视频中所用到的代码

视频中所用到的代码

清洗PDF

import PyPDF2
import re

    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)

    return 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()

微调代码

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")

微调好的中文llama3下载链接: https://huggingface.co/leo009/dir/tree/main

如有问题请联系up的徽信 : stoeng