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
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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.
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OLMo's Open Approach
- The OLMo project prioritizes open access to language models, data, and training details.
- This approach fosters scientific study and collaboration in LLM research.
OLMo's Ecosystem
- OLMo differentiates itself through its open ecosystem of tools and data, not just the model itself.
- This approach emphasizes collaboration and shared knowledge over leaderboard rankings.
Dolma Dataset
- Dolma, a 3 trillion token dataset, was released alongside OLMo to facilitate research on model behavior.
- This includes examining input-output relationships, capabilities, and biases like toxicity.