tokyotech-llm/Llama-3-Swallow-8B-v0.1
Llama-3-Swallow-8B-v0.1 is an 8 billion parameter language model developed by tokyotech-llm, continually pre-trained from the Meta Llama 3 family with a significant addition of Japanese language data. This model features an 8192-token context length and is optimized for strong performance in both Japanese and English language tasks. It excels particularly in Japanese benchmarks, demonstrating enhanced cross-lingual adaptation capabilities.
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.