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 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