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## Installation Instructions
As a pre-requisite, make sure you have [ducttape](https://github.com/CoderPat/ducttape) and [(mini)conda](https://docs.conda.io/en/latest/miniconda.html) installed.
First, clone this repository.
Then, to create a new conda environment with all the necessary dependencies, run the following command:
```bash
export CONDA_HOME="/path/to/(mini)conda3"
bash setup/conda.sh
```
# Training
## Data format
Before training, you must preprocess the training data. Before preprocessing, the data should be a `json` file, with the following format:
```json
{"text": "<instance_0_text>"}
{"text": "<instance_1_text>"}
```
Note that the preprocessing script will pack observations together in vectors of a specified length, and will separate each instance (json line) by the tokenizer's EOS token.
Then, run the bash scripts in this order:
```bash
./preprocess_data.sh [OPTIONS]
./convert2megatron.sh [OPTIONS]
./model_sharding.sh [OPTIONS]
./continue_pretraining.sh [OPTIONS]
```
>NOTE: each of these commands may be run with flag `--help`, which will inform the user on how to use each argument.
For example, for a continued pretraining run with Llama 2 7B on datasets `d1` and `d2` and 8 GPUs, run the following:
```bash
> ./preprocess_data.sh --dataset_json=<path_to_d1> --dataset_bin=<d1_output_path> --vocab_file=<path_to_hf_model>/tokenizer.model --repo=<path_to_repo>
> ./preprocess_data.sh --dataset_json=<path_to_d2> --dataset_bin=<d2_output_path> --vocab_file=<path_to_hf_model>/tokenizer.model --repo=<path_to_repo>
> ./convert2megatron.sh --megatron_model=<megatron_model_path> --model_path=<path_to_hf_model> --size=7 --repo=<path_to_repo>
> ./model_sharding.sh --megatron_model=<megatron_model_path> --sharded_model=<sharded_model_path> --tp=8 --pp=1 --vocab_size=32000 --repo=<path_to_repo>
> ./continue_pretraining.sh --data_path="1 d1 1 d2" --megatron_model=<sharded_model_path> --model_dir=<checkpoint_save_dir> --tokenizer_path=<path_to_hf_model>/tokenizer.model --tp=8 --pp=1 [TRAINING_ARGS]
``` |