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license: apache-2.0 |
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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 |
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TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running |
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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. |
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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 |
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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). |
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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]`: |
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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 |
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so if random shuffle asks for a story, it may use cache or load chunk. |
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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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