Understanding the Llama 3 Tokenizer | Llama for Developers @AIatMeta
Understanding the Llama 3 Tokenizer | Llama for Developers  @AIatMeta
Uploaded June 2024 | Updated September 2026, 7 hours ago
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Aston Zhang, research scientist working on Llama at Meta discusses the new tokenizer in Meta Llama 3. He discusses the improvements made to the tokenizer in Meta's latest Llama 3 models. The new tokenizer uses Tiktoken instead of SentencePiece and has a larger vocabulary size of 128k, resulting in better performance on coding, reasoning, and more. The increased vocabulary size allows for more specific and nuanced encoding of inputs, while the higher compression ratio reduces the number of tokens required to represent an input. Additionally, the use of Group Query Attention helps balance out the increased memory and compute needs, resulting in a model that can process larger batches without increasing latency.

# Timestamps
00:00 Introduction
00:25 What's new in the Llama 3 tokenizer?
01:58 Vocabulary size and compression ratio
13:01 Performance, efficiency and improving costs
17:46 Recap and resources


# Additional Resources
• Dive into Deep Learning ebook: go.fb.me/ao405f
• Getting Started Guide: go.fb.me/xucc2m


#llama3 #llm #opensource
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Understanding the Llama 3 Tokenizer | Llama for Developers

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