Summary: Learn how to customize the Huggingface Data Collator to add an EOS token without padding it to -100, catering to large language models like Mistral 7B.
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How to Modify Huggingface Data Collator to Add an EOS Token Without Padding It to -100
Large language models (LLMs) like Mistral 7B often require precise handling of tokens to ensure optimal performance. One such requirement arises with the need to add an EOS (End Of Sequence) token to sequences without applying padding that defaults to -100. This guide will guide you through modifying the Huggingface Data Collator to achieve this.
Understanding the Default Data Collator
Huggingface Data Collators are essential for handling tasks like padding, truncation, and token addition for models like GPT, BERT, and others. The default behavior for many Data Collators is to pad tokens to a default value, often -100, which can interfere with certain model training behaviors, especially with the introduction or handling of special tokens like EOS.
Customizing the Data Collator
Step 1: Define a Custom Data Collator Class
We start by defining a custom class that inherits from the default DataCollator:
[[See Video to Reveal this Text or Code Snippet]]
Step 2: Initialize the Custom Data Collator
Ensure you initialize the CustomDataCollator with the appropriate tokenizer for your model, such as the Mistral 7B tokenization.
[[See Video to Reveal this Text or Code Snippet]]
Step 3: Use the Custom Data Collator in Training
Integrate the data collator into your training loop or data loader as needed:
[[See Video to Reveal this Text or Code Snippet]]
Conclusion
Modifying the Huggingface Data Collator to add an EOS token without padding it to -100 can significantly improve the handling of sequences in large language models like Mistral 7B. By customizing the collator as demonstrated, you ensure that your sequences are managed precisely, avoiding unintended padding values. This small yet critical adjustment can lead to more effective and efficient training outcomes for your models.
Explore these steps and integrate them as per your project requirements to ensure your LLMs perform at their best.
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