VityaVitalich/Llama3.1-8b-instruct is an 8 billion parameter instruction-tuned causal language model based on the Llama 3.1 architecture, developed by VityaVitalich. This model is designed for general-purpose conversational AI and instruction following, leveraging its substantial parameter count and 32768 token context length for robust performance across various NLP tasks. Its primary strength lies in its ability to process and generate human-like text based on given instructions, making it suitable for a wide range of interactive applications.
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VityaVitalich/Llama3.1-8b-instructMost commonly used values from Featherless users
temperature
This setting influences the sampling randomness. Lower values make the model more deterministic; higher values introduce randomness. Zero is greedy sampling.
top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
presence_penalty
This setting penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens; < 0 encourages repetition.
repetition_penalty
This setting penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens; < 1 encourages repetition.
min_p
This setting representing the minimum probability for a token to be considered relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.