Sakana AI's RLT-7B is a 7.6 billion parameter autoregressive language model with a 131,072 token context length, developed using the Reinforcement-Learned Teachers (RLT) pipeline. This student model is distilled from a 7B Reinforcement-Learned Teacher explicitly trained to produce high-quality reasoning traces for student distillation. It is optimized for reasoning tasks, leveraging supervised fine-tuning with specific hyperparameters and reasoning tags. The model is provided for research and development purposes as an experimental prototype.
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SakanaAI/RLT-7BMost 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.