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home / papers / 2023 / LLaMA: Open and Efficient Foundation Language Models

LLaMA: Open and Efficient Foundation Language Models

Summary

The authors from Meta AI challenge the widespread assumption that more parameters automatically yield better quality. When a model is deployed at scale, the inference cost becomes the main concern, not just the training budget. They therefore trained the LLaMA series, ranging from 7B to 65B parameters, on far more tokens than the Chinchilla scaling laws recommend: 1T for the smaller models and 1.4T for the larger ones. The corpus was assembled exclusively from publicly available sources. The bulk of it is CommonCrawl, cleaned with CCNet, along with C4, GitHub, Wikipedia, books, arXiv and Stack Exchange. The architecture is a transformer with several well-known improvements: RMSNorm at the input of the sub-layers, the SwiGLU activation, and rotary positional embeddings (RoPE). The models were evaluated on 20 benchmarks in zero-shot and few-shot settings. LLaMA-13B outperforms GPT-3 (175B) on most tests despite being more than ten times smaller. LLaMA-65B is competitive with Chinchilla-70B and PaLM-540B. The work showed that state-of-the-art performance can be achieved without proprietary data. The model weights were released to researchers, and the smaller models can be run on a single GPU.

Key points

  • Training data composition: CommonCrawl 67%, C4 15%, GitHub, Wikipedia and books (Gutenberg and Books3) 4.5% each, arXiv 2.5%, Stack Exchange 2%. In total this amounts to roughly 1.4T tokens. The model sees Wikipedia and the books almost twice, and the rest of the data once.
  • Wikipedia was taken in 20 languages using the Latin and Cyrillic scripts, including Ukrainian (uk). The tokenizer is BPE as implemented in SentencePiece. It splits numbers into individual digits and decomposes unknown UTF-8 characters into bytes.
  • LLaMA-65B was trained on 2048 A100 GPUs (80GB) at about 380 tokens/s per GPU. Training took approximately 21 days. The optimizer is AdamW with a cosine learning rate schedule. Memory and time were saved through an efficient causal attention implementation from the xformers library.
  • The 7B model kept improving even after 1T tokens. For comparison, the Chinchilla recommendations suggest training a 10B model on only 200B tokens.
  • Carbon footprint: training LLaMA-65B took about 1.02 million GPU-hours, which corresponds to 173 tCO2eq. Developing the entire series produced approximately 1,015 tCO2eq. The code is published at github.com/facebookresearch/llama.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: AI & machine learning

Topics: Language models & pretraining

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2302.13971 · arXiv 2302.13971

@misc{touvron2023llama,
  title = {{LLaMA: Open and Efficient Foundation Language Models}},
  author = {Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timothée and Rozière, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
  year = {2023},
  doi = {10.48550/arxiv.2302.13971},
  eprint = {2302.13971},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2302.13971}
}

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