[AINews] Gemini Pro and GPT4T Vision go GA on the same day by complete coincidence • ButtondownTwitterTwitter

This section provides a recap of the latest discussions and developments from the AI community on Twitter. It covers performance updates on the Cohere Command R+ model, releases and updates of notable open models like Code Gemma and Griffin architecture by Google, as well as discussions on emerging trends such as AI for code generation, AI outperforming humans in coding tasks, scaling laws for language models, DSPy for language model programs, and the physics of language models. It also includes humorous posts and memes related to the topic.

An overview of discussions and developments in various AI-related Discords, including new AI model releases, efficient LLM training and deployment approaches, AI assistants and multimodal interactions, open-source AI frameworks and community efforts, as well as miscellaneous updates such as efficiency breakthroughs in LLM training and inference, advancements in Retrieval-Augmented Generation (RAG), architectural explorations and training techniques, and AI-generated art debates. The summaries cover a range of topics from model performance to community interactions and debates around emerging AI technologies.

Cool New Tools for AI Engineers

Hugging Face released Gemma 1.1 Instruct 7B with coding capabilities and reduced compute prices. They also introduced OCR datasets and Gradio's API Recorder feature.

AI Community's Learning Hub

Hugging Face shared a GitHub repo for NLP sentiment classification and encourages learning through tutorials like Gradio's and Langchain's.

Creative AI Developments to Watch

Innovations include BeastBot for viral content, Ragdoll Studio for character generation, and the rise of Deep Q-Learning applications showcased on GitHub.

AI Model Debugging and Optimization Conversations

Members discussed struggles with TorchScript export, Mistral on A100 GPUs, and recommended a Google Colab notebook for solutions.

Engaging Discussions in Specialized AI Topics

Discussions covered benchmarking hardware, model recognition, GPT-2 for summarization, and integrating contrastive loss in vision models.

CosXL Creation Covered by Contract: Stability AI introduced CosXL, a new model under a non-commercial research community license agreement which requires sharing user contact details, stirring up debate about data privacy and usage ethics.

Pulling Pixels from Prose Outside Stable Databases: Engineers shared methods on generating images from text not contained in the Stable Diffusion database and referred to Diffusers' documentation with a note on the software update to version 0.27.2.

AI's Role Reshaping the Freelance Realm: A blog post analysis of 5 million freelancing jobs on Upwork offered insights into how AI is influencing job displacement, a topic critical for engineers exploring freelance opportunities.

Model Training Tendencies: Discussions emerged over the efficiency of EDM schedules and offset noise in model training, suggesting a divided stance on best practices among practitioners.

Griffin Tops Transformers: Google's new Griffin architecture reportedly exceeds transformer performance, introducing an extra billion parameters and enhanced throughput which could signal shifts in architectural design choices.

A discussion took place in the Nous Research AI Discord channel about various updates in the Project Obsidian. The introduction of a new vision-language model called nanoLLaVA focused on efficiency for edge devices. Updates on integrating the model into Obsidian and Hermes Vision were discussed. Additionally, enhanced ChatML capabilities in LLaVA were announced. The conversation highlighted the anticipation surrounding upcoming releases and advancements in the field of data analytics.

A new model called Dolphin 2.8 Mistral 7b v0.2 has been introduced, with ongoing efforts in supporting and quantizing the model for improved performance. The GGUF quantization of the model has been completed, curated by bartowski. Discussions on hardware utilization include preferences for model quantization and the importance of VRAM. Users also debated the performance of CPU upgrades, the superiority of GPUs for LLM inferencing, the usage of multiple GPUs, and the minimum GPU requirements for specific models. Additionally, there were chats on LM Studio beta releases, model integration announcements, and Gemma models by Google. OpenAI discussions focused on GPT versions, AI artistry, and tasks breakdown with LLMs. LlamaIndex section highlighted improved RAG techniques, evaluations, multimodal applications, and upcoming events on building enterprise-grade RAG systems.

Adding Documents to OpenSearch Vector Store:

A member faced an issue where new data was not being added to the OpenSearch vector store despite using an index insert method. They tried index.refresh_ref_docs, but the problem persisted, indicating a need for document store and vector store layers. Reference to related GitHub notebook.

OpenAI Quota Exceeded:

A user encountered an error code 429 from OpenAI, indicating they had exceeded their quota, clarified to be an OpenAI limitation, not LlamaIndex.

Guidance on RAG with OpenSearch Vector DB:

Participants discussed using RAG with OpenSearch as a vector store, emphasizing inserting new documents properly and referring to OpenSearch documentation for instructions.

OCR Enhancement for PDFReader:

A member struggled with extracting text from image-based PDFs using PDFReader and found success with OCRmyPDF after exploring alternative solutions like LlamaParse. Introduction to LlamaParse.

Embedding Generation Speed Optimizations:

Tips were shared on improving the speed of generating embeddings on AWS Lambda with LlamaIndex 0.9, including using embedding.get_text_embedding_batch(text_chunks) and adjusting the embed_batch_size parameter for efficiency.

Virtual Long LLM (vLLM) Setup Queries:

A user sought advice on setting up Mixtral with a detailed evaluation template and vLLM, with recommendations on using function hooks like completion_to_prompt.

Dealing with Extended Metadata in LlamaIndex:

Best practices were discussed when faced with excessively long metadata, suggesting using metadata filters and potentially excluding certain metadata from the document store.

Difficulty with Documentation Links:

A member expressed frustration with many LlamaIndex documentation links leading to non-existent GitHub pages, highlighting a need for updated resources or examples.

Explorations of Postgres Integration in LlamaIndex:

Misleading references to MongoDB in the PostgresDocumentStore class documentation led to discussions on the suitability of Supabase for both VectorStore and Docstore, emphasizing the need for documentation improvements.

Implementing Role-Based Access Control (RBAC) on RAG:

Inquiries were made about implementing RBAC on RAG models, with potential utilization of metadata filters for data access control.

Request for Actionable Gemini LLM Examples:

A user requested examples tailored for Gemini LLM, similar to those in the OpenAIAgent cookbook, aiming to adapt existing OpenAI examples for Gemini.

Inquiry About Document/Node Retrieval from Vector Stores:

Users questioned how to retrieve all nodes and embeddings from a vector store, with suggestions to access the data through the vector db client or explore the underlying vector store attributes within the index.

Streaming Response Challenges in Server Endpoints:

Challenges were faced in streaming responses to the client-side, with guidance on using specific server response types suitable for streaming and mentions of FastAPI and Flask.

HuggingFace ▷ #today-im-learning:

• A tutorial for NLP sentiment classification using the IMDB movie review dataset was shared. It provides a generic way to solve NLP tasks efficiently. Check out the GitHub repository.

HuggingFace ▷ #cool-finds:

• A tutorial on Hugging Face and Langchain in 5 minutes was shared. It provides insights on using Hugging Face and accessing over 200,000 AI models for free. Additional links to Hugging Face tutorials were also shared.
• A research paper was discussed detailing dynamic FLOP allocation in transformers, focusing on optimizing the allocation for varying layers. The study introduces a top-k routing mechanism method within a static computation graph. The paper can be viewed on arXiv.
• DeepMind introduced SIM-α, a generalized AI agent for 3D virtual environments, aiming to scale instructable agents across simulated worlds. The full document can be accessed through DeepMind's PDF link.
• An article on Medium discussed integrating Qdrant, a vector search engine, with DSPy to enhance search functions for AI applications. More details in the Medium post.

HuggingFace ▷ #i-made-this:

• Various innovative projects were shared, such as BeastBot for creating viral content, Ragdoll Studio for character creation, and Deep Q-Learning Applications GitHub repository for projects. Additionally, RicercaMente, an open-source project mapping the history of data science, was introduced. Find more details about the projects and repositories.

HuggingFace ▷ #computer-vision:

• Discussions on diffusion models, XCLIP pretraining challenges, computer vision problems, transitioning from text to vision deep learning, and the importance of contrastive loss with large batch sizes were addressed.

HuggingFace ▷ #NLP:

• Conversations around Mistral training, GPT-2 for summarization, Mistral 7B and RAG combination, and the era of prompting for summarization using GPT-2.

HuggingFace ▷ #diffusion-discussions:

• Discussions involved overcoming TorchScript export issues, saving custom modules in Diffusers, exploring schedulers/samplers behavior, and collaborative debugging through a shared Google Colab notebook.

HuggingFace ▷ #gradio-announcements:

• Gradio version 4.26.0 introduced an API Recorder feature and included important bug fixes addressing slow page load times and crashing caused by rapid chatbot updates. The full changelog and more information can be found here.

Eleuther ▷ #general:

• Discussions on branding choices, disappearance of GPT-3.5 information, mysteries around Claude 3 Opus model, speculation on model architecture and pricing, and skepticism on optimistic claims about model capabilities.

Eleuther ▷ #research:

• Extensive discussions on learning rate behavior, moving averages in optimizers, knowledge storage efficiency in models, dense vs. sparse training in MoEs, and the effectiveness of the LAMB optimizer.

Meta sponsored a study on LLM knowledge capacity involving 4.2 million GPU hours, which took researchers four months to submit 50,000 jobs. Legal review by Meta added another month. A calculation revealed that Meta's GPU hours equate to roughly 479 years of continuous compute. The idea of porting GPT-2 training code to CUDA was mentioned as a benchmark project for efficiency and performance advancement, with a relevant GitHub repository shared. In response to interest in CUDA porting projects, a working group has been proposed to connect enthusiasts.

Training LLMs in C

A link to Andrej Karpathy's Tweet was shared, introducing llm.c, a lean implementation of GPT-2 training in pure C with only 1,000 lines of code. Discussions sparked enthusiasm for porting this C-based code to CUDA, leveraging the compactness of llm.c. Members showed interest in integrating the code into their libraries and sought clarification on license compatibility. The focus on practical application and knowledge sharing underscored the impact of the discussions in the community.

Python Version Compatibility Resolved:

• Members share experiences with Python version compatibility issues, highlighting the switch from Python 3.11.4 to 3.10 for improved functionality.

Troubleshooting pyaudio on M1 Mac:

• Solutions for pyaudio issues on M1 Macs are discussed, including reinstalling portaudio and trying different Python versions.

Excitement and Installation Woes with 01:

• Community members express excitement over bot functionality and discuss challenges faced during installation on Windows.

Raspberry Pi and Desk Robot Projects:

• Potential uses of Raspberry Pi for building bots and desk robots are explored, with a member planning an open-source project related to desk bots.

Jet MoE and Lepton AI's Simple Platform:

• Discussions include Jet MoE integration into Hugging Face's transformers and praise for Lepton AI as a user-friendly cloud-native platform.

Model Comparisons and Llama 3 Anticipation:

• Conversations on model comparisons and anticipation for Meta's Llama 3, with a focus on non-English performance and gemma tokenizer for untrained tokens.

• F-strings in Mojo Still Pending: Users are waiting for the f string functionality in Mojo for Python-like string formatting.
• Exploration for Local Documentation Command: Discussion on the lack of a command to download Mojo documentation locally.
• Temporary Solution for String Formatting: Using C-style formatting as a temporary solution in Mojo.
• Mojo API Documentation for Beginners: Shared API documentation link to help beginners.
• Contribute to Mojo's Evolution: Announced the open-sourcing of Mojo's standard library and a step-by-step guide for contributing.
• Karpathy's New GitHub Repository: Released a GitHub repository for GPT-2 training in pure C.
• Django Performance with Mojo: Discussions on using Django with Mojo for improved performance.
• Compiler Bug Report for Mojo: Encountered a compiler bug with async function pointers in Mojo.
• Flames of Progress in Nightly Builds: Updates on the Mojo nightly build release and goal for automatic weekday releases.
• Stability AI's New Model Release: Released CosXL model with a non-commercial research community license agreement.

Summary:

• Twists in Generative Models: Mentioned the irony of autoregression for image models and diffusion for text models, showcasing a reversal of previous trends.
• Vogue Cycles in Model Approaches: Clarified the presence and benefits of autoregressive image models for scalability and predictability of text and images.
• Potential Strengths of Autoregressive Models: Highlighted the potential for autoregressive image generation models to lead to text-to-video models with proper video tokenization, as per insights from the CM3leon paper.
• Griffin Triumphs over Transformers: Google's Griffin architecture reported to surpass transformers in performance with additional parameters and improved throughput for long contexts.
• Reevaluating Zero-Shot Generalization: Discussed the limitations of zero-shot generalization in multimodal models like CLIP, emphasizing the importance of data quality and quantity as articulated in a recent paper.