MMR-DAPO by kangdawei is a 1.5 billion parameter language model, fine-tuned from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. It was trained using the DAPO reinforcement learning method on the knoveleng/open-rs dataset, featuring a substantial 131072-token context length. This model is optimized for generating responses based on its specialized training, making it suitable for conversational AI and text generation tasks.
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kangdawei/MMR-DAPOMost 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.