Maykeye/TinyLLama-v0-GGUF

Quantized GGUF model files for TinyLLama-v0 from Maykeye

Name	Quant method	Size
tinyllama-v0.fp16.gguf	fp16	11.08 MB
tinyllama-v0.q2_k.gguf	q2_k	5.47 MB
tinyllama-v0.q3_k_m.gguf	q3_k_m	5.63 MB
tinyllama-v0.q4_k_m.gguf	q4_k_m	5.79 MB
tinyllama-v0.q5_k_m.gguf	q5_k_m	5.95 MB
tinyllama-v0.q6_k.gguf	q6_k	6.72 MB
tinyllama-v0.q8_0.gguf	q8_0	6.75 MB

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