The nvidia/Llama-3.3-Nemotron-70B-Select is a 70 billion parameter large language model developed by NVIDIA, built upon the Meta-Llama-3.3-70B-Instruct foundation. It is specifically fine-tuned using scaled Bradley-Terry modeling to select the most helpful LLM-generated responses to user queries. This model is designed to improve performance in general-domain, open-ended tasks by identifying high-quality outputs, making it suitable for integration into Inference-Time-Scaling systems.
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nvidia/Llama-3.3-Nemotron-70B-SelectMost 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.