Hello folks, here’s what we have today;
1/ Meta has released Code Llama, a large language model optimized for generating code. It is available in three sizes - 7B, 13B, and 34B parameters - each trained on 500B tokens of code data. Benchmark testing showed Code Llama outperforms other publicly available code-specific LLMs like Codex and AlphaCode, and is on par with ChatGPT.
Mysteriously, there’s a secret, unreleased model, “Unnatural Code Llama” mentioned that crushed every other model…
p.s. Code LLaMA is now on Perplexity’s LLaMa Chat
2/ OpenAI partners with Scale to provide support for enterprises fine-tuning models.
3/ An Interview with Daniel Gross and Nat Friedman about the AI Hype Cycle (paywalled). Ben Thompson has released his third interview with the duo, I’ve enjoyed each interview enormously.
Nat and Daniel discuss the current AI hype cycle: There is more noise but also real traction in enterprise use cases automating human labour.
They see the model ecosystem diversifying into generalist, high capability, and specialized expert models suited for different jobs. Enterprises will likely evolve from exploring general models to deploying tailored solutions based on factors like privacy, tuning needs, and performance.
This landscape creates infrastructure opportunities in model evaluation, training/deployment, augmentation, and operational tooling.
They see local models complementing cloud models, and opportunities in synthetic data and curriculum learning to address data constraints.
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From the community
Will enterprise AI models be “winner take all?” by Lightspeed.
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