Understanding the Meta Llama 3 Tokenizer | Llama for Developers

Download Meta Llama 3 ➡️ go. kbpn54
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. ao405f
• Getting Started Guide: go. xucc2m
#llama3 #llm #opensource
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[@loabrasumente2283]

TLDR
- from llama 2 to llama3 they switched from sentencepiece to tiktoken
- vocab size 32k -> 128k
- ~15% fewer tokens for english, ~50% fewer for "some other languages"

[@anirbansen7132]

Informative

[@parvesh-rana]

Aston please explain the attention mechanism , Actually I am stuck in the chapter "Attention and transformer" of your book d2l

[@stephennfernandes]

could someone from the meta LLaMa 3 team please explain how to train my very own tiktoken tokenizer like you guys did for llama 3. there is no opensource steps to recreate this

[@prabhashxai]

Cool Future

[@maksymkyiv1111]

ok.

[@user-wr4yl7tx3w]

i don't think this format works unless the intent is to discuss at a high level.