Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B is a 7 billion parameter end-to-end trained policy model developed by Gen-Verse, specifically designed for long Chain-of-Thought (CoT) reasoning. It excels at solving complex tasks and problems, particularly in math and science reasoning. This model leverages a trajectory-aware process reward model (PRM) for data selection and reinforcement learning, enabling fine-grained reward assignment aligned with structured reasoning traces.
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Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7BMost 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.