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- Qwen 2.5 Outperforms Llama 3.1 405B
Qwen 2.5 Outperforms Llama 3.1 405B
PLUS: Opensource Real-time Speech AI Model, AutoRAG LLM app tutorial
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Today’s top AI Highlights:
Alibaba drops Qwen 2.5 opensource models; outperforming Llama 3.1 405B
Run end-to-end real-time voice AI locally on Apple silicon
Google is bringing its text-to-video model Veo for YouTube Shorts
Visualize, track, and debug AI agents with a few lines of code
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AI Tutorials
Curious about building powerful LLM Apps using RAG and AI agents? Dive into our in-depth code walkthrough on creating an AutoRAG LLM app with GPT-4o and a vector database. It's a hands-on guide that takes you through the entire process, step by step.
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Latest Developments
French AI research lab Kyutai created quite a stir when they demoed their real-time AI voice assistant Moshi two months back (and we’re still waiting for OpenAI’s). They have now open-sourced Moshi and released its technical report, the model weights, its associated audio codec Mimi, and the full streaming inference code in PyTorch, Rust, and Apple MLX.
Moshi is an advanced voice AI that can process and generate audio simultaneously. It can process two audio streams for real-time listening and responding. This helps with natural flow of conversations. It was the first voice-enabled AI openly available for public testing and use.
Key Highlights:
Core Architecture - Moshi is based on Helium (a 7B LLM), Mimi (a neural audio codec), and a multi-stream system that handles audio from both the user and Moshi simultaneously. This allows real-time, interactive conversations with dynamic responses, backchannelling, and interruptions.
Realistic and natural - Moshi has a very realistic and emotionally nuanced voice, and can talk in 70 expressions. With a latency of ~160 ms, the conversations feel real-time and natural.
Open Access - The release includes pre-trained weights for both male and female voices, with the ability to fine-tune models on custom voices and settings. The full streaming inference code in Pytorch, Rust and MLX is available here.
Opensource AI is on fire, closing the gap between proprietary and open models. Qwen had its biggest release yesterday with the new Qwen 2.5 series of LLMs. The models come in various sizes, along with specialized models for coding (Qwen2.5-Coder) and mathematics (Qwen2.5-Math). All opensource models, except for the 3B and 72B variants, are licensed under Apache 2.0. The release also includes API access to flagship models Qwen2.5-Plus and Qwen2.5-Turbo.
The best part? The 72B model outperforms Llama 3.1 405B, Mixtral 8×22B, and Mistral Large.
Key Highlights:
Capabilities - Qwen2.5 models support 128K tokens and can generate up to 8K tokens. They also maintain multilingual support for over 29 languages. Trained on a massive dataset of 18 trillion tokens, they also demonstrate better instruction following, structured data understanding, and JSON output.
Qwen2.5-72B Performance - The 72B parameter model achieves top-tier performance, rivaling even larger models like Llama 3.1 405B, Llama-3.1-70B, Mistral-Large-V2, and DeepSeek-V2.5 across various benchmarks.
Specialized Coding Model - Qwen2.5-Coder, trained on 5.5 trillion tokens of code-related data, delivers competitive coding performance even in smaller model sizes, making it a powerful tool for developers.
Math-Focused Model - Qwen2.5-Math supports both Chinese and English, incorporating advanced reasoning methods like Chain-of-Thought (CoT) and Tool-Integrated Reasoning (TIR) for tackling complex mathematical problems.
Open-Source - Qwen 2.5 models are available now on Hugging Face and can be integrated with tools like vLLM and Ollama. Here’s the GitHub repo. Start experimenting with these models to take your AI projects to the next level!
Quick Bites
Runway and Lionsgate, the film production giant, are joining forces to develop a custom AI model based on Lionsgate’s film catalog, to enhance film production process. This collaboration will provide filmmakers with tools to use AI in pre-production and post-production processes.
YouTube is introducing AI tools to help creators generate ideas and concepts, titles, thumbnails, and even videos for YouTube Shorts using Google DeepMind’s text-to-video AI model Veo. These features for YouTube creators are expected to roll out late this year or early next year.
GitHub has made Copilot Extensions available in public beta to all Copilot users and open for any developer to create extensions. Alongside, they have also released a comprehensive Copilot Extensions Toolkit, designed to equip developers by centralizing the information they need to build quality extensions.
Tools of the Trade
AgentOps: Track, debug, and analyze AI agents across different platforms. With just a few lines of code, it provides detailed session replays, cost management, and security features to ensure smooth agent operation from prototype to production.
Opik: Opensource platform to evaluate, test, and monitor LLM applications throughout development and production. It helps log traces, compute evaluation metrics, and integrate with CI/CD pipelines for reliable performance tracking and debugging.
Backprop: A cloud platform offering on-demand GPU instances for AI tasks, with simple pay-as-you-go pricing. It provides pre-built AI environments, fast setup, and no hidden fees.
Awesome LLM Apps: Build awesome LLM apps using RAG to interact with data sources like GitHub, Gmail, PDFs, and YouTube videos through simple text. These apps will let you retrieve information, engage in chat, and extract insights directly from content on these platforms.
Hot Takes
I think people whose main skill is to write code will have a hard time staying employed.
You'll need much more than that to stay competitive. ~
SantiagoAmazing. ChatGPT-o1 has self-reasoning and now outperforms PhD experts. Can’t wait to have an AI-powered humanoid robot to stack my dishwasher ~
David Sinclair
Meme of the Day
SF tech bro was a little too deep in Founder Mode
— Jason (@mytechceoo)
10:13 PM • Sep 13, 2024
That’s all for today! See you tomorrow with more such AI-filled content.
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