[AINews] Zero to GPT in 1 Year • ButtondownTwitterTwitter

The Discord recap included discussions on various topics such as Mixtral and Mistral Models gaining traction, Rerank 3 and Cohere's search enhancements, CUDA optimizations and quantization quests, scaling laws and data filtering findings. Noteworthy discussions also involved the release of GPT-4 Turbo, Ella's anime image generation capabilities, anticipation for Stable Diffusion 3, discussions around the Hugging Face Rerank model, and OpenAI API advancements. The section highlighted different conversations from Stability.ai Discord focusing on Forge, Ella's limitations in anime art, expectations from Stable Diffusion 3, image-perfecting tools, and the recognition of Cascade for fast learning. The Cohere Discord section covered topics such as CORS issues, arguments over context length in LLMs, pricing and promotion details of Rerank 3, fine-tuning and deployment queries for Cohere's LLMs, and an overview of how Rerank 3 boosts enterprise search.

The Discord communities are buzzing with discussions and updates on various AI topics and advancements. From introductions of new AI models like Ghost 7B and detailed strategies for model deployment to discussions on the challenges of fine-tuning NLP models and the importance of domain-specific data, engineers are actively sharing insights and resources. Noteworthy developments include Google's C++ inference engine Gemma, Apple's AI chips, and advancements in Large Language Models (LLMs) in medical applications. Across different Discord channels, engineers are engaging in in-depth conversations on topics like linear optimization, speed breakthroughs, matrix multiplications, and the implications of using large language models. Community members are also exploring new tools and techniques to enhance model performance, like custom libraries for model optimization and code contributions to open-source projects. Overall, the Discord platforms serve as a valuable hub for AI enthusiasts to stay updated, share knowledge, and collaborate on cutting-edge AI technologies.

• Community members are questioning the alleged speed improvements of Haiku, with concerns particularly aimed at whether it significantly enhances total response time rather than just throughput.
• Engineers in the discussion are interested in the speed and code handling improvements of the newly released turbo, with some contemplating reactivating ChatGPT Plus subscriptions to experiment with turbo's capabilities.

Hunting for Jamba's Genesis

A community member expressed a desire to find the source code for Jamba but no URL or source location was provided.

Eager for Model Merging Mastery

A link to a GitHub repository, moe_merger, was shared that lays out a proposed methodology for model merging, although it's noted to be in the experimental phase.

Thumbs Up for Collaboration

Gratitude was shared by users for the resource on merging models, indicating a positive community response to the contribution.

Anticipation in the Air

There's a sense of anticipation among users for updates, likely regarding ongoing projects or discussions from previous messages.

Shared Wisdom on Standby

Users are sharing resources and expressing thanks, showcasing a collaborative environment where information and support are actively exchanged.

• Solving Fine-Tuning Woes: Users discussed challenges with fine-tuning NLP models, including discrepancies in evaluation and inference metrics.
• Model Deployment Dialogue: Exchange on deploying models post-training with Unsloth AI.
• VRAM Hunger Games: Strategies to fit models within VRAM limits were shared.
• Dataset Formatting for GEMMA Fine-tuning: Help provided for fine-tuning GEMMA on a custom dataset.
• GPU Limits in Machine Learning: Debate on multi-GPU support and licensing restrictions with Unsloth AI.
• Sneak Peek of Ghost 7B: Excitement around the upcoming Ghost 7B model for various language tasks.
• Enriching Low-Resource Languages: Tips for improving low-resource language datasets.
• Community Support for Ghost X: Positive feedback on Ghost X developments.

In the Nous Research AI channel on Discord, members shared UI inspirations for Worldsim, discussed the anticipation for Worldsim's return, and engaged in conversations likening dangerous UI allure to 'Hot but Crazy' relationships. Speculations about Worldsim's return timeframe were made, including potential alternatives like sysprompt, Anthropic workbench, and Sillytavern. The use of Anthropic API keys with the Claude model was also mentioned for experimentation.

In this section, discussions on CUDA Mode and HQQ's lower bit optimization are highlighted. In CUDA Mode discussions, members talk about planning next steps, starting implementation of proposed ideas, and pointing out mistakes in the order of operations in a GIF. In another section, A4000 Bandwidth breakthrough is discussed with vectorized loads, transitioning from C to C++ for CUDA development is emphasized, and cooperative groups' impact on softmax performance is mentioned. Moreover, a discussion on the outdated curriculum of CUDA programming book, performance gains, and PR reviews comparing PyTorch and RTX 4090 is presented. Lastly, in the LM Studio section, various discussions on LM Studio tool functionalities, model hosting, troubleshooting AVX2 instruction absence, and improving performance for large models are covered.

The LM Studio section delved into various topics related to embedding models and technology previews. Users encountered Mistral model loading issues and inquired about BERT embeddings, Google BERT clarification, and recommendations for improved embedding models with larger parameters. Additionally, the discussion covered options for compute costs with embeddings, including base, small, and large versions. The Eleuther section explored the challenges of adapting methods for extending context windows in encoder models, showcased new language models like MosaicBERT and Mixtral-8x22B, and discussed exploring the Pile dataset size discrepancies and developing a reading list for AI learning. Furthermore, discussions on Google's Mixture-of-Depths model, RULER's real context size, and finetuning a subset of layers for efficient training were highlighted. The section also introduced JetMoE-8B as an affordable and competitive large language model. The EleutherAI Discord channels engaged in discussions related to GitHub stars as a metric, activation to parameter concepts, anomaly detection, and the unique characteristics of NeoX embeddings. Lastly, OpenRouter announcements included the introduction and subsequent switch from Mixtral 8x22B:free to Mixtral 8x22B due to routing and rate-limiting confusions. Users were also informed about new experimental models for testing, Zephyr 141B-A35B, and Fireworks: Mixtral-8x22B Instruct (preview).

• Mojo Gives Back to the Community: Mojo open-sources their standard library with community-made changes.
• Seeking Modular Collaboration: BackdropBuild.com seeks to integrate Modular into their developer programs.
• Staying In The Right Lane: Discussions on directing business inquiries for better organization.
• Karpathy's llm.c Discussed in Mojo: Interest in using Mojo for the llm.c repo for benchmarking.
• Binary File Reading in Mojo: Implementing a binary file reader in Mojo for improved performance.
• GUI Design Philosophies Clash: Debates on GUI design approaches and paradigms.
• Mojo's Potential to Enhance Python Performance: Enthusiasm for Mojo's potential to enhance Python performance.
• Comparing Functionality between Mojo and C: Comparisons on functionalities between Mojo and C.
• Sneak Peek at Terminal Text Rendering with Mojo: Showcase of text rendering in the terminal using Mojo.
• Cheers for Basalt Integration: Compliments for Basalt integration.
• Exploring Memory Storage in Matrices: Analysis of matrix memory storage in Mojo and Numpy.
• Matrix Memory Order Notebook Error: Encountering errors in Jupyter notebook related to matrix memory orders.

The section discusses various interactions and updates within the LangChain and HuggingFace AI communities on Discord. It includes alerts about inappropriate content, requests for assistance on LangChain tools, the launch of Galaxy AI, the development of AI assistants, and the enhancement of meeting reporting using AI. Additionally, it covers discussions on training models, general AI operations, and dataset management. Link mentions include resources for AI app development, datasets, and tools for image augmentation.

• GPU Process Management Tool Recommended: Suggestion of 'nvitop' for GPU process management.
• Starting Steps in Video Correction Techniques: Advice on video correction techniques and reference to an image restoration paper.
• The Importance of Data Augmentation in Image Restoration: Emphasis on data augmentation and links to papers detailing augmentation pipelines.
• Understanding Video Deflickering: Reference to a project dealing with video deflickering.
• Integration of Multimodal and Vector Databases Explored: Discussion on integrating Google's Vertex multimodal embeddings with Pinecone vector database, including a link to a demo.

The LAION Discord channels are filled with various discussions and concerns. Members express dissatisfaction with closed-source projects like Draw Things and skepticism towards the TempestV0.1 Initiative. There are also questions about the Laion 5B demo and warnings about potential scams related to cryptocurrency. The community is active in addressing misinformation and solutions. Additionally, there are technical discussions on Intel vs. AMD and Nvidia manufacturing, improved diffusion models, and applying research findings to practical tools. Members also inquire about datasets like HowTo100M and discuss embedding storage in the LlamaIndex channels.

A new 22B parameter dense model called Mistral-22b-V.01 has been released, marking the successful conversion of a MOE model into a dense format. This accomplishment generated excitement within the community. Additionally, discussions took place regarding the challenges of converting models using Mergekit into MoE models, with reported underperformance compared to original models. Another notable release is the Zephyr 141B-A35B model, fine-tuned using an algorithm called ORPO and trained with 7k instances in just over an hour on impressive hardware. Furthermore, debates arose comparing the performance of the Supervised Fine-Tuning (SFT) Mixtral models versus original Mixtral instruct, with varying opinions on the results. Community members also queried about the successful fine-tuning of 22B models, considering the potential superiority of official Mixtral models due to a speculated 'secret sauce' about routing.

The section highlights discussions on multilingual language models and model training methodologies from various Discord channels. The conversations include topics such as leveraging pretraining with Supervised Fine-Tuning data, the balance between English and German data for model finetuning, exploring linguistic nuances through multilingual model fine-tuning, achievements with Occiglot-7B-DE-EN-Instruct model, and the implications of incorporating various datasets and methodologies during model training. Links to related papers, repositories, and research insights are shared throughout the discussions.

• Gradio UI for Figma Launches: Mozilla introduces Gradio UI for Figma, a library based on Hugging Face's Gradio, for rapid prototyping in the design phase. Access the toolkit on Figma.
• Join the Gradio UI Discussion: Engage in a conversation thread about Gradio UI for Figma with Thomas Lodato from Mozilla’s Innovation Studio on Discord through this thread.