Code Llama

Making Transformers Sing - with Mikey Shulman of Suno

Mar 14, 2024 · 58:58

In this episode of Latent Space, hosts Alessio and Shawn Wang interview Mikey Shulman, CEO of Suno, about making transformers sing. Suno uses transformers to predict audio tokens end-to-end, avoiding baked-in musical knowledge, with a tokenization secret sauce that also includes non-music audio for better vocal realism. Their models are relatively small (far below 175B parameters) due to latency needs, and they prioritize scaling research over brute-force size. Over half of Suno users employ expert mode, tweaking lyrics and style prompts, rather than easy mode. Shulman argues Suno is not the 'Midjourney of music' because music is inherently social and synchronous, unlike images. He demoed live generation, showing control via tokens like [beat drop] and style modifiers, and revealed future plans for collaborative concerts, continuous DJ modes, and personalized models. He also advocates for hiring economists to avoid Goodhart's law pitfalls in ML benchmarks, especially in audio where aesthetics matter most.

Beating GPT-4 with Open Source Models - with Michael Royzen of Phind

Nov 3, 2023 · 1:18:54

Michael Royzen, co-founder and CEO of Phind, explains how his company built a GPT-4-beating open-source model for developer Q&A. Royzen recounts founding SmartLens in high school, shifting to NLP after a Hugging Face demo, and creating an internet-scale LLM-powered RAG system in January 2022. He details Phind’s pivot to programmers, the Hacker News launch that gave it 1,500 points, and Paul Graham’s role in naming the company and introducing Ron Conway, who then connected Phind to NVIDIA for GPU access. Royzen argues that Phind’s model, fine-tuned from Code Llama 34B with extra data, closes the gap with proprietary models—especially on code reasoning—and that open-source will win the enterprise because the delta to GPT-5 will be small. He shares how Phind handles multi-step conversations via a pair programmer mode where users can pin messages, and reveals plans for reinforcement learning to reduce hallucination and improve correctness.

The End of Finetuning — with Jeremy Howard of Fast.ai

Oct 20, 2023 · 1:24:48

Jeremy Howard of Fast.ai argues that fine-tuning language models is essentially continued pre-training, not a separate process, and that the common practice of fine-tuning on a single task causes catastrophic forgetting. He recounts the discovery of single-shot memorization in LLMs, where models memorize entire datasets after one epoch, a phenomenon many practitioners ignore. Howard criticizes the current focus on zero-shot and few-shot learning, advocating for transfer learning and small models. He shares his journey from philosophy to founding Fast.ai, the creation of ULMFit (which inspired GPT), and his ongoing work on making AI accessible. He also discusses his involvement with Modular's Mojo language and the importance of democratizing AI technology.