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Temperature, top_p and top_k for chatbot responses
Hello I’m using GPT as a chatbot. I have successfully fine-tuned the model on conversation data. For inference I’m now using temperature = 1, top_p = 0.6, and top_k = 35. In the following link it is written that for cha…
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Temperature, top_p and top_k for chatbot responses
https://community.openai.com/t/temperature-top-p-and-top-k-for-chatbot-responses/295542/2
Hello I’m using GPT as a chatbot. I have successfully fine-tuned the model on conversation data. For inference I’m now using temperature = 1, top_p = 0.6, and top_k = 35. In the following link it is written that for cha…
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Temperature, top_p and top_k for chatbot responses
Hello I’m using GPT as a chatbot. I have successfully fine-tuned the model on conversation data. For inference I’m now using temperature = 1, top_p = 0.6, and top_k = 35. In the following link it is written that for cha…
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- descriptionHello I’m using GPT as a chatbot. I have successfully fine-tuned the model on conversation data. For inference I’m now using temperature = 1, top_p = 0.6, and top_k = 35. In the following link it is written that for cha…
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- twitter:titleTemperature, top_p and top_k for chatbot responses
- twitter:descriptionToday we try out Claude v2: Nucleus sampling is a technique used in large language models to control the randomness and diversity of generated text. It works by sampling from only the most likely tokens in the model’s predicted distribution. The key parameters are: Temperature: Controls randomness, higher values increase diversity. Top-p (nucleus): The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus. Top-k: Sampl...
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