[AINews] Llama 3.2: On-device 1B/3B, and Multimodal 11B/90B (with AI2 Molmo kicker) • ButtondownTwitterTwitter

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Updated on September 25 2024


AI Twitter Recap

AI Twitter Recap

  • OpenAI is rolling out an advanced voice model for ChatGPT Plus and Team users with lower latency and personalization features. The update includes new voices, improved accents, and the ability to interrupt long responses.
  • Google announced updates to Gemini 1.5 Pro and Flash with improvements in long context understanding, vision, and math. Price reductions for Gemini 1.5 Pro, faster output, lower latency, and increased rate limits were highlighted. The models can now handle large volumes of data such as 1000-page PDFs and 10K+ lines of code.
  • OpenAI's models continue to perform well in benchmarks, showcasing their leading position in the AI field.

AI Reddit Recap

AI Reddit Recap

  • Theme 1. High-Speed Inference Platforms: Cerebras and MLX
    • Cerebras Platform: Achieving impressive inference speeds with Llama models. Potential applications and comparisons with alternative platforms mentioned.
    • MLX ParaLLM Library: Demonstrating a speed improvement for Mistral models through batch generation. Energy efficiency tests and applications discussed.
  • Theme 2. Qwen 2.5: Breakthrough Performance on Consumer Hardware
    • Qwen 2.5 on 4x P100 GPUs: Speed details and comparison with Pixtral model. Performance on P100 GPUs highlighted.
    • Qwen 2.5 on Dual RTX 3090s: Efficiency details and configurations provided for achieving high performance with Docker compose setups. Hardware requirements and performance comparisons discussed.
  • Theme 3. Gemini 1.5 Pro 002: Google's Latest Model Impresses
    • Gemini 1.5 Pro 002 Benchmark: Impressive performance across various benchmarks and cost-effective pricing strategies. User comparisons and discussions on the consumer version highlighted.
    • Updated Gemini Models: Competitive pricing and improved benchmarks for the Gemini 1.5 Pro 002 model. User perspectives and discussions on pricing and performance shared.
  • Theme 4. Apple Silicon vs NVIDIA GPUs for LLM Inference
    • Hugging Chat Mac App: Release of Hugging Chat Mac App enabling local model runs. Performance comparisons between Mac devices and RTX 4090 discussed.

OpenAccess AI Collective (axolotl) Discord

  • Run Pod Issues Dismay Users: Reports of illegal CUDA errors on Run Pod prompt advice on switching machines. User humor lightens frustration levels.
  • Molmo 72B Takes Center Stage: Allen Institute for AI's Molmo 72B garners attention with state-of-the-art benchmarks on image-text pairs, aiming to compete with GPT-4o.
  • OpenAI's Leadership Shakeup Rocks Community: Discussion ensues after OpenAI's CTO resignation, stirring speculation on the organization's future direction.
  • Llama 3.2 Rollout Excites All: Introduction of lightweight models for edge devices generates excitement, with sizes from 1B to 90B discussed among users.
  • Meta's EU Compliance Quagmire: Restrictions on European access, coupled with possible license changes affecting model availability, spark debates on company motivations.

Discord Channels Highlights

This section provides insights and updates from various Discord channels related to AI and technology. It covers a wide range of topics including new launches, discussions on model performance, job opportunities, and technical clarifications. Members engage in conversations about advancements in AI models, fraud detection, edge capabilities, developer hiring, and more. The content reflects the dynamic and diverse discussions happening in these Discord communities.

Memory challenges with vLLM on Tesla T4

Users reported difficulties running vLLM with Llama 3.1 on a Tesla T4 GPU due to VRAM limitations, primarily when loading multiple models simultaneously. One user successfully executed the model separately but encountered issues with VRAM exhaustion when attempting to run additional models together.

Nous Research AI Research Papers

MIMO Framework Revolutionizes Character Video Synthesis:

A novel framework called MIMO proposes a solution for realistic character video synthesis by generating videos with controllable attributes like character, motion, and scene through simple user inputs. It aims to overcome limitations of 3D methods requiring multi-view captures and enhances pose generality and scene interaction using advanced scalability to arbitrary characters.

Advice Needed for Resume ATS and Job Recommendations:

One member shared their experience working on a resume ATS builder and a job matching and recommendation system, feeling lost in their search for quality research papers. They seek guidance from others on how to efficiently approach their research efforts in this area.

Link mentioned: Paper page - MIMO: Controllable Character Video Synthesis with Spatial Decomposed Modeling: no description found

Innovations and Challenges

Members are interested in adding voice feedback and automatic documentation searches to enhance user experience. Discussions revolve around vector database options like Chroma, Qdrant, and PostgreSQL with vector extensions. An incident with Sonnet 3.5 led to elevated errors for users, impacting availability and performance. During Meta Connect discussions, insights were shared about Meta's new models, including Llama 3.2. Concerns were raised about Aider's functionality with PDF files and how switching models can lead to better outputs. Strategies for managing token usage and integrating external libraries in Aider were also discussed.

Hardware and Integration Issues in LM Studio

Users are eager for support for the 11B multimodal model in LM Studio, but integration issues with SillyTavern have been reported, particularly related to server communication and response generation. Troubleshooting suggests that SillyTavern may require specific task inputs rather than freeform text prompts. Additionally, concerns have been raised over the multimodal model capabilities in Llama 3.2, with users seeking true multimodal capabilities similar to GPT-4. Benchmark results for Llama 3.2 show varied performance, with the 1B and 3B models achieving scores of 49.3% and 63.4%, respectively. Future support and development plans include expectations for additional support for new models and ongoing discussions about implementing various quantization levels. Users express optimism regarding the integration of NPU capabilities and faster inference speeds for future releases.

Interconnects: Nathan Lambert Posts

Llama 3.2 has been officially launched with model sizes including 1B, 3B, 11B, and 90B, aiming to enhance text and multimodal capabilities. Initial reactions suggest some rough edges but better availability during less busy hours. In addition, members discuss how Molmo, a new multimodal model, is outperforming Llama 3.2 90B, disrupting the landscape.

Discussion on GPU Mode Topics

The GPU Mode discussions covered a variety of topics related to GPU optimization, model deployments, and community projects. Members shared insights on the latest advancements, such as Meta integrating Llama for edge devices, mixed feelings about the deployment of Llama 3.2, and hints fuelling speculation around potential updates. Additionally, the discussions touched on Torch Profiler file size issues, PyTorch kernel performance improvement, and CUDA code integration, showcasing a collaborative and knowledge-sharing environment among members.

Cohere Projects

Exploring Cohere applications in Embedded Systems:

A user inquired about examples of Cohere being used in embedded systems, expressing interest in integrating it into a smart telescope mount for their capstone project. Discussion ensued about the potential of finding celestial objects using embeddings from the Messier catalog.

Smart Telescope Project Excites Community:

The user shared excitement about their project aimed at automatically locating 110 objects from the Messier catalog, with plans for further expansion beyond that. Community members enthusiastically supported the idea, encouraging collaboration and offering resources.

Cohere Cookbook as a Resource:

Members highlighted the availability of the Cohere Cookbook on their website, providing ready-made guides for using Cohere’s generative AI platform. These guides cover a range of use cases, such as building powerful agents and integrating with open source software.

Cases for Cohere

  • The Cohere Cookbook offers categories like embedding and semantic search crucial for AI projects. Members are encouraged to explore specific sections relevant to their project needs.
  • Code Examples on GitHub provide practical implementation and experimentation with Cohere's platform through shared notebooks and code examples.

DSPy Chat Discussions

This section discusses various topics related to DSPy, including utilizing DSPy for text classification and orchestrating user queries for conversational agents. It also mentions the launch of new DSPy features such as automatic experiment tracking and checkpoint state tracking in Langtrace, and the upcoming availability of these features for the Typescript version Ax. The discussions highlight the potential applications and enhancements that DSPy brings to users in the field of AI and machine learning.

Training Speed Issues and Code Bugs

Two key issues were highlighted in this section regarding training speed and code bugs. A user expressed frustration with slow training in tinygrad, even after upgrading to a 4090 GPU. Another user identified a bug in their sampling code affecting output quality during inference. Both cases underline the importance of addressing performance bottlenecks and code quality to enhance training efficiency and model output.


FAQ

Q: What is MIMO framework and how does it revolutionize character video synthesis?

A: MIMO is a novel framework that proposes a solution for realistic character video synthesis by generating videos with controllable attributes like character, motion, and scene through simple user inputs. It overcomes limitations of 3D methods requiring multi-view captures and enhances pose generality and scene interaction using scalability to arbitrary characters.

Q: What updates did Google announce for Gemini 1.5 Pro and Flash models?

A: Google announced updates for Gemini 1.5 Pro and Flash with improvements in long context understanding, vision, and math. Price reductions for Gemini 1.5 Pro, faster output, lower latency, and increased rate limits were highlighted. The models can now handle large volumes of data such as 1000-page PDFs and 10K+ lines of code.

Q: What are some highlights of the advances made by OpenAI in the AI field based on benchmarks?

A: OpenAI's models continue to perform well in benchmarks, showcasing their leading position in the AI field.

Q: Can you explain the themes discussed in the AI Reddit Recap related to high-speed inference platforms and breakthrough performance on consumer hardware?

A: The AI Reddit Recap discusses themes like high-speed inference platforms involving Cerebras achieving impressive speeds with Llama models and MLX ParaLLM Library demonstrating speed improvements for Mistral models. It also covers breakthrough performance by Qwen 2.5 on different GPUs and the impressiveness of Gemini 1.5 Pro 002 by Google.

Q: What challenges were faced by users running vLLM with Llama 3.1 on a Tesla T4 GPU?

A: Users reported difficulties due to VRAM limitations when loading multiple models simultaneously. Some managed to run the model separately but faced issues with VRAM exhaustion when running additional models together.

Q: What features and discussions were highlighted during Meta Connect discussions?

A: Insights were shared about Meta's new models, including Llama 3.2, concerns raised about Aider's functionality with PDF files, strategies for managing token usage, and integrating external libraries in Aider.

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