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| | license: apache-2.0 |
| | --- |
| | This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture. |
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| | * Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading |
| | TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running |
| | the cells. Validation content is not used by the script so you put anythin in |
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| | * Backup directory has a script do_backup that I used to copy weights from remote machine to local. |
| | Weight are generated too quickly, so by the time script copied weihgt N+1 |
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| | * This is extremely PoC version. Training truncates stories that are longer than context size and doesn't use |
| | any sliding window to train story not from the start |
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| | * Training took approximately 9 hours (3 hours per epoch) on 40GB A100. ~30GB VRAM was used |
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| | * I use tokenizer from open_llama_3b. However I had troubles with it locally(https://github.com/openlm-research/open_llama/issues/69). |
| | I had no troubles on the cloud machine with preninstalled libraries. |
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| | * Demo script is demo.py |
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| | * Validation script is provided: valid.py. use it like `python valid.py path/to/TinyStoriesV2-GPT4-valid.txt [optional-model-id-or-path]`: |
| | After training I decided that it's not necessary to beat validation into chunks |
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| | * Also this version uses very stupid caching mechinsm to shuffle stories for training: it keeps cache of N recently loaded chunks |
| | so if random shuffle asks for a story, it may use cache or load chunk. |
| | Training dataset is too small, so in next versions I will get rid of it. |
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| | from transformers import AutoModelForCausalLM, AutoTokenizer |
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