The tokyotech-llm/Gemma-2-Llama-Swallow-9b-pt-v0.1 is a 9 billion parameter pre-trained language model developed by tokyotech-llm, built upon the Gemma 2 architecture. This model enhances the Japanese language capabilities of the original Gemma 2 while retaining strong English performance, achieved through continual pre-training on approximately 200 billion tokens from Japanese web corpora, Wikipedia, and mathematical/coding content. It is designed for general language understanding and generation tasks, particularly excelling in Japanese contexts.
Loading preview...
Model tree for
tokyotech-llm/Gemma-2-Llama-Swallow-9b-pt-v0.1Most 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.