The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674
Mar 4, 2024
Akshita Bhagia, a senior research engineer at the Allen Institute for AI, shares her insights on OLMo, an open-source language model that includes a unique dataset and tools for training. She discusses the innovative Dolma dataset, which boasts a three-trillion-token corpus, and Paloma, a benchmarking tool for evaluating model performance. Throughout the conversation, Akshita emphasizes the importance of data transparency, collaborative research, and the challenges faced in training large-scale models, advocating for a shared knowledge approach in AI development.
32:12
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Quick takeaways
- OLMo provides transparent pre-training data and tools to foster collaborative research.
- Dolma dataset offers three trillion tokens from public data for analyzing model capabilities across domains.
Deep dives
The Motivation Behind Almost Project
The Almost project aims to address the lack of transparency in language model development by providing truly open language models. This initiative was driven by the necessity for researchers to access complete details of model training data and pre-training specifics. By releasing the 1B and 7B versions of the models alongside comprehensive pre-training data, training code, logs, and evaluation tools, Almost seeks to foster collaborative research and avoid redundant, costly experiments.
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