The jhu-clsp/rank1-32b model is a 32.8 billion parameter reasoning reranker, built upon the Qwen2.5-32B base model, designed for information retrieval tasks. It uniquely employs test-time compute to generate explicit reasoning chains before making relevance judgments for query-document pairs. This approach allows the model to break down complex relevance decisions into logical steps, enhancing performance on tasks requiring nuanced understanding. It is specifically optimized for improving the accuracy of information retrieval by providing confidence scores for relevance.
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jhu-clsp/rank1-32bMost 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.