🍮🦙🤏Flan-Alpaca-LoRA: Instruction Tuning from Humans and Machines with Low-Rank Adaptation
This repo trains google/flan-t5 on alpaca dataset with low-rank adaptation training method. It reduces the GPU memory needed and speeds the training.
Jun 17, 2023: add a notebook. You can try flan-alpaca-lora with
now.
May 3, 2023: train flan-t5-xl using alpaca-gpt4 dataset.
Apr 13, 2023: train flan-t5-xl using GPTeacher dataset (Instruct and Roleplay), which seems to perform well.
Apr 5, 2023: train flan-t5-xxl using 8bit quantization. The model can be fitted into a single 3090 GPU. All of the models can be found in huggingface.
Dependencies
torch == 1.13.1
transformers == 4.29.1
peft == 0.3.0
bitsandbytes==0.38.1
accelerate==0.19.0
Newest version of these packages should work fine.
Training
The following command finetune Flan-T5-base with only 20 mins on a single 3090 GPU
python train.py \
--model_name_or_path google/flan-t5-base \
--data_path ./alpaca_data_cleaned.json \
--bf16 True \
--output_dir ./ckpts/ \
--num_train_epochs 3 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--save_strategy "no" \
--learning_rate 5e-4 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 50 \
--tf32 True
Example usage:
import transformers
from peft import PeftModel
# Where peft_model_id should be the saving directory or huggingface model id
model_name = "google/flan-t5-large"; peft_model_id = "reasonwang/flan-alpaca-lora-large"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
base_model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_name)
peft_model = PeftModel.from_pretrained(base_model, peft_model_id)
# Input an instruction or any other questions.
inputs = tokenizer("List a few tips to get good scores in math.", return_tensors="pt")
outputs = peft_model.generate(**inputs, max_length=128, do_sample=True)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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