This tutorial details how to efficiently merge the PEFT LoRA adapter with the base model to generate a completely independent model. The article points out that it is wrong to directly use transformers.AutoModel to load the adapter and manually merge the weights, and provides the correct process to use the merge_and_unload method in the peft library. In addition, the tutorial also emphasizes the importance of dealing with word segmenters and discusses PEFT version compatibility issues and solutions.

Run pipinstall-rrequirements.txt to install the dependency package. It is recommended to create and activate the virtual environment first to avoid conflicts, ensure that the file path is correct and that the pip has been updated, and use options such as --no-deps or --user to adjust the installation behavior if necessary.

Python is a simple and powerful testing tool in Python. After installation, test files are automatically discovered according to naming rules. Write a function starting with test_ for assertion testing, use @pytest.fixture to create reusable test data, verify exceptions through pytest.raises, supports running specified tests and multiple command line options, and improves testing efficiency.

Theargparsemoduleistherecommendedwaytohandlecommand-lineargumentsinPython,providingrobustparsing,typevalidation,helpmessages,anderrorhandling;usesys.argvforsimplecasesrequiringminimalsetup.

This article aims to explore the common problem of insufficient calculation accuracy of floating point numbers in Python and NumPy, and explains that its root cause lies in the representation limitation of standard 64-bit floating point numbers. For computing scenarios that require higher accuracy, the article will introduce and compare the usage methods, features and applicable scenarios of high-precision mathematical libraries such as mpmath, SymPy and gmpy to help readers choose the right tools to solve complex accuracy needs.

This article details how to use the merge_and_unload function of the PEFT library to efficiently and accurately merge the LoRA adapter into the basic large language model, thereby creating a brand new model with integrated fine-tuning knowledge. The article corrects common misunderstandings about loading adapters and manually merging model weights through transformers.AutoModel, and provides complete code examples including model merging, word segmenter processing, and professional guidance on solving potential version compatibility issues to ensure smooth merge processes.

PyPDF2, pdfplumber and FPDF are the core libraries for Python to process PDF. Use PyPDF2 to perform text extraction, merging, splitting and encryption, such as reading the page through PdfReader and calling extract_text() to get content; pdfplumber is more suitable for retaining layout text extraction and table recognition, and supports extract_tables() to accurately capture table data; FPDF (recommended fpdf2) is used to generate PDF, and documents are built and output through add_page(), set_font() and cell(). When merging PDFs, PdfWriter's append() method can integrate multiple files

Import@contextmanagerfromcontextlibanddefineageneratorfunctionthatyieldsexactlyonce,wherecodebeforeyieldactsasenterandcodeafteryield(preferablyinfinally)actsas__exit__.2.Usethefunctioninawithstatement,wheretheyieldedvalueisaccessibleviaas,andthesetup