⚡ Technical Documentation

Fine Tuning Llms With Hugging Face Autotrain

Technical guide covering fine tuning llms with hugging face autotrain

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Cosmic Lounge AI Team
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6/1/2025
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#llm #ai #model #fine-tuning #training #gpu #cuda #pytorch #introduction #design

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🌌 Fine-Tuning LLMs with Hugging Face AutoTrain

Large language models (LLMs) have revolutionized the field of natural language processing (NLP), enabling various applications like text generation, translation, and question answering. However, fine-tuning is often crucial to optimize these models for specific tasks. Hugging Face AutoTrain is a powerful tool that simplifies this process, making it accessible to both beginners and experts.

🌟 Introduction to Hugging Face AutoTrain

🚀 Welcome to this comprehensive guide! This section will give you the foundational knowledge you need. Hugging Face AutoTrain is a no-code platform that empowers users to fine-tune LLMs without writing any code. It offers a user-friendly interface for selecting models, datasets, and hyperparameters, and automates the training process. AutoTrain supports various fine-tuning tasks, including supervised fine-tuning (SFT), reward modeling, and direct preference optimization (DPO) .

AutoTrain on Hugging Face Spaces provides a streamlined experience with pre-installed dependencies and managed hardware resources. This platform is optimized for ease of use, making it suitable for users with varying levels of expertise. Fine-tuning LLMs is essential for adapting them to specific use cases and improving their performance on tasks that require specialized knowledge or behavior. For instance, fine-tuning can be used to enhance an LLM’s ability to understand product metadata in its raw form or to generate more relevant responses in a conversational setting.

🌟 Configuring AutoTrain for Local Use on a Windows Machine

While AutoTrain is primarily designed for Hugging Face Spaces, it can also be configured for local use on a Windows machine. This involves installing the necessary software and setting up a virtual environment.

⚡ Prerequisites

Before installing AutoTrain, ensure your Windows machine meets the following prerequisites:

  • Python: Python 3.10 or higher is required.

  • Git LFS: Git LFS (Large File Storage) is used to manage large files, such as model weights.

  • PyTorch: PyTorch is a deep learning framework used by AutoTrain.

  • CUDA: CUDA is a parallel computing platform and programming model used to accelerate deep learning training. A dedicated GPU is highly recommended for optimal performance, especially for larger models .

⚡ Installation Steps

1. Install Python: Download and install the latest version of Python from the official website. 2. Install Git LFS: Download and install Git LFS from the official website. 3. Install PyTorch: Open a command prompt and run the following command:

pip install torch torchvision torchaudio

1. Install CUDA: Download and install the latest version of CUDA from the NVIDIA website. 2. Create a Conda Environment: Conda is an open-source package management system and environment management system. Create a new Conda environment with the following command:

conda create -n autotrain python=3.10

1. Activate the Conda Environment: Activate the newly created environment with the following command:

conda activate autotrain

1. Install AutoTrain Advanced: Install the AutoTrain Advanced package with the following command:

pip install autotrain-advanced

1. Install Additional Packages: Install the following additional packages:

conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia conda install -c “nvidia/label/cuda-12.1.0” cuda-nvcc

1. Set Hugging Face Token: Obtain a Hugging Face write token from the Hugging Face website and set it as an environment variable:

export HF_TOKEN=your_hugging_face_write_token

1. Set Hugging Face Username: Similarly, set your Hugging Face username as an environment variable:

export HF_USERNAME=your_hugging_face_username

⚡ Running AutoTrain Locally

Once the installation is complete, you can run AutoTrain locally using the following command:

autotrain app —host 127.0.0.1 —port 8000

This will start the AutoTrain user interface (UI) on your local machine. You can access the UI by opening a web browser and navigating to http://127.0.0.1:8000.

⚡ AutoTrain API

AutoTrain also provides an API that allows users to run their own instance of AutoTrain and train models on Hugging Face Spaces infrastructure. This API is designed to be used with AutoTrain compatible models and datasets and provides a simple interface to train models with minimal configuration. To get started with the AutoTrain API, install autotrain-advanced and run the following command:

autotrain app —port 8000 —host 127.0.0.1

You can then access the API reference at http://127.0.0.1:8000/docs.

⚡ Using Docker Images

Alternatively, you can use Docker images for AutoTrain, which can be an alternative for local installation. This approach can simplify the setup process and ensure consistency across different environments. To pull the latest AutoTrain Advanced Docker image, run the following command:

docker pull huggingface/autotrain-advanced:latest

You can then run the Docker image with the necessary configurations.

⚡ Trade-offs between Local and Hugging Face Spaces

Choosing between running AutoTrain locally and on Hugging Face Spaces involves considering various factors like cost, performance, and data privacy. Running AutoTrain locally provides more control over the environment and can be more cost-effective for smaller projects. However, Hugging Face Spaces offers a more streamlined experience with managed resources and potentially better performance for larger models and datasets.

🌟 Full Example with Code and Steps

This example demonstrates how to fine-tune a GPT-2 model for text generation using a custom dataset.

1. Prepare the Dataset: Create a CSV file named train.csv with the following format:

text
This is an example sentence.
Another example sentence.
Fine-tuning LLMs is exciting!

1. Configure AutoTrain: Open the AutoTrain UI and select the following settings:

  • Task: Text Generation

  • Model: gpt2

  • Dataset: Upload the train.csv file.

  • Hyperparameters: Use the default hyperparameters.

1. Start Training: Click the “Start Training” button to initiate the fine-tuning process.

🌟 Fine-Tuning LLMs with AutoTrain

AutoTrain provides a simple and intuitive interface for fine-tuning LLMs. It simplifies the process by automating many aspects of model training, allowing users to focus on selecting the appropriate model, dataset, and fine-tuning task .

⚡ Fine-Tuning Tasks

AutoTrain supports various fine-tuning tasks, including:

  • Supervised Fine-Tuning (SFT): Training an LLM on a labeled dataset of input-output pairs to improve its performance on a specific task.

  • Generic Fine-Tuning: Adapting an LLM to a new domain or task by training it on a large, unlabeled dataset.

  • ORPO (Optimal Reward Policy Optimization): A technique that combines supervised fine-tuning and preference alignment stages into a single process, reducing computational resources and training time .

  • DPO (Direct Preference Optimization): A method for training LLMs to generate outputs that are preferred by humans.

  • Reward Modeling: Training a reward model to predict the quality of LLM outputs, which can then be used to guide the fine-tuning process.

⚡ Selecting a Model and Dataset

AutoTrain supports a wide range of LLMs, including GPT-2, GPT-Neo, T5, and more. The choice of model depends on the specific task and the available computational resources. Similarly, AutoTrain supports datasets from the Hugging Face Hub and custom datasets. When using custom datasets, ensure they are in the correct format. AutoTrain supports CSV and JSONL formats, with JSONL being the preferred format for its readability and compatibility with the —chat-template parameter .

⚡ Configuring Hyperparameters

AutoTrain provides default values for most hyperparameters, but you can customize them as needed. Some important hyperparameters include:

  • Learning Rate: Controls how quickly the model learns from the training data.

  • Batch Size: The number of samples processed together as a single batch during training.

  • Epochs: The number of times the model sees the training data during fine-tuning.

⚡ Using Config Files

You can use config files to define the training settings and run AutoTrain from the command line. This approach can be useful for automating the training process and ensuring reproducibility. To use a config file for training, run the following command:

autotrain —config <path_to_config_file>

⚡ Creating a CSV File with Training Data

When creating a CSV file for training data, ensure it has a single column named “text” . For different models, specific formatting might be required. For example, Mistral models require a specific chat template format. Here’s an example of the CSV format for Mistral:

text
”human: What is the meaning of life? \n bot: 42."
"human: What is the capital of France? \n bot: Paris.”

⚡ Configuring the Hugging Face AutoTrain Advanced Notebook

The Hugging Face AutoTrain Advanced Notebook provides more advanced options for configuring and running AutoTrain. To use this notebook, select an A100 runtime for ample memory. Keep in mind that using A100 GPUs will incur costs, typically less than $1 USD per AutoTrain session. The notebook includes interactive sections for configuring the project name, model name, and hyperparameters. You can also upload your CSV file to the notebook and execute it to start the training process.

⚡ Downloading the LoRA and Updating the Adapter Config

After training, you can download the LoRA (Low-Rank Adaptation) files, which contain the fine-tuned model weights. You also need to update the adapter_config.json file with the correct model_type parameter. Valid values for model_type include mistral, gemma, and llama.

⚡ Dataset Formats and Fine-Tuning Results

Different dataset formats can significantly impact fine-tuning results. Projects like Dolly and Orca have demonstrated the importance of enriching data with context or system prompts. Other projects, like Vicuna, use chains of multi-step Q&A with solid results. Choosing the appropriate dataset format depends on the specific task and the desired outcome.

⚡ Hyperparameter Tuning and Model Validation

Finding the right hyperparameters for fine-tuning LLMs can be challenging and often requires experimentation. Improperly tuned hyperparameters can lead to overfitting or underfitting. Model validation is crucial to ensure that the fine-tuned model generalizes well to unseen data.

⚡ Memory Optimization

When fine-tuning LLMs, memory optimization is essential, especially when working with larger models. Techniques like using appropriate block size, enabling mixed precision training, and utilizing PEFT (Parameter-Efficient Fine-Tuning) can help reduce memory usage and improve training efficiency.

🌟 Troubleshooting

During installation and configuration, you might encounter some common errors. Here are a few troubleshooting tips:

  • Conda Environment Issues: If you encounter issues with the Conda environment, try creating a new environment or reinstalling the necessary packages.

  • CUDA Errors: Ensure that you have installed the correct version of CUDA that is compatible with your GPU.

  • Hugging Face Token Errors: Double-check that you have set the correct Hugging Face write token as an environment variable.

  • Dependency Conflicts: If you encounter dependency conflicts, try using a virtual environment to isolate the AutoTrain installation from other packages.

🌟 Limitations and Challenges

While AutoTrain simplifies LLM fine-tuning, it has some limitations and challenges:

  • Windows Support: AutoTrain does not officially support Windows. While it might work in some cases, you might encounter issues or limitations. Consider using WSL (Windows Subsystem for Linux) or Docker images for better compatibility.

  • Dataset Size: AutoTrain might have limitations on the size of datasets that can be used for fine-tuning, especially when running locally. For very large datasets, consider using Hugging Face Spaces or alternative solutions.

🌟 Alternative Solutions

If AutoTrain is not suitable for your needs, consider these alternative solutions for LLM fine-tuning on a Windows machine:

  • Google Colab: Google Colab provides free access to GPUs, making it a viable option for fine-tuning smaller LLMs.

  • WSL (Windows Subsystem for Linux): WSL allows you to run a Linux environment within Windows, providing better compatibility with AutoTrain and other LLM fine-tuning tools. Fine-tuning LLMs on resource-constrained machines can be challenging. Strategies for overcoming these challenges include using smaller models, reducing the dataset size, and employing memory optimization techniques .

🌟 Conclusion

Hugging Face AutoTrain is a valuable tool that simplifies the process of fine-tuning LLMs. By following the instructions and examples in this article, you can configure and use AutoTrain locally on a Windows machine to optimize LLMs for your specific needs. While AutoTrain offers a user-friendly approach, it’s essential to understand the different fine-tuning tasks, model selection, dataset preparation, and hyperparameter tuning to achieve optimal results.

🔧 Works cited

1. Fine-Tune LLMs Locally With No Code Using AutoTrain Configs - YouTube, https://www.youtube.com/watch?v=\_PA0bkNSeUI 2. Train (almost) Any LLM Model Using Autotrain - YouTube, https://www.youtube.com/watch?v=a8p7Yr82iq4 3. LLM Finetuning with AutoTrain Advanced - Hugging Face, https://huggingface.co/docs/autotrain/tasks/llm\_finetuning 4. Quickstart Guide to AutoTrain on Hugging Face Spaces, https://huggingface.co/docs/autotrain/quickstart\_spaces 5. LLM Fine-Tuning with AutoTrain in Snowflake | by Jason Summer - Medium, https://medium.com/snowflake/llm-fine-tuning-with-autotrain-in-snowflake-bc045efd37b1 6. Install AutoTrain Locally to Fine-Tune Any Model - YouTube, https://www.youtube.com/watch?v=vajTcKkN58c 7. AutoTrain: Train ANY Large Language Model with 1 Command - YouTube, https://www.youtube.com/watch?v=1C0F9mZCisU 8. AutoTrain API - Hugging Face, https://huggingface.co/docs/autotrain/autotrain\_api 9. huggingface/autotrain-advanced - GitHub, https://github.com/huggingface/autotrain-advanced 10. AutoTrain: No-code training for state-of-the-art models - ACL Anthology, https://aclanthology.org/2024.emnlp-demo.44.pdf 11. Frequently Asked Questions - Hugging Face, https://huggingface.co/docs/autotrain/faq 12. Fine Tune Models With AutoTrain from HuggingFace · Cloudflare Workers AI docs, https://developers.cloudflare.com/workers-ai/tutorials/fine-tune-models-with-autotrain/ 13. Hugging Face AutoTrain - Weights & Biases Documentation - Wandb, https://docs.wandb.ai/guides/integrations/autotrain/ 14. My experience on starting with fine tuning LLMs with custom data : r/LocalLLaMA - Reddit, https://www.reddit.com/r/LocalLLaMA/comments/14vnfh2/my\_experience\_on\_starting\_with\_fine\_tuning\_llms/ 15.

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