📽 [Webinar] Beat GPT-4 with a Small Model and 10 Rows of Data*
Was this email forwarded to you? Sign up here Small language models (SLMs) are increasingly rivaling the performance of large foundation models like GPT-4. However, the need for high-quality datasets for fine-tuning these models presents a persistent challenge. On August 8th, Predibase is going to showcase an approach for fine-tuning an SLM that outperforms GPT-4 with synthetic data generated from only 10 real-world examples. The Data DilemmaWhile fine-tuning SLMs with high-quality datasets can consistently produce task-specific models that outperform large foundation models, many teams face a significant barrier: assembling sufficient training data. This challenge has often been a bottleneck in AI development, limiting the ability of teams to develop production-ready models quickly and cost-effectively. Synthetic Data Through DistillationOur upcoming webinar introduces an innovative solution to this persistent challenge. By leveraging the capabilities of large language models such as GPT-4 and Llama-3.1-405b, we've developed techniques to generate high-quality synthetic data for fine-tuning task-specific SLMs. This approach enables teams to achieve GPT-4 level results with as few as 10 real-world examples, dramatically reducing the data collection burden and accelerating the path to production. In this comprehensive session, we'll delve into the following key areas:
Join us on August 8thWhether you're an AI practitioner, startup founder, or enterprise decision-maker, this session will equip you with knowledge to effectively use synthetic data and SLMs. Join us to explore how synthetic data and fine-tuned SLMs can unblock your AI initiatives. Register today. *This post was written by Will Van Eaton from Predibase. We thank Predibase for their insights and ongoing support of TheSequence.You’re on the free list for TheSequence Scope and TheSequence Chat. For the full experience, become a paying subscriber to TheSequence Edge. Trusted by thousands of subscribers from the leading AI labs and universities. |
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Edge 418: Meet The New DSPy: The Hot Framework to Build LLM Apps You Should Know About
Thursday, August 1, 2024
Created by Stanford researchers, the framework provides an interesting alternative to LangChain or LlamaIndex. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 417: Building Multi Agent Systems
Tuesday, July 30, 2024
Can multiple agents collaborate in a single application? Some research and technologies that might help with that. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
3 vs. 3: The Open vs. Closed Battle for Big AI
Sunday, July 28, 2024
Big model announcements by Meta, Mistral and xAI. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 416: Inside Apple's 4M-21 Model that Could be the Foundation of its On-Device Multimodal Experience
Thursday, July 25, 2024
The model was trained simultaneously across 21 different modalities. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 415: Agents that Remember Actions with Procedural Memory
Tuesday, July 23, 2024
One of the most unique forms of memory in autonomous agents. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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