AMindToThink/gemma-2-2b-it_RMU_s400_a300_layer7
The AMindToThink/gemma-2-2b-it_RMU_s400_a300_layer7 is a 2.6 billion parameter instruction-tuned language model based on the Gemma-2 architecture. This model is designed for general language understanding and generation tasks, leveraging its instruction-tuned nature for improved conversational abilities. With an 8192-token context length, it can process moderately long inputs for various applications. Its compact size makes it suitable for deployment in resource-constrained environments while maintaining strong performance.
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.
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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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