The ryusangwon/ko_en_Llama-3.2-1B-Instruct model is a 1 billion parameter instruction-tuned causal language model, fine-tuned by ryusangwon from the meta-llama/Llama-3.2-1B-Instruct base model. It was trained using the TRL framework. This model is designed for general text generation tasks, leveraging its instruction-following capabilities.
Loading preview...
Model tree for
ryusangwon/ko_en_Llama-3.2-1B-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.