Finetune LLMs (llama, vicuna, gptneo, pythia) without any code! | Summary and Q&A

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May 15, 2023
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Abhishek Thakur
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Finetune LLMs (llama, vicuna, gptneo, pythia) without any code!

TL;DR

Hugging Face's Auto Train Advanced allows users to fine-tune various models, such as LLM, Pythia, and GPT Neo, by creating an Auto Train space through their Hugging Face account.

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Questions & Answers

Q: How can Hugging Face's Auto Train Advanced be used for model fine-tuning?

To use Auto Train Advanced, users need to log into their Hugging Face account and create an Auto Train space. They can then select the desired model, such as LLM 7B or Pythia, and upload the dataset for training.

Q: What types of training are available in Auto Train Advanced?

Auto Train Advanced offers two training types: generic and chat. For generic training, only one input is required, while chat training expects three inputs. Users can select the appropriate training type based on their needs.

Q: Can users upload a validation set for training?

Yes, users have the option to upload a validation set for training, although it is not mandatory. If no validation set is provided, one will be created from the training set automatically.

Q: Are the models trained using Auto Train Advanced private?

Yes, all models trained using Auto Train Advanced are private. Users are free to deploy the trained models using the provided inference endpoints or access the model files for further use.

Summary & Key Takeaways

  • Hugging Face's Auto Train Advanced enables the fine-tuning of models like LLM 7B and Pythia, as well as GPT Neo.

  • Users can create an Auto Train space through their Hugging Face account, selecting the Docker template and Auto Train flavor.

  • It is important to mark the Auto Train space as private to protect the user's running face token.

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