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TinyLlama-1.1B

https://github.com/jzhang38/TinyLlama

The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs πŸš€πŸš€. The training has started on 2023-09-01.

We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.

This Collection

This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.

Eval

Model Pretrain Tokens HellaSwag Obqa WinoGrande ARC_c ARC_e boolq piqa avg
Pythia-1.0B 300B 47.16 31.40 53.43 27.05 48.99 60.83 69.21 48.30
TinyLlama-1.1B-intermediate-step-50K-104b 103B 43.50 29.80 53.28 24.32 44.91 59.66 67.30 46.11
TinyLlama-1.1B-intermediate-step-240k-503b 503B 49.56 31.40 55.80 26.54 48.32 56.91 69.42 48.28
TinyLlama-1.1B-intermediate-step-480k-1007B 1007B 52.54 33.40 55.96 27.82 52.36 59.54 69.91 50.22
TinyLlama-1.1B-intermediate-step-715k-1.5T 1.5T 53.68 35.20 58.33 29.18 51.89 59.08 71.65 51.29
TinyLlama-1.1B-intermediate-step-955k-2T 2T 54.63 33.40 56.83 28.07 54.67 63.21 70.67 51.64
TinyLlama-1.1B-intermediate-step-1195k-2.5T 2.5T 58.96 34.40 58.72 31.91 56.78 63.21 73.07 53.86
TinyLlama-1.1B-intermediate-step-1431k-3T 3T 59.20 36.00 59.12 30.12 55.25 57.83 73.29 52.99
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