This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture.
- 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
- 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
- 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
I had no troubles on the cloud machine with preninstalled libraries.
- Demo script is demo.py
- 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
- 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.
from transformers import AutoModelForCausalLM, AutoTokenizer
BAAI 
unsloth