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## Installation Instructions |
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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. |
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First, clone this repository. |
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Then, to create a new conda environment with all the necessary dependencies, run the following command: |
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```bash |
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export CONDA_HOME="/path/to/(mini)conda3" |
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bash setup/conda.sh |
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``` |
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# Training |
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## Data format |
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Before training, you must preprocess the training data. Before preprocessing, the data should be a `json` file, with the following format: |
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```json |
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{"text": "<instance_0_text>"} |
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{"text": "<instance_1_text>"} |
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``` |
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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. |
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Then, run the bash scripts in this order: |
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```bash |
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./preprocess_data.sh [OPTIONS] |
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./convert2megatron.sh [OPTIONS] |
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./model_sharding.sh [OPTIONS] |
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./continue_pretraining.sh [OPTIONS] |
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``` |
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>NOTE: each of these commands may be run with flag `--help`, which will inform the user on how to use each argument. |
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For example, for a continued pretraining run with Llama 2 7B on datasets `d1` and `d2` and 8 GPUs, run the following: |
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```bash |
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> ./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> |
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> ./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> |
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> ./convert2megatron.sh --megatron_model=<megatron_model_path> --model_path=<path_to_hf_model> --size=7 --repo=<path_to_repo> |
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> ./model_sharding.sh --megatron_model=<megatron_model_path> --sharded_model=<sharded_model_path> --tp=8 --pp=1 --vocab_size=32000 --repo=<path_to_repo> |
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> ./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] |
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``` |