The jhu-clsp/rank1-14b is a 14.8 billion parameter reasoning reranker model, based on the Qwen2.5-14B architecture, developed by jhu-clsp. It is specifically designed for information retrieval tasks, leveraging test-time compute to generate explicit reasoning chains before making relevance judgments. This model excels at breaking down complex relevance decisions into logical steps, improving performance on nuanced retrieval tasks. Its primary strength lies in its unique 'think before judging' mechanism for enhanced relevance scoring.
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jhu-clsp/rank1-14bMost 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.