Hugging Face The rinna/gemma-2-baku-2b is a 2.6 billion parameter transformer-based language model, continually pre-trained by rinna on 80 billion tokens of mixed Japanese and English datasets. Building upon Google's Gemma 2 architecture, this model is specifically optimized to enhance performance on Japanese language tasks. It maintains an 8192-token context length and utilizes the original Gemma 2 tokenizer, making it suitable for applications requiring strong Japanese language understanding and generation.
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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.
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top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
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top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
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frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
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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.
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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.
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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.
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