed a continued interest in verifying the findings of the Cohere paper independently.
  • Insight on PPO Corrections for LLMs: @natolambert mentioned that <@304671004599255043> had knowledge related to the lack of need for PPO corrections in LLMs for months, a topic covered in a recently released interview.

LangChain AI ▷ #general (56 messages🔥🔥):

  • Exploring SharePoint Data Ingestion: @rajib2189 mentioned success in loading data from a PDF folder on SharePoint, and shared a YouTube video that demonstrates extracting document content from SharePoint using Langchain. More details can be found in the Langchain documentation regarding Microsoft SharePoint integration.

  • Langchain Implementation Query: @tawsif2781 is trying to pass a dictionary directly to a RunnablePassthrough in Langchain, aiming to avoid a “stuff” key and maintain a specific dictionary structure for their use case. They seek advice on modifying the chain to achieve this.

  • Choosing the Right Tech Stack for Scalable LLM Web App: In response to @thebeast3326’s inquiry, @sharrajesh suggested a tech stack including Python 3.11, FastAPI, Langchain, and others for a scalable LLM web application, while @lhc1921 recommended Next.js with Langchain.js hosted on Vercel.

  • Discussions on Langchain’s Production Readiness: @buzzoo123 and @mintier discussed concerns about Langchain’s stability and customization for commercial use, recognizing its benefits for high-level understanding and hobby projects but opting to write custom code for production purposes.

  • Questions Regarding Anthropic’s Claude 3 Models: @dclarktandem inquired about using the new Claude 3 models via Langchain, and after some confusion, @.bagatur clarified the correct package and model string to use ("claude-3-opus-20240229") and provided relevant code snippets and links to Anthropic’s integration in Langchain docs.

Links mentioned:

LangChain AI ▷ #langserve (5 messages):

  • Seeking LLM Web App Stack Advice: User @thebeast3326 inquired about the appropriate tech stack for building a scalable llm (language learning model) web app, but no recommendations or follow-up discussions were provided in the channel.
  • Exploring .docx File Creation with Langserve: @yoangab questioned whether Langserve is capable of returning a .docx file created by a runnable; however, details on whether this functionality exists or how it can be achieved were not discussed.
  • Cache Conundrums with Langserve: @kandiesky is experiencing issues with Langserve not utilizing their LLM cache for requests, even though they are following the langchain cache (set_llm_cache) documentation, and mentioned that In Memory Cache doesn’t work either; no solution or responses have been provided on the thread.
  • Spam Alert: @teitei40 posted a message that appears as a spam link promising $50 for Steam, accompanied by a nonsensical text with various random words and a link (https://u.to/BkNtIA); users should exercise caution as it seems unrelated and potentially malicious.

Links mentioned:

21 YEARS TOGETHER Get a $50 gift card!: Steam is the ultimate destination for playing, discussing, and creating games.

LangChain AI ▷ #langchain-templates (2 messages):

  • Suspicious Steam Gift Link Alert: User @teitei40 shared a link purportedly offering a $50 Steam gift (steamcommunity.com/gift/7584903) and tagged @everyone. Due to the nature of the message and link, users should exercise caution.

LangChain AI ▷ #share-your-work (9 messages🔥):

  • Chat with YouTube Videos through Devscribe AI: @deadmanabir along with @Faisal introduced Devscribe AI, a GEN AI project to chat with YouTube videos and get summaries and key concepts without watching the entire content. They highlighted features like pre-generated summaries, video organization, and contextual video chat, provided a video demo and the project link, and requested feedback and sharing on LinkedIn and Twitter.

  • Generative AI Enhancing Asset-Liability Management: @solo78 shared a post on Medium discussing the role of generative AI in revolutionizing asset-liability management in the life insurance industry, detailing the potential benefits and including a link to the article.

  • Feynman Technique for Efficient Learning: @shving90 shared a Twitter thread from @OranAITech about adopting the Feynman Technique with their latest flow, aiming to help users articulate their understanding of concepts.

  • Introducing Free API Service with Galaxy AI: @white_d3vil announced the launch of Galaxy AI, providing free API service for premium AI models, including GPT-4, GPT-4-1106-PREVIEW, and GPT-3.5-turbo-1106. Users are invited to try it out and integrate it into their projects but no links were provided.

  • Release of Next.js 14+ Starter Template: @anayatk released a Next.js 14+ starter template with several modern development tools and shared the GitHub Template link.

  • Blog on Building Real-Time RAG with LangChain: @hkdulay shared a post detailing the construction of Real-Time Retrieval-Augmented Generation (RAG) using LangChain, aiming to enhance the response accuracy from large language models by citing sources and provided a link to the blog.

  • Exploring Advanced Indexing in RAG Series: @tailwind8960 discussed the intricacies of indexing in retrieval-augmented generation and shared insights on avoiding inaccuracies or hallucinations in responses, with a link to the conversation.

  • Duplicate Message about Steam Gift: @teitei40 posted twice about a $50 Steam gift, providing a link for redemption but no additional context was given.

Links mentioned:

LangChain AI ▷ #tutorials (3 messages):

  • Let’s Decode the Tokenizer: @lhc1921 shared a YouTube video titled “Let’s build the GPT Tokenizer,” which delves into the creation of a tokenizer, essential for translating between strings and tokens in Large Language Models (LLMs).
  • Questionable Steam Gift Link: User @teitei40 posted a link apparently offering $50 for Steam, but the URL (https://u.to/BkNtIA) appears dubious and is followed by seemingly random text, prompting concerns about legitimacy.

Links mentioned:

Latent Space ▷ #ai-general-chat (51 messages🔥):

  • Google Sharpens AI with Stack Overflow: User @mjng93 shared a TechCrunch article announcing Stack Overflow’s new OverflowAPI, which Google will use to enhance Gemini for Google Cloud. The partnerships aim to integrate validated Stack Overflow answers directly into the Google Cloud console.

  • Sergey Brin Spotlights Google’s Gemini: User @swyxio created excitement by sharing a tweet featuring Sergey Brin discussing Google’s artificial intelligence potentially reaching AGI via initiatives like Gemini.

  • Innovative AI Reflections in Photoshop: @swyxio demonstrated the creative potential of Stable Diffusion by sharing a LayerDiffusion GitHub repository that allows users to photoshop items into scenes with realistic reflections.

  • Claude 3 Model Announcements Cause Stir: Users discussed the launch of Anthropic’s Claude 3 model family; @jreddy shared the announcement, while users like @guardiang and @thenoahhein discussed its impact and performance with comparisons to existing models, including head-to-head summaries and observations of increased metadata awareness in Claude 3 (source tweet).

  • Concern Over India’s AI Deployment Regulation: User @swyxio highlighted a tweet by Martin Casado expressing concerns over India’s requirement for government approval before deploying AI models, sparking debates about potential governmental oversight and innovation impacts.

Links mentioned:

OpenAccess AI Collective (axolotl) ▷ #general (22 messages🔥):

  • In Search of Gemma Insights: User @drewskidang_82747 queried about successes with Gemma, but no further discussion or details were provided.
  • PC Build Comedy on Reddit: @yamashi shared a link to a Reddit post featuring a humorous setup of maxed-out PCIe slots and later expressed amusement with a simple message: “i am wheezing”.
  • Nvidia Nemo Megatron Tools: @le_mess posted a link to the Nvidia NeMo-Megatron-Launcher asking if anyone had experience with it, accompanied by a GitHub URL.
  • Model Merging Techniques and Utilities: @yamashi inquired about creating Mixture of Experts (MoE) models from smaller models, @dreamgen replied with a suggestion to look into mergekit on GitHub for tools related to merging pretrained language models.
  • A Discussion on LoRA and DoRA: @stoicbatman initiated talk about comparing LoRA with DORA, with @nruaif and @dreamgen joining in to discuss implementations and share additional research, including an arXiv link to the DoRA paper that outlines a novel finetuning approach.

Links mentioned:

OpenAccess AI Collective (axolotl) ▷ #axolotl-dev (12 messages🔥):

  • Hugging Face Kerfuffle Resolved: @giftedgummybee mentioned that the issue with the Hugging Face KTO was resolved after realizing there was a mix-up with the git commit version being used.
  • Axolotl Port to Tinygrad Unlikely: In response to @realmrfakename’s inquiry, @nanobitz confirmed that there are no current plans to port Axolotl to Tinygrad, as the project relies on the Hugging Face transformers library.
  • Padding Token Conundrum: @realmrfakename asked about adding a padding token to a model from a config, and shared a ValueError regarding the absence of a padding token in the tokenizer.
  • Channel Etiquette Reminder: @nanobitz advised @realmrfakename to keep configuration and error-related questions in a different, more appropriate help channel.

OpenAccess AI Collective (axolotl) ▷ #general-help (6 messages):

  • Optuna CLI Feature Suggestion: User @casper_ai highlighted the need for a CLI tool for hyperparameter optimization with optuna in axolotl, referring to GitHub issue #1356.
  • Python Version Causes GPU Woes: @dreamgen mentioned a discovery by a user that a python versus python3 conflict was preventing GPU usage, with no mention of the resolver’s handle.
  • Missing Tokenizer File in Axolotl: @dreamgen reported a critical issue where Axolotl is not saving tokenizer.json, but provided no further details or solutions.
  • Deepseed Configuration Troubles: User @c.gato resolved a GPU issue caused by Deepseed in Axolotl’s configuration, pointed out after @dreamgen mentioned a python vs python3 issue, but they did not disclose how it was resolved.
  • Deepspeed Save Glitch Reported: @nanobitz brought up a recent problem with deepspeed’s final save, which required them to revert to the last checkpoint, corroborating that this glitch is observed by others. In contrast, @rtyax confirmed that deepspeed zero 3 final save functioned correctly for them two days ago, on deepspeed 0.13.4.

Links mentioned:

Hyperparameter optimization CLI · Issue #1356 · OpenAccess-AI-Collective/axolotl: ⚠️ Please check that this feature request hasn’t been suggested before. I searched previous Ideas in Discussions didn’t find any similar feature requests. I searched previous Issues didn’t…

OpenAccess AI Collective (axolotl) ▷ #community-showcase (4 messages):

  • Mixtral vs. Mistral Large Enigma: @dctanner inquired about the performance differences between Mixtral and Mistral Large for synthetic data generation, pondering on the potential cost-effectiveness of the latter.
  • Personal Models Triumph Over Mixtral: @le_mess noted that they only briefly tested Mixtral, finding it to be just “okay” for their purposes, and favored their own models instead.

DiscoResearch ▷ #disco_judge (2 messages):

  • Insights on “Aristotelian Rescoring”: @crispstrobe suggested exploring the “Aristotelian Rescoring” approach, which might be applicable to complex challenges. Also mentioned were related works such as STORIUM, FairytaleQA & TellMeWhy, with a link to the TellMeWhy dataset on GitHub and Hugging Face.

  • Collaborators Wanted for DPO Refinement: @huunguyen is considering a minor refinement to DPO and is seeking assistance for the test. Anyone interested in collaborating was invited to help.

Links mentioned:

GitHub - StonyBrookNLP/tellmewhy: Website for release of TellMeWhy dataset for why question answering: Website for release of TellMeWhy dataset for why question answering - StonyBrookNLP/tellmewhy

DiscoResearch ▷ #general (8 messages🔥):

  • German Semantic Similarity Boosted: User @sten6633 successfully enhanced semantic similarity calculations by finetuning gbertlarge from deepset with German domain-specific texts, converting it into a sentence transformer, and further finetuning with Telekom’s paraphrase dataset. Each step resulted in significant improvement.

  • “AI in Production” Conference Call for Speakers: @dsquared70 invites developers integrating Generative AI into production to speak at a conference in Asheville, NC. Potential speakers can apply by April 30 for the event on July 18 & 19.

  • Claude-3’s Performance in German Unclear: @bjoernp inquires about the performance of Anthropic’s Claude-3 in German, sharing a link about it, while user @devnull0 mentions limited access and issues with German phone numbers.

  • Claude AI Access Issues in the EU: @bjoernp recalled that Claude AI is not available in the EU by sharing a location restrictions link, although @devnull0 mentions using tardigrada.io for access in December.

  • German Phone Number Success with Claude AI: Contradicting @devnull0’s experience, user @sten6633 states that registering with a German mobile number was fine.

Links mentioned:

AI in Production - AI strategy and tactics.: no description found

DiscoResearch ▷ #benchmark_dev (3 messages):

  • Dataset Translation Quirk Spotted: @johannhartmann pointed out a translation issue where the category “Stem” was incorrectly translated to “Stamm” in the German dataset.
  • Integration Efforts into FastEval: @johannhartmann announced they were integrating a dataset into FastEval, a tool for realistic evaluation of chat language models.
  • Technical Troubles Resolved: After encountering a VLLM error potentially caused by a switch from threading to asyncio, @johannhartmann managed to resolve the issues and successfully run FastEval with the command ./fasteval -b mt-bench-vago -t chatml -m malteos/hermeo-7b.

Links mentioned:

GitHub - mayflower/FastEval: Fast & more realistic evaluation of chat language models. Includes leaderboard.: Fast & more realistic evaluation of chat language models. Includes leaderboard. - mayflower/FastEval

DiscoResearch ▷ #discolm_german (18 messages🔥):

  • Brezn’s Impressive Performance and Future Possibilities: @thomasrenkert acknowledges the success of Brezn-7b, while @johannhartmann reveals that Brezn outperforms in German due to a merge of good models aligned with 3 DPO datasets, which results in more reliable answers. Johannhartmann is considering using ChatML by default in Brezn for better benchmark scores.

  • Merging and Laser Strategy for Language Models: @devnull0 inquires about the process of merging before lasering on models, prompting @johannhartmann to discuss his use of DARE TIES and lasered models in an experimental approach known as “shotgun training”.

  • Translation Techniques for Dataset Alignment: @crispstrobe links to a Reddit post discussing prompt format effects on model outputs and mentions the importance of dataset curation. @johannhartmann uses AzureML for cost-effective and high-quality translation of datasets and points out Mayflower GmbH’s contributions to German-language LLMs and datasets on Hugging Face.

  • Brezn’s Base Model Potential: @thomasrenkert tests Brezn and expresses amazement at its performance, hypothesizing that combining it with DiscoLM_German_8x7b_v2 as the base model could yield even better results.

  • Debate Over German Hatespeech Dataset’s Relevance: @_chromix_ and @sten6633 discuss the merits and limitations of a German hatespeech dataset from Zenodo, noting that it might be more indicative of newspaper moderation bias and that it would require cleaning to avoid training overly sensitive models.

Links mentioned:

Datasette - LLM (@SimonW) ▷ #ai (1 messages):

dbreunig: This demo of stable diffusion xl lightning is blowing my mind: https://fastsdxl.ai/

Datasette - LLM (@SimonW) ▷ #llm (4 messages):

  • Artichoke Amusement: User @bdexter provided an inventive list of names for artichokes, including playful monikers such as “Choke-a-tastic,” “Arti-party,” and “Leafy Delight.”

  • Mistral’s High Price Performance: @derekpwillis tested the new Mistral large model and commented on its solid performance in extracting data from text, despite being somewhat costlier than preferred.

  • Introducing Claude 3 Plugin: @simonw announced a new plugin for interacting with the Claude 3 family of models, sharing the link to its GitHub repository (GitHub - simonw/llm-claude-3).

  • Quick Praise for Plugin Development: In response to the new plugin, @0xgrrr quickly commended @simonw on the fast development of the tool.

Links mentioned:

GitHub - simonw/llm-claude-3: LLM plugin for interacting with the Claude 3 family of models: LLM plugin for interacting with the Claude 3 family of models - simonw/llm-claude-3

Alignment Lab AI ▷ #looking-for-collabs (2 messages):

  • Invitation to Collaborate Accepted: User @wasooli expressed interest in collaborating on a project and inquired about the possibility of direct messaging. @taodoggy responded positively, welcoming a direct message.

Alignment Lab AI ▷ #general-chat (1 messages):

  • Calling All AI Enthusiasts: @dsquared70 is organizing a conference in Asheville, NC focusing on GenAI in production and has opened a call for papers. Interested developers and speakers are invited to apply by April 30th, with more details available at AI in Production. 🏔️ 🍻

Links mentioned:

AI in Production - AI strategy and tactics.: no description found

Skunkworks AI ▷ #general (3 messages):

  • AI in Production Conference Call: @dsquared70 invites developers integrating GenAI into production to speak at a conference in Asheville, NC. Details and the call for papers can be found at AI in Production Call for Presentations, with submissions due by April 30.

  • A Bright “Yolks” Morning: @oleegg greets the chat with a jovial “good morning yokks,” followed by a correction to “yolks.”

Links mentioned:

AI in Production - AI strategy and tactics.: no description found

AI Engineer Foundation ▷ #general (3 messages):

  • Hackathon Confusion Cleared Up: User @needforspeed4 inquired if the hackathon at Agape was related to the AI Engineer Foundation that manages this Discord server. They also asked if different Discords are used for each hackathon.
  • Distinct Hackathon Entities: @hackgoofer clarified that The AI Engineer Foundation Hackathons are indeed hosted within this Discord, however, the Agape hackathon is not affiliated with the AI Engineer Foundation.