AXCXEPT/Llama-3-EZO-8b-Common-it
AXCXEPT/Llama-3-EZO-8b-Common-it is an 8 billion parameter instruction-tuned causal language model developed by AXCXEPT, based on Meta's Llama-3-8B-Instruct architecture. This model has been specifically enhanced for Japanese language tasks through additional pre-training and instruction tuning, while also improving general performance for diverse global needs. It leverages high-quality Japanese Wikipedia and FineWeb data for training, making it particularly effective for Japanese-centric applications.
Hugging Face Popular Sampler Settings
Most 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.