def clean_extracted_text(text):
"""Clean and preprocess extracted text."""
# Remove chapter titles and sections
text = re.sub(r'^(Introduction|Chapter \d+:|What is|Examples:|Chapter \d+)', '', text, flags=re.MULTILINE)
text = re.sub(r'ctitious', 'fictitious', text)
text = re.sub(r'ISBN[- ]13: \d{13}', '', text)
text = re.sub(r'ISBN[- ]10: \d{10}', '', text)
text = re.sub(r'Library of Congress Control Number : \d+', '', text)
text = re.sub(r'(\.|\?|\!)(\S)', r'\1 \2', text) # Ensure space after punctuation
text = re.sub(r'All rights reserved|Copyright \d{4}', '', text)
text = re.sub(r'\n\s*\n', '\n', text)
text = re.sub(r'[^\x00-\x7F]+', ' ', text)
text = re.sub(r'\s{2,}', ' ', text)
# Remove all newlines and replace newlines only after periods
text = text.replace('\n', ' ')
text = re.sub(r'(\.)(\s)', r'\1\n', text)
def extract_text_from_pdf(pdf_path):
"""Extract text from a PDF file."""
with open(pdf_path, 'rb') as file:
reader = PyPDF2.PdfReader(file)
text = ''
for page in reader.pages:
if page.extract_text():
text += page.extract_text() + ' ' # Append text of each page
return text
def main():
pdf_path = '/Users/charlesqin/Documents/The Art of Asking ChatGPT.pdf' # Path to your PDF file
extracted_text = extract_text_from_pdf(pdf_path)
cleaned_text = clean_extracted_text(extracted_text)
# Output the cleaned text to a file
with open('cleaned_text_output.txt', 'w', encoding='utf-8') as file:
file.write(cleaned_text)
if __name__ == '__main__':
main()
from unsloth import FastLanguageModel
import torch
from trl import SFTTrainer
from transformers import TrainingArguments
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
"unsloth/mistral-7b-bnb-4bit",
"unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"unsloth/llama-2-7b-bnb-4bit",
"unsloth/gemma-7b-bnb-4bit",
"unsloth/gemma-7b-it-bnb-4bit", # Instruct version of Gemma 7b
"unsloth/gemma-2b-bnb-4bit",
"unsloth/gemma-2b-it-bnb-4bit", # Instruct version of Gemma 2b
"unsloth/llama-3-8b-bnb-4bit", # [NEW] 15 Trillion token Llama-3
] # More models at https://huggingface.co/unsloth
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/llama-3-8b-bnb-4bit",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
model = FastLanguageModel.get_peft_model(
model,
r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 16,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
def formatting_prompts_func(examples):
instructions = examples["instruction"]
inputs = examples["input"]
outputs = examples["output"]
texts = []
for instruction, input, output in zip(instructions, inputs, outputs):
# Must add EOS_TOKEN, otherwise your generation will go on forever!
text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
texts.append(text)
return { "text" : texts, }
pass
from datasets import load_dataset
file_path = "/home/Ubuntu/alpaca_gpt4_data_zh.json"
dataset = load_dataset("json", data_files={"train": file_path}, split="train")
dataset = dataset.map(formatting_prompts_func, batched = True,)
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = dataset,
dataset_text_field = "text",
max_seq_length = max_seq_length,
dataset_num_proc = 2,
packing = False, # Can make training 5x faster for short sequences.
args = TrainingArguments(
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
warmup_steps = 5,
max_steps = 60,
learning_rate = 2e-4,
fp16 = not torch.cuda.is_bf16_supported(),
bf16 = torch.cuda.is_bf16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "outputs",
),
)
trainer_stats = trainer.train()
model.save_pretrained_gguf("dir", tokenizer, quantization_method = "q4_k_m")
model.save_pretrained_gguf("dir", tokenizer, quantization_method = "q8_0")
model.save_pretrained_gguf("dir", tokenizer, quantization_method = "f16")
