- [2. Init Model Preparation](#2-init-model-preparation)
- [3. Data Preparation](#3-data-preparation)
- [4. Command Line Arguments for Training](#4-command-line-arguments-for-training)
- [5. Running Command](#5-running-command)
- [Technical Insights](#technical-insights)
- [Data](#data)
- [Tokenizer](#tokenizer)
- [Training Strategy](#training-strategy)
- [Multi-stage Training](#multi-stage-training)
- [Bucket-based Training](#bucket-based-training)
- [Bridging Any Domain-specific Large Models](#bridging-any-domain-specific-large-models)
- [Citations](#citations)
@ -260,7 +271,7 @@ Here is details about CLI arguments:
* Booster plugin: `--plugin`. `gemini`, `gemini_auto`, `zero2`,`zero2_cpu` and `3d` are supported.For more details, please refer to [Booster plugins](https://colossalai.org/docs/basics/booster_plugins/).
* Intermediate checkpoint to load: `--load_checkpoint`. Path to the intermediate checkpoint. Saved checkpoint contains the states for `lr_scheduler`, `optimizer`,`running_states.json` and `modelling`. If `load_checkpoint` points to the `modelling` folder, only the model weights will be loaded without any other states to support multi-stage training.
* Save interval: `--save_interval`. The interval (steps) of saving checkpoints. The default value is 1000.
* Checkpoint directory: `--save_dir`. The directoty path to save checkpoint and intermediate states. Intermediate states include `lr_scheduler`, `optimizer`,`running_states.json` and `modelling`.
* Checkpoint directory: `--save_dir`. The directory path to save checkpoint and intermediate states. Intermediate states include `lr_scheduler`, `optimizer`,`running_states.json` and `modelling`.
* Tensorboard directory: `--tensorboard_dir`. The path to save tensorboard logs.
* Configuration file: `--config_file`. The path to save the configuration file.
* Number of epochs: `--num_epochs`. Number of training epochs. The default value is 1.
@ -404,5 +415,4 @@ Applying the above process to perform knowledge transfer in any field allows for