phi2-fine-tuning.py

# pip install torch peft==0.4.0 bitsandbytes transformers==4.31.0 trl==0.4.7 accelerate einops
# pip install tqdm scipy

import os
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from datasets import load_from_disk
from peft import LoraConfig, prepare_model_for_kbit_training
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    AutoTokenizer,
    TrainingArguments,
)
from tqdm.notebook import tqdm
from transformers import pipeline
from trl import SFTTrainer
import pandas as pd

print("Is CUDA enabled?",torch.cuda.is_available())

# dataset = load_from_disk("C:\\Users\\PROMETHEUS\\Desktop\\dataset\\CommonFAQ.csv")
training_dataset = load_dataset("csv", data_files="formatted_qna_lite.csv", split="train")
print(training_dataset)

base_model = "microsoft/phi-2"
new_model = "phi-2-ft-medq"

tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=False,
)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=bnb_config,
    # use_flash_attention_2=True, # Phi does not support yet.
    trust_remote_code=True,
    flash_attn=True,
    flash_rotary=True,
    fused_dense=True,
    low_cpu_mem_usage=True,
    device_map={"": 0},
    revision="refs/pr/23",
)

model.config.use_cache = False
model.config.pretraining_tp = 1

model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)

training_arguments = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=2,
    gradient_accumulation_steps=32,
    evaluation_strategy="steps",
    eval_steps=2000,
    logging_steps=15,
    optim="paged_adamw_8bit",
    learning_rate=2e-4,
    lr_scheduler_type="cosine",
    save_steps=2000,
    warmup_ratio=0.05,
    weight_decay=0.01,
    max_steps=-1
)

peft_config = LoraConfig(
    r=32,
    lora_alpha=64,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=["Wqkv", "fc1", "fc2"]  # ["Wqkv", "out_proj", "fc1", "fc2" ], - 41M params
    # modules_to_save=["embed_tokens","lm_head"]
)

trainer = SFTTrainer(
    model=model,
    train_dataset=training_dataset,
    peft_config=peft_config,
    dataset_text_field="Text",
    max_seq_length=900,
    tokenizer=tokenizer,
    args=training_arguments,
)

trainer.train()

prompt = "How old was Pascal when he lost his mother?"

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=250)

while prompt != 'EXIT':
    result = pipe(f"[INST] {prompt} [/INST]")
    print(result[0]['generated_text'])
    prompt = input("Ask the next question .....\n")