| 1B-parameter models trained on Python-only datasets. In the different branches, models are trained on different versions of the Stack: | |
| - stack v1 | |
| - stack v2 - permissive | |
| - stack v2 - permissive and unlicensed | |
| 24 layers, a hidden-size of 2048 and 16 attention heads (multiquery). | |
| The learning-rate is set to $4\times10^{-4}$ after a warmup of $1000$ steps and follows a cosine decay to $4\times10^{-5}$ at the end of training. | |
| Trained with a batch size of 128 samples of 8192 tokens each, for $100$k iterations, such that the model sees $100$B tokens at the end of training. | |
| We use a FIM-rate of $0.5$, the same tokenizer as StarCoder (except for tokenizer ablations) and learned absolute positional embeddings. |