Llama 3.1 Swallow 8B v0.5 is an 8 billion parameter large language model developed by tokyotech-llm, built by continually pre-training Meta Llama 3.1. This model significantly enhances Japanese language capabilities, reasoning in code and math, while maintaining strong English performance. It was trained on approximately 210 billion tokens from a diverse corpus including Japanese web data, Wikipedia, and specialized mathematical and coding content, making it particularly strong for bilingual applications requiring robust reasoning.
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tokyotech-llm/Llama-3.1-Swallow-8B-v0.5Most 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.