[AINews] Mergestral, Meta MTIAv2, Cohere Rerank 3, Google Infini-Attention • ButtondownTwitterTwitter

This section provides detailed recaps of the discussions and updates related to AI on Reddit and Twitter. The Reddit recap covers a wide range of topics such as new models like Mistral 8x22B and Command R+, developments in Stable Diffusion and Image Generation, retrieval-augmented generation, open-source efforts, prompt engineering, fine-tuning, benchmarks, comparisons, evaluations, and even memes and humor. On the other hand, the Twitter recap highlights LLM developments such as the Mixtral-8x22B release, improvements in GPT-4 Turbo, Command R+ release, and the launch of Gemini 1.5 Pro. It also discusses efficient LLMs like the Infini-attention for infinite context and adapting LLaMA decoder to vision tasks. Lastly, it touches on robotics and embodied AI with DeepMind training AI agents in agile soccer skills.

A summary of different AI Discord channels' discussions and updates include: anticipation for new AI models like SD3, Llama 3, Mixtral-8x22b, and others, advancements in AI hardware like Meta's MTIA and Intel's Lunar Lake CPUs, new AI applications and integrations, open-source projects like Rerank 3 and Zephyr Alignment, and innovative AI experiments like Suno Explore and Udio Text-to-Music. In addition, the AI community is exploring areas like AI model efficiency gains in CUDA and quantization, and advancements in AI communication, model fine-tuning, and hardware suitability. Discussions on deployment strategies, new model releases, and community collaborations are also prominent themes across the Discord channels.

The section highlights various discussions and developments in different Discord communities related to advancements in AI, tech, and finance. From CUDA implementations and study groups to knowledge scaling models and fine-tuning techniques, each Discord community is abuzz with unique insights and innovations. Notable topics include AI acceleration hardware, advancements in TTS technology, and the implications of next-generation CPUs. Community engagement is fervent, with members actively participating in discussions, sharing resources, and exploring new tools and techniques to push the boundaries of AI applications.

LangChain AI Discord

• Keep an Eye on Your Tokens: Engineers suggest monitoring token usage with tiktoken to estimate costs of API calls efficiently.
• Metadata Filters in Action: Vector databases utilize metadata filters for specific queries, enhancing custom retrievers for richer context in results.
• Beta Features in the Spotlight: Discussion on the beta status of 'with_structured_output' in ChatOpenAI class and related tools like Instructor for Python for structuring LLM outputs.
• LangChain's Open-Source Compatibility Conundrum: Seeking clear examples for utilizing non-OpenAI LLMs.
• Galaxy of New AI Tools Emerges: Introduction of apps like GPT AI with GPT-4 and Vision AI, Galaxy AI offering free premium AI APIs, and Appstorm v1.6.0 for app-building.

DiscoResearch Discord

• Mixtral Models Turn Heads with AGI Eval Triumph: Mixtral's models excel on benchmarks; queries about equivalent benchmarks for German language models noted.
• Model Licensing Discussion Heats Up: Apache 2.0 licensing confirmed for latest models, discussions on licensing impact ongoing.
• Model Performance Discrepancies Unearthed: Performance variance in models discussed due to a newline character, sparking tokenizer configuration discussions.
• Cross-Language Findings Set the Stage: Community delves into multitask finetuning and its impact on non-English data.
• Dense Model Conversion Marks a Milestone: News of 22B parameter MoE model conversion to a dense version prompts discussions on model merging methods.

Interconnects Discord

• New Model Sparks Curiosity: Introduction of a new AI model with Apache 2.0 license discussed, speculated rushed release influenced by competitor models.
• Benchmarks, Blogs, and Base Models: Debates on benchmarks, proposal for unbiased human eval blog, and discussion on BigXtra's base model.
• Evaluating Instruction Tuning Debate: Debate on redundant instruction-tuning stages in model training process.
• Machine Learning Morality Questioned: Tense conversation on ethics in AI industry initiated by allegations of insider trading and conflicts of interest.
• Interview Intrigue and Recruitment Musings: Anticipation for possible interview with John Schulman and strategies for new member recruitment.

Datasette - LLM Discord

• Audio Intelligence Takes a Leap Forward: Gemini enhances AI to answer questions about audio in videos.
• Google's Copy-Paste Plagued By Pasting Pains: Engineers call for improvement in Google's text formatting capabilities.
• Stanford Storms into Knowledge Curation: Introduction of Stanford Storm project for AI knowledge curation.
• Shell Command Showdown on MacOS: MacOS iTerm2 issue resolved with user input fix.
• Homebrew or Pipx: LLM Shells Still Stump Users: Troubleshooting LLM cmd issues highlights interaction requirements.

Mozilla AI Discord

• Bridge the Gap with Gradio UI for Figma: Introduction of Gradio UI for Figma for fast prototyping.
• GPU Constraints Make Waves: Discussion on managing GPU memory limitations.
• Kernel Conversations Can Crash: Issue of kernel panic during tensor engagement on M2 MacBook.
• A Lesson in Language Model Memory Management: Discussion on ollama project for memory handling and Quiet-STaR technique for text predictions refinement.
• Boost Text Predictions with Quiet-STaR: Interest in Quiet-STaR technique and related resources shared.

Skunkworks AI Discord

• Mistral's Major Milestone: Mistral 8x22b sets a new standard in AGIEval.
• A Quest for Logic in AI: Exploration of resources for imparting logical reasoning in large language models.
• Formal Proof AI Assistance: Interest in Coq dataset for formal theorem proving in LLMs.
• Google's CodeGemma Emerges: Introduction of Google's CodeGemma code completion model.
• Hot Dog Classification Goes Viral: Tutorial on AI models for classifying hot dogs showcased.

LLM Perf Enthusiasts AI Discord

• GPT's Coding Game Still Strong: GPT's robust coding capabilities noted.
• Cursor vs. Claude: The Tool Time Talk: Comparison of cursor and Claude for code generation.
• Gemini 1.5 Rises: Positive feedback on Gemini 1.5.
• Copilot++ Takes Off: Introduction of Copilot++ for coding tasks.
• Claude's Rare Slip Up: Claude's unexpected code generation discussed.

AI21 Labs Discord

• Jamba Code Hunt: Interest in locating Jamba source code.
• Curiosity for Jamba Updates: Community eager for recent updates on Jamba.

Unsloth AI Discord

• Discussion on Model Performance and Optimization: Conversations on fine-tuning Mistral and CodeGemma models.
• Interest in Apple Silicon Support: Enthusiasm for Apple Silicon support.
• Queries on Learning Triton DSL and Platform Usage: Discussions on Triton DSL learning and platform efficiency.
• Feedback and Experiences with Unsloth's Fine-tuning: User experiences with Unsloth's fine-tuning and challenges faced.

The conversation touched upon Perplexity Labs and its instruction tuning, with one user noting similarities between search results and the outputs from the model. Discussions also mentioned concerns about the effectiveness of inflection and interest in new models like Mixtral-8x22b and their performance.

This section includes various discussions from the LM Studio Discord channels focused on topics like optimizing model deployment, challenges with time series data for large language models, and navigating cloud costs. Additionally, there are conversations about LM Studio's hardware utilization, troubleshooting AMD machine model visibility, and integration issues with Open WebUI. The section also touches on recent concerns regarding ROCm functionality, potential unsupported GPUs, and the development of RNN-based architectures. These discussions highlight the evolving landscape of AI models, their applications, and the challenges faced by users in different hardware and software environments.

Members in the Nous Research AI channel engaged in various discussions related to Large Language Models (LLMs). They explored topics such as the upcoming release of Llama 3 models, options for fine-tuning Mistral at 7b efficiently, and the use of alternatives to full fine-tuning like Qlora. There were also inquiries about datasets for logical reasoning and the sharing of helpful resources such as Udio music generator and Nvidia's performance analysis. Overall, the channel provided a platform for members to share insights and seek guidance on different aspects of working with LLMs.

In the 'cool-finds' section of the HuggingFace community, various interesting discoveries were shared. This included a demonstration of a tiny but powerful multilingual model, a GitHub repository for using Quanto with Transformers, a new Marimo app for experimenting with HuggingFace models, an article on the RecurrentGemma model, and a GitHub project by Andrej Karpathy offering a stripped-down LLM training implementation in C/CUDA. These findings showcased innovative tools and projects within the AI community.

The HuggingFace community engages in various discussions related to intelligent customer service chat systems, mathematical papers on samplers and schedulers, KD-diffusion papers, multi-GPU support with device map, eradicating watermarks with AI, NVidia GPU process monitoring advice, video correction techniques, augmentation for restoration model generalization, challenges of specific video datasets, evaluating language models with cosine similarity, exploring GPT-4 alternatives, seeking long context models, Hugging Face Trainer's pause-resume feature, script assistance for model training, and more. Furthermore, users discuss different analytical methods, Perplexity AI integration and partnerships, API authentication issues, and specific queries for insight on video games, financials, Meta's custom AI, chip developments, and analytical methods for various scopes. Lastly, the Perplexity API section addresses features, limitations, and bypassing methods, while the CUDA MODE sections highlight Meta's impressive AI training infrastructure and the attorch repository for CUDA mode.

CUDA MODE ▷ #torch (3 messages): - Users encountered issues while attempting to quantize ViT models and add support for flashattention2 to a BERT model. They are seeking guidance and insights on these problems.

CUDA MODE ▷ #beginner (7 messages): - Members discuss setting up a study group for PMPP book lectures, reminders about individual learning progress, comparisons between learning CUDA and German, and scheduling viewing party sessions with a Discord group for interested participants.

CUDA MODE ▷ #ring-attention (3 messages): - Members jokingly announce the arrival of an 'extra large dataset', suggest creating a list of tasks, and mention having testing tasks lined up with 'mamba'.

CUDA MODE ▷ #off-topic (3 messages): - Users identify the server picture as Goku, celebrate surpassing 5000 members, and share advice on effective knowledge consumption.

CUDA MODE ▷ #hqq (76 messages🔥🔥): - Discussions revolve around quantization scripts and benchmarks, performance of different int4 kernels, reproducibility issues, discrepancies in perplexity metrics, and transition from C to C++ in CUDA development.

CUDA MODE ▷ #triton-viz (3 messages): - Members discuss potential enhancements for the triton-viz chatbot, including modifying hyperlinks and adding step-by-step code annotations.

CUDA MODE ▷ #llmdotc (67 messages🔥🔥): - Users report efficiency gains in CUDA forward pass compared to PyTorch for a GPT-2 model, discuss optimizing in pure CUDA's C subset, implementing warp-wide reductions and kernel fusion, evaluating cooperative groups for CUDA, and transitioning from C to C++ in CUDA development.

Eleuther ▷ #announcements (1 message): - New research shows the adaptability of interpretability tools for transformers to modern RNNs like Mamba and RWKV.

Eleuther ▷ #general (83 messages🔥🔥): - Discussions include enthusiasm over the Mixtral 8x22B model, a timeline of AI predictions, concerns over election security with AI technologies, technical discussions on extending encoder models, and clarifications on downloading The Pile dataset for research purposes.

• The section discusses various findings and discussions in the Eleuther community related to interpretability, model training constraints, Mistral's bidirectional attention, and training hybrid RWKV5 transformer models.
• The community explores research around adversarial image examples, subset fine-tuning methods, and uncovering model training budget constraints.
• Recent discoveries suggest Mistral models may leverage bidirectional attention, leading to high cosine similarity across layers and positions.
• Discussions also touch on training hybrid RWKV5 transformer models and seeking benchmarks for OpenAI's gpt-4-turbo version.

Modular (Mojo 🔥) Community Projects

• Mojo Gets Iterative: A code snippet for iterating over string characters was shared, potentially useful for others. The actual iterator code is available on the Discord link provided.
• Keyboard Events Come to mojo-ui-html: Update introduces keyboard events, window minimization, and per-element CSS styling improvements aimed at game and custom widget developers. Demo available on GitHub.
• Lightbug Framework Celebrates Contributions: Community contributions include addition of remote address retrieval and a new API framework like Django. Check out the developments on GitHub.
• Elevating Terminal Text Rendering with Mojo: Showcased text rendering in a terminal using Mojo with inspiration from Go packages like lipgloss. Code available on GitHub for inspection.
• Basalt Illustrates Mojo's Visual Appeal: Praised the use of Basalt in enhancing terminal applications for Mojo-rendered text examples.

This section discusses various technical updates and user experiences related to AI models like Gemini, Mixtral 8x22b, and Command r+. Users had issues with models not being free when expected, the 'Updated' tag not reflecting the latest changes, and confusion regarding rate limits and token counting. Clarifications were provided on Gemini token pricing, and positive feedback was shared about Mixtral 8x22b, noting its good reasoning capabilities and cost-effectiveness compared to GPT-4. Discussions in the OpenInterpreter channel covered topics like the efficiency of different models, interface setups, and the transition to prepaid credits by OpenAI. Members shared technical support experiences and expressed anticipation for Mixtral and OI integration. In the LlamaIndex channel, the focus was on control over agents with IFTTT execution stops, new methods for building ColBERT-based retrieval agents, and tutorials on creating apps for interacting with GitHub repositories.

This section covers a range of discussions related to AI development and implementation. From CI tests lacking for copy_from_fd in tinygrad to the rejection of a Rust export feature proposal, the discussions also include emphasis on performance over language preference, code standardization for mnist datasets in tinygrad, and user observations on memory safety in Rust. Other topics include AI art, music AI, AI hardware, text-to-speech advancements, and the uncertain future of Laion 5B web demo. Additionally, the section showcases requests for dataset access, AI model personalization, and metadata utilization in vector databases among other interesting conversations.

This section discusses updates and discussions related to various language models. It includes details on model conversions, experimental releases, challenges with model merges, and clarification on model performance comparisons. Additionally, there are mentions of community shares, licensing information, benchmark results, and upcoming model releases. The content also covers topics like German benchmarking interest, machine learning model training processes, and potential issues with tool commands. Lastly, it touches on AI advancements such as audio awareness in AI, knowledge curation systems, and collaboration tools for design prototyping.

The section discusses ollama's handling of large language models with a link to the ollama project on GitHub, a kernel panic triggered by conversing with tensors on an M2 MacBook, and the introduction of Quiet-STaR for implicit reasoning in text. Quiet-STaR's method for generating rationales at each token to improve text predictions is detailed along with links to the research paper and GitHub repository. The conversation also touches on Mistral 8x22b's performance in AGIEval, seeking logic datasets, curated lists for reasoning AI, the LOGIC-LM project for faithful logical reasoning, and efforts towards COQ-compatible LLMs. Additionally, discussions about CodeGemma, classifying hot dogs with AI, debunking slacking AI rumors, Gemini 1.5, Cursor preferences, Copilot++, and Claude's code hallucination are also mentioned.