[AINews] Music's Dall-E moment • ButtondownTwitterTwitter

The Reddit recap highlighted various advancements in AI models and architectures, open-source efforts, benchmarks, and comparisons. Key topics included Google's Griffin architecture, Command R+ climbing leaderboard, and Mistral releasing an 8x22B model. On the other hand, the Twitter recap focused on GPT-4 Turbo model improvements, Mistral AI's new 8x22B model release, and Google's new model releases and announcements. Additionally, it covered topics such as Anthropic's research on model persuasiveness, Cohere's Command R+ model performance, and Meta's new AI infrastructure and chip announcements.

New and Upcoming AI Model Releases and Benchmarks

• Excitement around the release of Mixtral 8x22B, a 176B parameter model outperforming other open-source models on benchmarks like AGIEval. A magnet link was shared.
• Google quietly launched Griffin, a 2B recurrent linear attention model, and CodeGemma, new code models.
• OpenAI's GPT-4 Turbo model has been released with vision capabilities, JSON mode, and function calling, showing notable performance improvements over previous versions. Discussions revolved around its speed, reasoning capabilities, and potential for building advanced applications. It has notable performance gains, discussed alongside models like Sonnet and Haiku in benchmark comparisons.
• Anticipation for releases like Llama 3, Cohere, and Gemini 2.0, with speculation about their potential impact.

Quantization, Efficiency, and Hardware Considerations

• Discussions on quantization techniques like HQQ and Marlin to improve efficiency, with concerns about maintaining perplexity.
• Meta's study on LLM knowledge capacity scaling laws found int8 quantization preserves knowledge with efficient MoE models.
• Hardware limitations for running large models like Mixtral 8x22B locally, with interests in solutions like multi-GPU support.
• Comparisons of AI acceleration hardware from companies like Meta, Nvidia, and Intel's Habana Gaudi3.

Open-Source Developments and Community Engagement

• LlamaIndex showcased for enterprise-grade Retrieval Augmented Generation (RAG), with the MetaGPT framework at ICLR 2024 leveraging RAG.
• New tools like mergoo for merging LLM experts and PiSSA for LoRA layer initialization.
• Community projects: everything-rag chatbot, TinderGPT dating app, and more.
• Rapid open-sourcing of new models like Mixtral 8x22B by community members on HuggingFace.

Prompt Engineering, Instruction Tuning, and Benchmarking Debates

• Extensive discussions on prompt engineering strategies like meta-prompting and iterative refinement using AI-generated instructions.
• Comparisons of instruction tuning approaches: RLHF vs Direct Preference Optimization (DPO) used in StableLM 2 model.
• Skepticism towards benchmarks being 'gamed', with recommendations for human-ranked leaderboards like arena.lmsys.org.
• Debates around LLM2Vec for using LLMs as text encoders and its practical utility.

Gemma 1.1 Instruct Outclasses Its Predecessor:

• Gemma 1.1 Instruct 7B shows promise over its previous version, now available on HuggingChat, and is prompting users to explore its capabilities. The model can be accessed here.

CodeGemma Steps into the Development Arena:

• A new tool for on-device code completion, CodeGemma, is introduced, available in models of 2B and 7B with 8192k context, and can be found alongside the recent non-transformer model RecurrentGemma here.

Cost-cutting Operations at HuggingFace:

• HuggingFace announces a 50% reduction in compute prices for Spaces and Inference endpoints, edging out AWS EC2 on-demand services in cost-effectiveness from April for these services.

Community Blog Makeover:

• A revamp of community blogs to 'articles' with added features such as upvotes and enhanced visibility within HuggingFace is now in effect. Engage with the new articles format here.

Serverless GPUs Hit the Scenes with Bonus ML Content:

• Hugging Face showcases serverless GPU inference in collaboration with Cloudflare and furthers education with a new bonus unit on Classical AI in Games in its ML for Games Course. Investigate serverless GPU inference via this link, and explore the course's new content here.

Decoding Python for Debugging:

• Leverage eager execution in JAX or TensorFlow, use Python's breakpoint() function, and remove PyTorch implementations for effective debugging.

AI Watermark Eradicator Introduced:

• An AI tool designed to remove watermarks from images has been suggested, benefiting those with extensive batches of watermarked images. Review the tool on GitHub.

GPT-2's Summarization Struggles & Prompting Approach:

• A user's challenge with using GPT-2 for summarization could be a hint at the importance of prompts aligning with the model's training era, suggesting a possible need for updated instructions or newer models better suited for summarization.

Navigating CPU & GPU Challenges:

• Techniques like accumulation or checkpointing were discussed as workarounds for batch size limitations when using contrastive loss, acknowledging potential update issues with batchnorm. Tracking GPU usage via nvidia-smi became a point of interest for efficient resource management.

Diffuser Denoising Steps Illuminate Image Quality:

• Explorations into diffusers revealed that image quality fluctuates with changed denoising step counts. The ancestral sampler's role in quality variance was elaborated, and guidance for distributed multi-GPU inference was provided, particularly for handling significant memory requirements of models like MultiControlnet (SDXL).

Speed Matters in LLM Help Commands:

Users have raised concerns regarding the slow performance of the llm --help command, where one instance took over 2 seconds to complete, raising red flags about system health.

Rapid Responses for LLM Commands:

A contrasting report indicates that llm --help can execute in a swift 0.624 seconds, suggesting performance issues may be isolated rather than universal.

The Docker Difference:

When benchmarking llm --help, a user noticed a stark difference in command execution time, enduring a sluggish 3.423 seconds on their native system compared to a more acceptable 0.800 seconds within a Docker container, hinting at configuration issues.

Fresh Installs Fix Frustrations:

A user discovered that reinstalling llm not only enhanced the speed of llm --help, bringing it down from several seconds to a fraction but also rectified an error when running Claude models.

MacOS Mystery with LLM:

On macOS, llm cmd execution hangs in iTerm2 while the same setup yields successful runs on a remote Ubuntu server, indicating possible conflicts with customized shell environments in macOS.

Laptops Might Run Small LLMs

• Members discuss using nvidia-smi to check GPU VRAM for laptops with NVIDIA graphics.

Introducing CodeGemma

• CodeGemma model is introduced, offering code completion and generation, ideal for Python programming.

Smaug Model for Enhanced Performance

• Discussion on the Smaug 34B model's potential inclusion in LM Studio and its impressive performance.

Running Command R+ on a Mac Studio

• Users achieve 5.9 tokens per second using Command R+ on Mac Studio with 192GB RAM.

Mixtral Model Potential

• Excitement around the Mixtral-8x22B-v0.1-GGUF model with 176B MoE, requiring 260GB VRAM in fp16.

LM Studio Beta Releases

• Beta release includes Command R Plus support and updates in llama.cpp.

ROCM Utilization Issues

• Users report ROCM utilization problems and discuss bug resolution steps.

DockDuckGo as a Search Alternative

• Member recommends DuckDuckGo for searches without API, notes restrictions by Crewai.

Google Launches CodeGemma Series

• Google launches CodeGemma models for code generation with different variants.

Issues with Evaluation Methods

• Discussions on LLM evaluations comparing models like GPT-4 and 3B-LLM.

• StableLM 2 enters the Chat Game: StableLM 2 - 12B Chat, a 12 billion parameter AI trained with Direct Preference Optimization (DPO) for chat optimization, is highlighted. The usage instructions and code snippet to implement it are shared along with a link to the model.
• Debating AI Tuning Approaches: A member expresses mixed feelings about using DPO in chat finetuning and prefers methods like SFT+KTO or DNO, mentioning the effective use of DNO in Microsoft's Orca 2.5.
• LLMs as Text Encoders: The GitHub repository for the 'LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders' project is shared, suggesting that encoder LLMs can produce quality embeddings.

In this section, there is a discussion on the potential use of traditional LLMs for embeddings to enrich context and save on VRAM by multitasking on machines. Additionally, there is clarification on prefix LM, explaining its bidirectional attention at the start of a sequence and its impact on AI performance. Other topics covered include the competition between Mistral 8x22b and Command R+, interest in AI-generated math problems in the AIMO competition, challenges of new large AI models on hardware limits, the release of GritLM integrating text embedding and generation, and a discussion on the Quantization Aware Training in OLMo-Bitnet-1B. Various links mentioned include those related to stabilityai/stablelm, TensorFlow, and the Habana Gaudi3 AI accelerators.

The section delves into various discussions surrounding GPT models, encoder-decoder architectures, and advancements in language representations. Highlights include comparisons of different techniques like SVD and LoRA, introducing LLM2Vec for improved performance, and exploring the latent potential of encoder-decoder models for embedding research. An interesting paper on estimating the knowledge storage capacity of language models is also discussed, suggesting that models can store 2 bits of knowledge per parameter. Additionally, conversations in different OpenAI and Eleuther channels touch upon topics like service status updates, custom instructions for AI models, and techniques to enhance AI-generated content, such as Pokémon battle dialogues.

The Latent Space channel discussed the upcoming Paper Club presentation on 1-bit Large Language Models (LLMs), featuring the BitNet b1.58 paper. The research explores the effectiveness of ternary 1-bit LLMs compared to full-precision models in terms of performance and cost-efficiency. Links shared include the arXiv submission for further reading and joining the LLM Paper Club for insights. Discussions in the chat revolved around visual and audio issues during the session, along with insights into 1-bit LLMs and their training processes. Future paper club topics, including TimeGPT and BloombergGPT, were suggested for exploration.

HuggingFace ▷ #announcements

• Gemma 1.1 Instruct 7B Takes Center Stage: The newer version of Gemma 1.1 Instruct 7B is available on HuggingChat with expected net improvements. Users are encouraged to try it out here.

• CodeGemma Unveiled: CodeGemma and RecurrentGemma models are now available on HuggingFace, optimized for on-device code completion.

• A More Economical Hugging Face: Compute prices have been slashed up to 50% for Spaces and Inference endpoints, making them more cost-effective.

• Community Insights Get Revamped: Community blogs have been upgraded to 'articles' with new features like upvotes and activity feed presence.

• Serverless GPUs and Bonus ML Content: Hugging Face introduces serverless GPU inference with Cloudflare and adds a bonus unit focusing on Classical AI in Games to its ML for Games Course.

HuggingFace ▷ #general

• Checkpoints Saving Woes: Troubleshooting checkpoints saving issues with a model using TrainingArguments.

• Gradio Questions Forum: Providing links for Gradio-related inquiries channels on Discord.

• Call for SEO Prompts: Seeking prompts for SEO blog articles.

• Learning Journey for AI Novices: A new member seeks advice on starting with LLMs or image generator AI.

• Model Error Queries and Troubleshooting: Discussions on addressing model errors.

HuggingFace ▷ #today-im-learning

• Learn NLP in a Day: Shared a comprehensive guide for sentiment classification with the IMDB movie dataset.

• Navigating the Maze of Package Management: A video discussing various package management tools.

HuggingFace ▷ #cool-finds

• SimA: AI Trained Across Many Worlds: Introducing SimA, a generalist AI agent for 3D virtual environments.

• Qdrant Meets DSPy for Enhanced Search: Detailing the integration of Qdrant with DSPy for advanced search capabilities.

• Karpathy's Tweet Sparks Curiosity: Stirring conversations among enthusiasts, requiring a direct visit to the link for details.

• Explore HuggingFace Models with Marimo Labs: Marimo Labs team developed an interface for experimenting with HuggingFace models.

• Multilingual Information Extraction on HuggingFace: Discover a powerful multilingual information extraction model on HuggingFace Spaces.

• Quantum Leap for Transformers with Quanto: Showcase of employing Quanto for quantizing Transformers models.

HuggingFace ▷ #i-made-this

• Deep Dive into Deep Q-Learning: A collection of Deep Q-Learning projects shared on GitHub.

• Tracing Data Science Evolution: Introducing RicercaMente, a collaborative project mapping the evolution of data science.

• Local LLMs Unleashed with everything-rag: A local chatbot assistant supporting any LLM and data, including personal pdf files.

• Fashion Forward with Virtual Try-On: Creating a virtual try-on system using IP-Adapter Inpainting.

• Insights on Model Layer Behavior: Discussion on model layers' connection variations based on input types and potential for pruning in models like Mixtral 8x22B.

• Python Debugging Advice: Suggestions were made to understand Python classes, functions, decorators, imports, and objects for better code implementation. Recommendations include removing PyTorch implementations for testing, enabling eager execution on JAX or TensorFlow, and utilizing Python's breakpoint() for tracking variable changes during code execution.
• Navigating Colab's Features: Tips were shared on using function_name for documentation lookup, object_name.__class__ to find an object's class, and inspect.getsource to print a class's source code efficiently.
• Gratitude Expression: A member acknowledged community help with a simple '🙏' emoji.
• Link to Prior Inquiry: Reference was made to a past question in the ask-for-help section by providing a Discord channel link, highlighting improved understanding of PyTorch since the initial query.
• Request for Dialogue System Paper: A request was made for research papers or work related to building a multi-turn dialogue system for intelligent customer service.
• Mathematical Breakdown of Samplers Needed: A member sought recommendations for papers on sampling methods post ddpm and ddim, focusing on foundational samplers in the field.

The section discusses the importance of matrix shapes in performance optimization, particularly in matrix multiplication. It provides an example of the optimal configuration for matrix multiplication to avoid negative performance impacts. Additionally, there are inquiries about the compatibility between the MIT license and the Apache 2.0 license, seeking advice from individuals knowledgeable about licenses.

A recent discussion in the LAION channel reevaluates the concept of 'Zero-Shot' generalization in multimodal models like CLIP and Stable-Diffusion. The analysis suggests that the quality of data is crucial for CLIP models, especially when dealing with less common concepts. Google has advanced with a larger Griffin model, incorporating an additional 1 billion parameters for improved performance. Moreover, a new study challenges traditional LLM training methods with pair-wise optimization strategies, showing significant performance improvements over Reinforcement Learning from Human Feedback (RLHF). This alternative approach indicates potential advantages in optimizing directly over general preferences compared to point-wise reward methods.

This section delves into discussions surrounding post-training improvements for large language models (LLMs) through preference feedback from oracles, including the teaching of language models to self-improve with general preferences. In the OpenInterpreter community, there is excitement over the release of GPT-4, with users noting its enhanced performance and integrated vision capabilities. Additionally, discussions touch on models like Mixtral 8x22b and Command r+, with users comparing performance within the OpenInterpreter framework and the compute power required. The section also covers troubleshooting and installation issues related to OpenInterpreter devices, as well as community inquiries and updates on customer orders. Furthermore, the section presents experiences and debates within the Interconnects community, such as the introduction of new language model models like Griffin and Mixtral, skepticism towards benchmark optimization, and preferences for practical model improvements over theoretical advancements. Lastly, the section highlights a step-by-step guide for adding custom accelerators to tinygrad and community discussions regarding network examples, technical issues, and conversions within the tinygrad environment.

Axolotl Update: The community discussed adding dataset versioning to Axolotl and a technique called PiSSA for improving fine-tuning results in LoRA layers.

Generative AI Hackathon Announcement: The Samsung Next 2024 Generative AI Hackathon focusing on Health & Wellness and Mediatech tracks was announced.

Seeking Compatible Frontends: Question raised about web self-hostable frontend compatible with various APIs.

Modular (Mojo 🔥) Updates:

• Discussions on C formatting hints, API documentation, and contributions beyond Mojo stdlib.
• New Mojo-UI project and call for community feedback on Mojo traits.
• Mention of async/await features and Mojo's roadmap.

Modular (Mojo 🔥) Community Projects:

• Platforms like Mojo GPT and Lightbug framework gaining momentum.
• Refactoring code for simplicity and a Curated List of Mojo Resources on GitHub.

Mixtral Model Updates from DiscoResearch:

• Updates on Mixtral-8x22B model on Hugging Face and model conversion scripts shared.
• Benchmark scores released for Mixtral models and license confirmation under Apache 2.0.
• Discussion on AGIEval results and model runs on a virtual Large Language Model (vLLM) setup.

DiscoResearch

• New LLM Merging Tool Unveiled: A new library called mergoo has been shared to simplify and improve the efficiency of merging multiple Large Language Model (LLM) experts inspired by a paper from March.
• RAG Benchmarking Reveals Odd Behavior: An issue in the DiscoResearch/DiscoLM_German_7b_v1 model showed varied performance outcomes due to the placement of a line break in the ChatML template.
• Line Break Impact Investigated: Discussions were triggered about potential data loading/script issues due to a line break affecting benchmarks, prompting a review of training data application.
• Model Formatting Issues Explored: Speculation arises about modifying the tokenizer configuration for DiscoLM_German_7b_v1 to address performance anomalies.
• Generalizability of Line Break Issue in Question: Questions are raised regarding whether the sensitivity to line break formatting is specific to DiscoResearch/LeoLM models or a broader phenomenon affecting other models. The topic remains open for further investigation.

LLM Perf Enthusiasts AI

• Good Morning with a Tweet: A Twitter link potentially related to new updates or discussions from OpenAI shared in the channel.
• Surprising Benchmark Results: Sonnet and Haiku outperformed GPT-4 Turbo and Opus in a quick vision benchmark, sparking interest in further exploration.
• Exploration of GPT-4 Turbo Features: Highlight of promising function calling and JSON mode in GPT-4 Turbo for building with vision models.
• Is It GPT-4.5 or not?: Humorous discussions around the latest model improvements, with varying opinions on the update.
• Comparison of AI Coding Abilities: Brief exchange on coding capabilities of the latest models, including comparisons with Gemini 1.5 and benefits of copilot++.

Datasette - LLM (@SimonW)

• LLM Help Command Performance: Concerns raised about the speed of the llm --help command and its implications on security.
• Benchmarking LLM Help: Different users reported varying performances of llm --help command, sparking discussions related to system configurations.
• Reinstallation Resolves Issues: Reinstalling llm resolved speed problems and errors encountered, suggesting a fresh install could alleviate operational issues.
• LLM Command Hiccups on MacOS: Users faced command hanging issues on macOS but not on Ubuntu, mentioning differences in shell environments.

Skunkworks AI

• Seeking Benchmark Comparisons: Inquiry made about performance benchmarks for models like phi-2, dolphin, and zephyr on the HumanEval dataset.
• Skepticism on Benchmarks: Discussion on skepticism towards benchmarks and the recommendation of a human-ranked leaderboard for trustworthy results.
• First AGIEval Results for Mistral 8x22b: Sharing of Mistral 8x22b model's first AGIEval results, indicating superior performance over other base models with detailed updates shared in provided links.

Mozilla AI

• Fine-Tuning GPU Usage: Member discovered improved performance with a lower -ngl value to fit GPU memory limitations.
• Adaptive Layer Offloading in Question: Inquiry made if llamafile could offload layers to fit user VRAM limitations.
• ollama Offers LLM Flexibility: Praise for ollama's model layer distribution handling and sharing of GitHub link detailing its implementation.

Alignment Lab AI

• Tuning into Remix Music AI: Excitement shared about a remix music model, including a link to listen to the music.
• Call for Coding Support: Request for coding assistance through direct messaging.

For more information on the mentioned links and details, you can refer to the corresponding sections above.