github.com/ConiferLabsWA/flan-ul2-alpaca @main

Flan-UL2-Alpaca - Available On HuggingFace

This repository contains code for leveraging the Stanford Alpaca synthetic dataset to fine tune the Flan-UL2 model, leveraging recent advances in instruction tuning. The Flan UL2 model has been shown to outperform Flan-T5 XXL on a number of metrics and has a 4x improvement in receptive field (2048 vs 512 tokens).

Resource Considerations

A goal of this project was to produce this model with a limited budget demonstrating the ability train a robust, LLM using systems available to even small businesses and individuals. This had the added benefit of personally saving me money as well :). To achieve this a server was rented on vultr.com with the following pricing/specs: - Pricing: $2.604/hour - OS: Ubuntu 22.10 x64 - 12 vCPUs - 120 GB CPU RAM - 80 GB GPU RAM (1 x A100)

To dramatically reduce memory footprint and compute requirements Low Rank Adaption(LoRA) was used as opposed to finetuning the entire network. Additionally, the Flan-UL2 model was loaded and trained in 8 bit mode, also greatly reducing memory requirements. Finally, a batch size of 1 was used with 8 gradient accumulation steps. Here is a list of training parameters used: - Epochs: 2 - Learning Rate: 1e-5 - Batch Size: 1 - Gradient Accumulation Steps: 8 - 8 Bit Mode: Yes

Environment Setup

conda env create -f environment.yml
conda activate conifer

If you are running in a Unix environment and loading the model in 8 bit mode, you may encounter this error from bitsandbytes:

UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers and GPU quantization are unavailable.

If that happens, try this workaround:

cd ~/miniconda3/envs/conifer/lib/python3.10/site-packages/bitsandbytes/
cp libbitsandbytes_cuda120.so libbitsandbytes_cpu.so

Training

The following command will finetune the Flan-UL2 for 1 epoch. (1 epoch = ~13 hours on 1 x A100 [80GB VRAM])

python train_lora.py

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel, PeftConfig

prompt = "What color is the sky?"

peft_model_id = 'coniferlabs/flan-ul2-alpaca-lora'
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map="auto", load_in_8bit=True)
model = PeftModel.from_pretrained(model, peft_model_id, device_map={'': 0})
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model.eval()

tokenized_text = tokenizer.encode(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids=tokenized_text, min_length=10, max_length=500)
tokenizer.batch_decode(outputs, skip_special_tokens=True)

Results

Loss Trendline: y = -1.1302001815753724e-05x + 0.73000991550589