Mastering Hugging Face's Auto Train Advanced: Fine-Tune Models with Ease
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In this riveting tutorial by Abhishek Thakur, he unveils the secrets of mastering Hugging Face's cutting-edge Auto Train Advanced feature. With the power of this tool, you can fine-tune a plethora of models, from the mighty llm 7B Vikuna to the formidable 13B models, and even the revered Pythia and GPT Neo. The process kicks off with creating a private space in your Hugging Face account, selecting the appropriate docker template and Auto Train flavor, and securing your space with a token - ensuring your training endeavors remain exclusive.
Once your space is set up, it's time to dive into the world of training tasks, whether it's natural language processing or delving into specific training types like generic or chat. By feeding in datasets like the Alpaca dataset and tweaking parameters to your liking, such as selecting the base model like Pythia 70m for the llm 7B, you're on your way to creating a project with estimated costs. As the training commences and progress unfolds on the dashboard, you're one step closer to unleashing your finely-tuned models into the world.
As the training progresses, you can keep a close eye on the dashboard to track the development of your models. Once your model is trained and ready to go, deploying it is a breeze - whether through inference endpoints or by delving into the handler.py file for a peek at how the model operates. The beauty of Auto Train lies in the fact that all models are kept private, giving you the freedom to utilize them as you see fit. So buckle up, dive into the world of Auto Train Advanced, and watch as your models evolve into powerful tools at your disposal.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch Finetune LLMs (llama, vicuna, gptneo, pythia) without any code! on Youtube
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Creating a custom dataset for fine-tuning
Running fine-tuned model on own infrastructure and calling it using APIs from Java/JS App
Recommended resources for learning AI/ML as a beginner
Issues with Hugging Face autotrain and runtime errors
Differences between two models trained with the same data and settings
Cost comparison with Colab Pro and Paperspace
Training on personal hardware and obtaining code from the interface
Concerns about automation of basic tech jobs
Open-sourcing the code on the backend for implementing a similar interface/training process on local hardware
Troubleshooting login issues with HuggingFace Token placement
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