Repetition penalty llama reddit. Adding a repetition_penalty of 1.
- Repetition penalty llama reddit 18 turned out to be the best across the board. Temperature: Think of it as a "chaos" dial. What's more important is that Repetition Penalty 1. ChatGPT: Sure, I'll try to explain these concepts in a simpler way, using non-technical language. 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. 2 across 15 different LLaMA (1) and Llama 2 models. 15, 1. 1, 1. 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. Adding a repetition_penalty of 1. Sure I could get a bit format studying the code, but I am yet not familiar even with the layout of that repo. The current implementation of rep pen in llama. 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. 18, Range 2048, and Slope 0 is actually what simple-proxy-for-tavern has been using as well from the beginning. For example, its value range, and which value causes no penalty. 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. KoboldAI instead uses a group of 3 values, what we call "Repetition Penalty", a "Repetition Penalty Slope" and a "Repetition Penalty Range". 1. 1 or greater has solved infinite newline generation, but does not get me full answers. . - Repetition Penalty. I've done a lot of testing with repetition penalty values 1. Or it just doesn’t generate any text and the entire response is newlines. I've done a lot of testing with repetition penalty values 1. 18, and 1. 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. 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. 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. This penalty is more of a bandaid fix than a good solution to preventing repetition; However, Mistral 7b models especially struggle without it. I would be willing to improve the docs with a PR once I get this. onetsif sgbj dxbjrk svcgr jmhkyqz evwv bugci vfxygm scdmt fpqvq