Research Note

Training Data Disclosure Evidence Framework

Classify each statement as exact item disclosure, named dataset, source category, proportion, collection method, filtering method, deduplication method, token count, time

Aug 4, 20261 min readBy Dalton Anderson

Source conversation

Beyond the Hype: Understanding Llama 3's True Potential

Summary In this episode, Dalton Anderson discusses the research paper 'Herd of LLMs' released by Meta. He provides an overview of the models and their capabilities, the pre-training and post-training processes, and the emphasis on safety. The paper covers topics such as model architecture, tokenization, and data filtering. Dalton highlights the importance of open sourcing research and models, and the potential for businesses to utilize and build upon these models. In this conversation, Dalton Anderson discusses the architecture and training process of the LAMA 3.1 language model. He explains the pre-training and fine-tuning stages, as well as the challenges faced in mathematical reasoning and long context handling. He also highlights the importance of safety measures in open-source models. Overall, the conversation provides insights into the inner workings of LAMA 3.1 and its applications.

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Training Data Disclosure Evidence Framework

Classify each statement as exact item disclosure, named dataset, source category, proportion, collection method, filtering method, deduplication method, token count, time cutoff, synthetic-data description, post-training description, or undisclosed.

Record the publisher, artifact, version, date, quotation or faithful paraphrase, scope, exclusions, uncertainty, and what cannot be inferred.

A phrase such as publicly available online data does not identify domains, documents, rights, geography, language distribution, persistence, or removals. A filtering description does not prove every prohibited or low-quality item was removed.

Use missing rather than estimated when the source does not disclose a fact.

Sources

Follow the evidence.

Introducing Llama 3.1ai.meta.com
ai.meta.com: the llama 3 herd of modelsai.meta.com
arxiv.org: 1810arxiv.org
crfm.stanford.edu: indexcrfm.stanford.edu
arxiv.org: 2203arxiv.org
open.spotify.com: 0iRBPcPw9iYjpUVAVWSkRCopen.spotify.com
NIST AI Risk Management Frameworknist.gov
github.com: MODEL CARDgithub.com
daltonanderson.ghost.io: metas llama 3 1 inside the ai research paperdaltonanderson.ghost.io
Meta Llama models repositorygithub.com
arxiv.org: 2001arxiv.org
youtu.be: UMhmWCor1kYyoutu.be
github.com: LICENSEgithub.com