| # Model arguments | |
| model_name_or_path: HuggingFaceTB/SmolLM2-360M # we use this script for the 135M model too | |
| model_revision: main | |
| tokenizer_name_or_path: HuggingFaceTB/SmolLM2-360M-Instruct # Custom tokenizer with <|im_start|> and <|im_end|> tokens | |
| torch_dtype: bfloat16 | |
| use_flash_attention_2: true | |
| # Data training arguments | |
| dataset_mixer: | |
| HuggingFaceTB/smol-smoltalk: 1.0 | |
| dataset_splits: | |
| - train | |
| - test | |
| preprocessing_num_workers: 36 | |
| # SFT trainer config | |
| bf16: true | |
| do_eval: true | |
| evaluation_strategy: epoch | |
| gradient_accumulation_steps: 4 | |
| gradient_checkpointing: true | |
| gradient_checkpointing_kwargs: | |
| use_reentrant: false | |
| hub_model_id: smollm2-360M-sft | |
| hub_strategy: every_save | |
| learning_rate: 1.0e-03 # 3e-4 | |
| log_level: info | |
| logging_steps: 5 | |
| logging_strategy: steps | |
| lr_scheduler_type: cosine | |
| max_seq_length: 8192 | |
| max_steps: -1 | |
| num_train_epochs: 2 | |
| output_dir: data/smollm2-360M-sft | |
| overwrite_output_dir: true | |
| per_device_eval_batch_size: 4 | |
| per_device_train_batch_size: 4 | |
| push_to_hub: true | |
| remove_unused_columns: true | |
| report_to: | |
| - tensorboard | |
| - wandb | |
| save_strategy: "no" | |
| seed: 42 | |
| warmup_ratio: 0.1 |