meituan-longcat/UNO-Scorer-Qwen3-14B
UNO-Scorer-Qwen3-14B by meituan-longcat is a 14 billion parameter LLM-based evaluation model built on the Qwen3-14B architecture, designed for automated scoring of Large Multimodal Models (LMMs). Fine-tuned on 13K in-house data, it provides numerical scores and detailed reasoning by comparing sub-questions against reference answers. This model excels in evaluating complex Multi-Step Open-Ended Questions, demonstrating superior accuracy compared to models like GPT-4.1 in this specific domain.
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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