nvidia/Qwen3-Nemotron-14B-BRRM
The nvidia/Qwen3-Nemotron-14B-BRRM is a 14 billion parameter Branch-and-Rethink Reasoning Reward Model developed by NVIDIA. This model implements a novel two-turn reasoning framework for evaluating LLM-generated responses, performing adaptive branching and branch-conditioned rethinking to focus on critical evaluation dimensions. It achieves state-of-the-art performance on major reward modeling benchmarks, making it suitable for integrating into RLHF pipelines to improve LLM response quality.
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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