smolhub 0.5.9

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SmolHub

A lightweight package for fine-tuning language models using LoRA (Low-Rank Adaptation).

Installation

pip install smolhub

Usage

A default config file is created in the user project directory if not already there

Example Config

project:
  name: SFTrainer
  author: Yuvraj Singh
  version: 1.0

LoRA:
  rank: 4
  alpha: 8
Dataset:
    use_hf_dataset: True
    dataset_path: stanfordnlp/imdb
    max_length: 512
    batch_size:  16
    num_workers:  4
    shuffle:  True
    drop_last: True
    pin_memory:  True
    persistent_workers:  True
    type: "classification" #TODO Add Chat style and Instruction 
   
huggingface:
  hf_token: "..."

Model:
  epochs: 1
  eval_iters: 10

MAP:
  use_bfloat16:  False
  use_float16: False

Optimizations:
  use_compile: False


wandb:
  project_name: "SFTrainer"
  

import torch
import smolhub
# from smolhub.helper.dataset.load_config import Config
from smolhub.scripts.finetune import SFTTrainer
from transformers import AutoTokenizer, AutoModelForCausalLM
from smolhub.helper.scheduler import CustomLRScheduler
from smolhub.scripts.lora import LoRAModel
from smolhub.helper.dataset.dataset_main import PreprocessDataset
from load_config import Config #Needs to be created

model_id = "openai-community/gpt2"

config = Config().get_config()
dataset_path = config["Dataset"]["dataset_path"]

tokenizer = AutoTokenizer.from_pretrained(model_id, token=config['huggingface']['hf_token'])
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", token=config['huggingface']['hf_token'])

if tokenizer.pad_token is None:
    # Set the pad token to the eos token if it doesn't exist
    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
    # tokenizer.pad_token = tokenizer.eos_token
    

    print("Setting pad token as PAD token ")

model.resize_token_embeddings(len(tokenizer))

lora_model = LoRAModel(model)
optimizer = torch.optim.Adam(lora_model.parameters(), lr=2e-3)
scheduler = CustomLRScheduler(optimizer, warmup_iters=100, lr_decay_iters=2000, min_lr=2e-5, max_lr=2e-3, _type="cosine")

#Loading the dataset
preprocess_dataset = PreprocessDataset(dataset_path=dataset_path, tokenizer=tokenizer)
train_dataloader, val_dataloader, test_dataloader = preprocess_dataset.prepare_dataset()

#Initialize the Trainer
sft_trainer = SFTTrainer(lora_model, train_dataloader, val_dataloader, test_dataloader, optimizer, None, scheduler)

#Train
sft_trainer.train()