Chinchilla (language model)

Chinchilla
DeveloperDeepMind
ReleaseMarch 29, 2022 (2022-03-29)
Available inEnglish
TypeLarge language model

Chinchilla is a family of large language models (LLMs) developed by the research team at Google DeepMind, presented in March 2022.

Models

Chinchilla is a further development of an earlier DeepMind model family named Gopher. Both model families were trained to investigate the scaling laws of large language models

It claimed to outperform GPT-3. It considerably simplifies downstream utilization because it requires much less computer power for inference and fine-tuning. Based on the training of previously employed language models, it has been determined that if one doubles the model size, one must also have twice the number of training tokens. This hypothesis has been used to train Chinchilla by DeepMind. Similar to Gopher in terms of cost, Chinchilla has 70B parameters and four times as much data (1.4T tokens vs. 300B).

Chinchilla has an average accuracy of 67.5% on the Measuring Massive Multitask Language Understanding (MMLU) benchmark, which is 7% higher than Gopher's performance. Chinchilla was still in the testing phase as of January 12, 2023.

The authors concluded that more attention should go to scaling training datasets, and that larger datasets help only when the data is of high quality.

It has been used for the Flamingo vision-language model.

Architecture

Both the Gopher family and Chinchilla family are families of transformer models.

In particular, they are essentially the same as GPT-2, with different sizes and minor modifications. Gopher family uses RMSNorm instead of LayerNorm; relative positional encoding rather than absolute positional encoding. The Chinchilla family is the same as the Gopher family, but trained with AdamW instead of Adam optimizer.

The Gopher family contains six models of increasing size, from 44 million parameters to 280 billion parameters. They refer to the largest one as "Gopher" by default. Similar naming conventions apply for the Chinchilla family.

The Gopher family has the following specifications:

Model specifications for Gopher family
Parameter countLayersNumber of headsKey/value sizeInternal dimensionMax learning rateBatch size
44M816325126 × 10−40.25M
117M1212647686 × 10−40.25M
417M12121281,5362 × 10−40.25M
1.4B24161282,0482 × 10−40.25M
7.1B32321284,0961.2 × 10−42M
Gopher 280B8012812816,3844 × 10−53M → 6M

The 70-billion-parameter Chinchilla compares with Gopher 280B as follows:

Comparison between Chinchilla and Gopher
Parameter countLayersNumber of headsKey/value sizeInternal dimensionMax learning rateBatch size
Gopher 280B8012812816,3844 × 10−53M → 6M
Chinchilla 70B80641288,1921 × 10−41.5M → 3M

See also

References

  1. 1 2 Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; Buchatskaya, Elena; Cai, Trevor; Rutherford, Eliza; Casas, Diego de Las; Hendricks, Lisa Anne; Welbl, Johannes; Clark, Aidan; Hennigan, Tom; Noland, Eric; Millican, Katie; Driessche, George van den; Damoc, Bogdan (2022-03-29). "Training Compute-Optimal Large Language Models". arXiv:2203.15556 [cs.CL].
  2. 1 2 Rae, Jack W.; Borgeaud, Sebastian; Cai, Trevor; Millican, Katie; Hoffmann, Jordan; Song, Francis; Aslanides, John; Henderson, Sarah; Ring, Roman; Young, Susannah; Rutherford, Eliza; Hennigan, Tom; Menick, Jacob; Cassirer, Albin; Powell, Richard (2022-01-21). "Scaling Language Models: Methods, Analysis & Insights from Training Gopher". arXiv:2112.11446 [cs.CL].
  3. ↑ Eliaçık, Eray (January 12, 2023). "Chinchilla AI is coming for the GPT-3's throne". Dataconomy. Archived from the original on March 26, 2023.
  4. ↑ Hendrycks, Dan (2023-03-14), Measuring Massive Multitask Language Understanding, archived from the original on 2023-03-15, retrieved 2023-03-15
  5. ↑ Chaithali, G. (April 9, 2022). "Check Out This DeepMind's New Language Model, Chinchilla (70B Parameters), Which Significantly Outperforms Gopher (280B) and GPT-3 (175B) on a Large Range of Downstream Evaluation Tasks". Archived from the original on March 27, 2023. Retrieved January 15, 2023.
  6. ↑ Wali, Kartik (April 12, 2022). "DeepMind launches GPT-3 rival, Chinchilla". Analytics India Magazine. Archived from the original on March 26, 2023. Retrieved January 15, 2023.
  7. ↑ Alayrac, Jean-Baptiste; Donahue, Jeff; Luc, Pauline; Miech, Antoine; Barr, Iain; Hasson, Yana; Lenc, Karel; Mensch, Arthur; Millican, Katherine; Reynolds, Malcolm; Ring, Roman; Rutherford, Eliza; Cabi, Serkan; Han, Tengda; Gong, Zhitao (2022-12-06). "Flamingo: a Visual Language Model for Few-Shot Learning". Advances in Neural Information Processing Systems. 35: 23716–23736. arXiv:2204.14198.