LLAMA_Fine-Tuning.py

# -*- coding: utf-8 -*-
"""LLAMA_Fine-Tuning.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1C-kNPOgPiCC9ybxVKhOkWB9ts53APbOb

# Fine-tune Llama 3 in Google Colab
"""

"""
!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7
!pip install datasets
!pip install --upgrade accelerate peft bitsandbytes transformers trl
"""


import os
import torch
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)
from peft import LoraConfig, PeftModel
from trl import SFTTrainer

# The model that you want to train from the Hugging Face hub
model_name = "unsloth/llama-3-8b-bnb-4bit"

# The Hugging Face token
token_name = "XXXX"


# Fine-tuned model name
new_model = "llama3_python_TFG"

################################################################################
# QLoRA parameters
################################################################################

# LoRA attention dimension
lora_r = 64

# Alpha parameter for LoRA scaling
lora_alpha = 16

# Dropout probability for LoRA layers
lora_dropout = 0.1

################################################################################
# bitsandbytes parameters
################################################################################

# Activate 4-bit precision base model loading
use_4bit = True

# Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"

# Quantization type (fp4 or nf4)
bnb_4bit_quant_type = "nf4"

# Activate nested quantization for 4-bit base models (double quantization)
use_nested_quant = False

################################################################################
# TrainingArguments parameters
################################################################################

# Output directory where the model predictions and checkpoints will be stored
output_dir = "./results"

# Number of training epochs
num_train_epochs = 1

# Enable fp16/bf16 training (set bf16 to True with an A100)
fp16 = False
bf16 = False

# Batch size per GPU for training
per_device_train_batch_size = 4

# Batch size per GPU for evaluation
per_device_eval_batch_size = 4

# Number of update steps to accumulate the gradients for
gradient_accumulation_steps = 2

# Enable gradient checkpointing
gradient_checkpointing = True

# Maximum gradient normal (gradient clipping)
max_grad_norm = 0.3

# Initial learning rate (AdamW optimizer)
learning_rate = 2e-4

# Weight decay to apply to all layers except bias/LayerNorm weights
weight_decay = 0.001

# Optimizer to use
optim = "paged_adamw_32bit"

# Learning rate schedule
lr_scheduler_type = "cosine"

# Number of training steps (overrides num_train_epochs)
max_steps = -1

# Ratio of steps for a linear warmup (from 0 to learning rate)
warmup_ratio = 0.03

# Group sequences into batches with same length
# Saves memory and speeds up training considerably
group_by_length = True

# Save checkpoint every X updates steps
save_steps = 0

# Log every X updates steps
logging_steps = 25

################################################################################
# SFT parameters
################################################################################

# Maximum sequence length to use
max_seq_length = None

# Pack multiple short examples in the same input sequence to increase efficiency
packing = False

# Load the entire model on the GPU 0
device_map = {"": 0}

from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorWithPadding
from datasets import Dataset

def load_text_file(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        return [line.strip() for line in f if line.strip()]

train_texts = load_text_file('LLAMA_DatosEntrenamiento.txt')
val_texts = load_text_file('LLAMA_DatosValidacion.txt')

tokenizer = AutoTokenizer.from_pretrained(model_name, token=token_name)

def tokenize_and_encode(texts):
    encodings = tokenizer(texts, truncation=True, padding="longest", max_length=512, return_tensors="pt")
    encodings['labels'] = encodings['input_ids'].clone()  # Duplicar input_ids para usar como labels
    return encodings

train_encodings = tokenize_and_encode(train_texts)
val_encodings = tokenize_and_encode(val_texts)

train_dataset = Dataset.from_dict({key: val.numpy() for key, val in train_encodings.items()})
val_dataset = Dataset.from_dict({key: val.numpy() for key, val in val_encodings.items()})

training_arguments = TrainingArguments(
    output_dir=output_dir,
    evaluation_strategy="steps",  # Evaluar basado en el número de pasos
    eval_steps=500,  # Evaluar cada 500 pasos
    num_train_epochs=1,
    per_device_train_batch_size=4,
    logging_steps=logging_steps,
    save_steps=1000,  # Guardar el modelo cada 1000 pasos para reducir la frecuencia de escritura en disco
    learning_rate=2e-4,
    weight_decay=0.001,
    lr_scheduler_type="cosine",
    warmup_ratio=0.03,
    report_to="tensorboard",
    fp16=False  # Desactivar la precisión mixta para simplificar el entrenamiento
)

model = AutoModelForCausalLM.from_pretrained(model_name, token=token_name)

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

trainer = Trainer(
    model=model,
    args=training_arguments,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    data_collator=data_collator
)

trainer.train()

model.save_pretrained(new_model)

model.push_to_hub("eibeel/llama3-python-TFG")


# Commented out IPython magic to ensure Python compatibility.
#  %load_ext tensorboard
#  %tensorboard --logdir results/runs