nomic-ai/CodeRankLLM
CodeRankLLM is a 7.6 billion parameter language model developed by nomic-ai, specifically fine-tuned for listwise code-reranking. It enhances the quality of retrieved results for various code retrieval tasks when combined with performant code retrievers. This model excels at re-ranking code snippets, improving the relevance and order of search results in code-related applications. It was initialized from Qwen2.5-Coder-7B-Instruct and trained using a language modeling objective.
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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