英文字典中文字典


英文字典中文字典51ZiDian.com



中文字典辞典   英文字典 a   b   c   d   e   f   g   h   i   j   k   l   m   n   o   p   q   r   s   t   u   v   w   x   y   z       







请输入英文单字，中文词皆可：

horsehide    

安装中文字典英文字典查询工具!


中文字典英文字典工具:
选择颜色:
输入中英文单字

































































英文字典中文字典相关资料:


  • Sampling Parameters — vLLM
    By default, best_of is set to n presence_penalty – Float that penalizes new tokens based on whether they appear in the generated text so far Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens
  • Vendor-recommended LLM parameter quick reference - Muxup
    They also suggest setting the presence_penalty between 0 and 2 to reduce endless repetitions The Qwen 3 technical report notes the same parameters but also states that for the non-thinking mode they set presence_penalty=1 5 and applied the same setting for thinking mode for the Creative Writing v3 and WritingBench benchmarks THUDM GLM-4-32B-0414
  • Potential degredation in sampling too repetitive #712 - GitHub
    I modified vllm so that it never generates those special tokens like HF's bad_words_ids, but the issue persists (those special tokens will also make the inference quality significantly worse especially with non-chat prompts, but it is a different issue)
  • vLLM - Outlines
    By default, best_of is set to n None: presence_penalty: float: Float that penalizes new tokens based on whether they appear in the generated text so far Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens 0 0: frequency_penalty: float
  • vllm. sampling_params — vLLM - Read the Docs
    By default, `best_of` is set to `n` presence_penalty: Float that penalizes new tokens based on whether they appear in the generated text so far Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens frequency_penalty: Float that penalizes new tokens based on their frequency in the generated
  • How to use Presence Penalty - vellum. ai
    The Presence Penalty parameter prevents the model from repeating a word, even if it’s only been used once It basically tells the model, “You’ve already used that word once — try something else ”
  • [D] Understanding frequency penalty, presence penalty . . .
    Will increasing the frequency penalty, presence penalty, or repetition penalty help here? My understanding is that they reduce repetition within the generated text (aka avoid repeating a word multiple times), but they don't prevent repeating words or phrases that appear in the prompt





中文字典-英文字典  2005-2009