vandijklab/C2S-Scale-Gemma-2-2B
C2S-Scale-Gemma-2-2B is a 2.6 billion parameter decoder-only transformer model, built upon the Gemma-2 2B architecture and fine-tuned by van Dijk Lab (Yale), Google Research, and Google DeepMind. This model specializes in single-cell biology, processing single-cell RNA sequencing (scRNA-seq) data by converting it into "cell sentences." It excels at tasks such as cell type prediction, tissue classification, and generating biologically meaningful cell representations, serving as a powerful foundation for single-cell analysis.
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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