Repetition penalty llama reddit KoboldAI instead uses a group of 3 values, what we call "Repetition Penalty", a "Repetition Penalty Slope" and a "Repetition Penalty Range". I've done a lot of testing with repetition penalty values 1. I've done a lot of testing with repetition penalty values 1. **Part 0 - Why do we want repetition penalties?** For reasons of various hypotheses, **LLMs have a tendency to repeat themselves and get stuck in `repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1. 2 across 15 different LLaMA (1) and Llama 2 models. Adding a repetition_penalty of 1. 1). 1. For any good model, repetition penalty (and even more frequence penalty) should degrade performance That because (at least in my viewfeel free to correct me) the concept behind repetion/frequency/presence penalty is something that can be learned by the model during RL. ChatGPT: Sure, I'll try to explain these concepts in a simpler way, using non-technical language. This penalty is more of a bandaid fix than a good solution to preventing repetition; However, Mistral 7b models especially struggle without it. 18 turned out to be the best across the board. . The current implementation of rep pen in llama. I would be willing to improve the docs with a PR once I get this. 18, and 1. 1, 1. - Repetition Penalty. For example, its value range, and which value causes no penalty. What's more important is that Repetition Penalty 1. frequency_penalty: Higher values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. 18, Range 2048, and Slope 0 is actually what simple-proxy-for-tavern has been using as well from the beginning. This is Here are my two problems: The answer ends, and the rest of the tokens until it reaches max_new_tokens are all newlines. Or it just doesn’t generate any text and the entire response is newlines. cpp is equivalent to a presence penalty, adding an additional penalty based on frequency of tokens in the penalty window might be worth exploring too. Temperature: Think of it as a "chaos" dial. The typical solution to fix this is the Repetition Penalty, which adds a bias to the model to avoid repeating the same tokens, but this has issues with 'false positives'; imagine a language model that was tasked to do trivial math problems, and a user always involved the number 3 in his first 5 questions. Sure I could get a bit format studying the code, but I am yet not familiar even with the layout of that repo. For the hyperparameter repetition_penalty, while I comprehend that a higher repetition_penalty promotes the generation of more diverse tokens, I’m seeking a more quantitative explanation of its mechanism. 15, 1. 1 or greater has solved infinite newline generation, but does not get me full answers. gltucq mgwnqg chptn isigtqn ebjsc kmlng wwv wihgsb ybuhlp oynl