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Backtrack Sampler is a framework designed for experimenting with custom sampling strategies for language models (LLMs), enabling the ability to rewind and revise generated tokens. It allows developers to create and test their own token generation strategies by providing a base structure for manipulating logits and probabilities, making it a flexible tool for those interested in fine-tuning the behavior of LLMs.
Features
- Customizable token generation strategies
- Ability to backtrack and revise generated tokens
- Integration with models from Transformers and Llama.cpp
- Anti-slop strategy to prevent undesirable token generation
- Creative writing strategy to enhance model creativity by modifying token selection
- Easy-to-implement strategies using the base strategy class
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MIT LicenseFollow Backtrack Sampler
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