Chinchilla (language model)

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Chinchilla is a family of

DeepMind, presented in March 2022.[1] It is named "chinchilla" because it is a further development over a previous model family named Gopher. Both model families were trained in order to investigate the scaling laws of large language models.[2]

It claimed to outperform

DeepMind. Similar to Gopher in terms of cost, Chinchilla has 70B parameters and four times as much data.[3]

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.[4]

Chinchilla contributes to developing an effective training paradigm for large autoregressive language models with limited compute resources. The Chinchilla team recommends that the number of training tokens is twice for every model size doubling, meaning that using larger, higher-quality training datasets can lead to better results on downstream tasks.[5][6]

Architecture

Both the Gopher family and Chinchilla family are families of

transformer models
.

In particular, they are essentially the same as

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.

Table 1 of [2] shows the entire Gopher family:

Model Specifications for Gopher family
Parameter count Layers Number of heads Key/Value size Internal dimension Max learning rate Batch size
44M 8 16 32 512 6 × 10−4 0.25M
117M 12 12 64 768 6 × 10−4 0.25M
417M 12 12 128 1,536 2 × 10−4 0.25M
1.4B 24 16 128 2,048 2 × 10−4 0.25M
7.1B 32 32 128 4,096 1.2 × 10−4 2M
Gopher 280B 80 128 128 16,384 4 × 10−5 3M → 6M

Table 4 of [1] compares the 70-billion-parameter Chinchilla with Gopher 280B.

Comparison between Chinchilla and Gopher
Parameter count Layers Number of heads Key/Value size Internal dimension Max learning rate Batch size
Gopher 280B 80 128 128 16,384 4 × 10−5 3M → 6M
Chinchilla 70B 80 64 128 8,192 1 × 10−4 1.5M → 3M

See also

References

  1. ^
    arXiv:2203.15556 [cs.CL
    ].
  2. ^
    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.
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