LLAMA-2: Fine-tune Models Easily with Auto Train
Learn the easiest way to fine-tune models using the Auto Train package from Hugging Face. Adjust parameters for optimal results.
00:00:00 Learn how to fine-tune any model using the Auto Train library from Hugging Face with just one line of code. Works on Google Colab and requires Python 3.8+ and an Nvidia GPU.
:computer: You can fine-tune a Llama-2 model on your own dataset using a single line of code.
:package: To run this locally, you need to download the Auto Train Advance package from Hugging Face's GitHub repo.
:rocket: An Nvidia GPU is required for model fine-tuning, but you can use Google Colab if you don't have one.
00:02:06 Learn the easiest way to fine-tune models, including language and computer vision models, using the Auto Train package from Hugging Face.
🦙 The video explains how to fine-tune models using the Auto Train package from Hugging Face.
🙌 The code provided demonstrates how to specify the project name and the model to be fine-tuned.
📝 It is important to note that this method can be used with any model from Hugging Face, not just the llama models.
00:04:11 Learn how to fine-tune a model using different datasets, either by uploading data to Hugging Face or using local files. Two example datasets are discussed.
🦙 You can create a sharded version of the original gamma model site using any available version.
🙌 To fine-tune the model, you need to provide the name or path to the dataset.
📁 You can upload your data to hugging face or provide the local path to the dataset.
00:06:17 Learn how to format your data set in the required format and define the text column for fine-tuning on your data using LLAMA-2.
🦙 The video discusses the formatting of the data set for the LLAMA-2 model.
🙌 It emphasizes the importance of using special tokens in the data set for the model to understand the input and output.
👥 The format of the data set for fine-tuning the base model is different from the prompt template used in Lemma 2 chat models.
00:08:24 Learn how to fine-tune models using the efficient fine-tuning method proposed by Hugging Face. Adjust parameters based on hardware and dataset for optimal results.
🦙 Using the 'use' command, the model can be fine-tuned on custom data.
🙌 The learning rate controls the speed of conversion during the training process.
🔧 The trainer used is 'sft', which stands for supervised fine-tuning.
00:10:31 Learn how to fine-tune a large language model using your own training data set. Expect at least an hour for the model to appear in your own hugging face account.
🦙 Fine-tuning a large language model using your own training data set takes time, but it can be done using a single NF code.
🙌 During the training process, a project folder is created to track the progress, and once complete, you can find the config.json file, tokenizer, and model file in the folder.
📊 To push the fine-tuned model to your own account, you need to provide the repo ID and be patient as it may take at least an hour to appear.
00:12:37 Learn how to fine-tune a large language model on your own data set using the powerful Auto Train package. Requires a powerful GPU.
🦙 Learn how to fine-tune a large language model on your own data set using the Auto Train package.
🔍 Discover the process of creating your own data sets instead of using pre-existing ones on Hugging Face.
💻 Ensure you have a powerful GPU to effectively run the fine-tuning process.
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