Baby Llama
Our submission to the strict-small track of the BabyLM challenge.
Baby Llama is a 58M-parameter model, distilled from an ensemble consisting of LLaMA-360M and GPT2-705M, both trained on the babylm_10M dataset.
See the associated paper for a detailed discussion of the training procedure and of the model performance. The training code is available at https://github.com/timinar/BabyLlama.
Hyperparameters for the tasks that require fine-tuning
When evaluating the model on the tasks that require fine-tuning, we noticed that the default hyperparameters suggested by the BabyLM organizers lead to severe overfitting in a number of tasks. To avoid this issue, we have re-tuned those hyperparameters. The sets of hyperparameters selected for each task are listed in the table below.
| Task | Maximum learning rate | Batch size | Maximum epochs | Patience | Evaluate every (steps) | Random seed |
|---|---|---|---|---|---|---|
| CoLA | 4e-5 | 64 | 3 | 10 | 20 | 12 |
| SST-2 | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| MRPC | 3e-5 | 64 | 3 | 10 | 20 | 12 |
| QQP | 4e-5 | 64 | 10 | 10 | 1000 | 12 |
| MNLI | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| MNLI-mm | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| QNLI | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| RTE | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| BoolQ | 3e-4 | 16 | 10 | 10 | 10 | 12 |
| MultiRC | 1e-4 | 64 | 7 | 10 | 1000 | 42 |
| WSC | 5e-7 | 1 | 10 | 1000 | 2000 | 12 |
| CR (Control) | 5e-5 | 64 | 10 | 10 | 100 | 12 |
| LC (Control) | 1e-3 | 64 | 1 | 2 | 10 | 12 |
| MV (Control) | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| RP (Control) | 1e-3 | 64 | 1 | 10 | 10 | 12 |
| SC (Control) | 1e-3 | 64 | 2 | 10 | 10 | 12 |
| CR_LC | 1e-3 | 64 | 2 | 10 | 10 | 12 |
| CR_RTP | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| MV_LC | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| MV_RTP | 5e-5 | 64 | 6 | 10 | 200 | 12 |
| SC_LC | 1e-3 | 64 | 2 | 10 | 10 | 12 |
| SC_RP | 1e-3 | 64 | 2 | 10 | 10 | 12 |