Original model card
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Description
GGML Format model files for This project.
inference
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
Original model card
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
Training took approximately 9 hours (3 hours per epoch) on 40GB A100. ~30GB VRAM was used
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.
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 chunksAlso 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