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Windows下中文微调Llama3,单卡8G显存只需5分钟,模型可接入GPT4All、Ollama,实现CPU推理聊天,附Colab一键训练脚本。

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💫 Short Summary

The video discusses using AI for exploration and discovery, focusing on utilizing Llama 3 for micro tuning with 8,000 data points, model quantization, and optimization for CPUs. It covers the process of setting up training libraries, model refinement, and data conversion. The segment also delves into installing Python 3.11.9, Visual Studio 2022, and other necessary software for AI development. Troubleshooting steps and successful installations are highlighted, along with the importance of managing storage space and CPU load. The final steps involve downloading model files, conducting fine-tuning, and executing test scripts for training, resulting in a GPT4All model available for processing using CPU.

✨ Highlights
📊 Transcript
Discussion on using AI for exploration and discovery, focusing on Llama 3 for micro tuning with 8,000 data points.
00:36
Llama 3 system runs on Windows 10 and efficiently processes large amounts of data.
Details on model quantization and optimization for CPUs are provided.
Importance of accurate conversation response rates is emphasized.
Setting up training libraries on the local Windows system is explained, highlighting the speed benefits of Llama 3 compared to previous versions.
Evolution of predictive modeling from Meta and importance of testing training data for model performance.
01:29
Original model must address the issue or response will return empty.
Process involves preparing data on Colab, adjusting the model, and uploading to Hugging Face for fine-tuning.
Steps for users include logging in, selecting New Dataset, filling in data repository names, choosing an open-source license, and creating files.
Format for Llama model datasets is adapted accordingly.
Summary of training process in a notebook.
03:26
Duplicating the data set and pasting it into the training notebook is the first step.
Setting training parameters and adjusting the LoRA method for sequence length are key aspects covered.
Learning rates, merging models for output, and using resources efficiently are discussed.
Progress includes completing multiple steps, adjusting learning rates and loss rates, and testing different model types.
Model refinement and data conversion process after training a LoRA model.
05:39
Emphasizes the need to combine the post-training LoRA model with the original model and save it as a 4-bit quantized version.
Transformation of HF format file with 16 bits to 4 GB file size and GGUF format file with 16 bits to a 4 GB file.
Combining and quantizing the model to a 40 GB usable space.
Discussion on downloading the final file locally and the efficiency of using Google Drive for downloads.
Overview of the chatbot program installation process.
06:39
Importance of proper installation to access necessary files.
Steps include downloading system packages, opening C drive, locating AppData folder, and finding specific directories.
Accessing nomic.ai folder and transitioning to GPT4All directory.
Instructions on selecting and running models for training and conversation sessions, installing and configuring software like Llama 3 and Triton on Windows.
Installation process of Python 3.11.9 and other necessary software.
08:10
Importance of using Python 3.11.9 and virtual environments for installation.
Installation of Visual Studio 2022, unsloth compression download, and editing Triton library files.
Need to download LLVM compression package and CUDA driver installation.
Importance of freeing up at least 20GB of disk space before installation and specific steps for CUDA installation.
Setting up unsloth directories, adding Python 3.11, installing Git, and customizing installation options.
10:05
Emphasizes the importance of step-by-step execution and the sequence for installing PyTorch and virtual environments.
Highlights the need for a specific order of operations for successful installation and testing of PyTorch and deepspeed.
Mentions troubleshooting steps and the significance of executing commands correctly to avoid errors.
Ensuring successful installation of required packages.
Troubleshooting installation error with PyTorch version compatibility.
12:04
Reinstallation using 'pip list' to verify and ensure correct PyTorch version 2.2.2.
Successful uninstallation and reinstallation with no reported errors.
Testing confirmed successful installations of CUDA and xformers components.
Triton dependency package 'unsloth' also successfully installed, marking a milestone.
The process of downloading model files, conducting fine-tuning, and executing test scripts for training is discussed in the video segment.
13:30
Managing storage space, CPU load, and model versions is highlighted as crucial during the process.
Setting up training data paths, using Hugging Face for downloads, and modifying local code as needed are covered in the segment.
Saving and adjusting model codes, focusing on LoRA models and GGUF formats, is explained.
The steps for operating and modifying specific code files like llama.cpp, and the importance of model versioning and optimization, are outlined.
Steps for converting a model to run on GPU with Visual Studio 2022.
15:05
Copy the project address and open CMD under unsloth directory.
Use 'cl' command to edit llama.cpp file and create a translation folder.
Set translation parameters and modify system environment variables.
Add cmake path in 'path', copy CUDA directory under the project directory, and translation process takes about 16 minutes.
Instructions for processing the over 30GB GPT4All model file.
16:51
The file can be duplicated to the GPT4All directory for processing using CPU.
Download links and instructions are available in the video description.
Viewers are encouraged to share their feedback.
Stay tuned for future updates on the topic.