model: deepseek/deepseek-chat
label:
  en_US: deepseek-chat
model_type: llm
features:
  - agent-thought
model_properties:
  mode: chat
  context_size: 32000
parameter_rules:
  - name: temperature
    use_template: temperature
    type: float
    default: 1
    min: 0.0
    max: 2.0
    help:
      zh_Hans: 控制生成结果的多样性和随机性。数值越小，越严谨；数值越大，越发散。
      en_US: Control the diversity and randomness of generated results. The smaller the value, the more rigorous it is; the larger the value, the more divergent it is.
  - name: max_tokens
    use_template: max_tokens
    type: int
    default: 4096
    min: 1
    max: 4096
    help:
      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
  - name: top_p
    use_template: top_p
    type: float
    default: 1
    min: 0.01
    max: 1.00
    help:
      zh_Hans: 控制生成结果的随机性。数值越小，随机性越弱；数值越大，随机性越强。一般而言，top_p 和 temperature 两个参数选择一个进行调整即可。
      en_US: Control the randomness of generated results. The smaller the value, the weaker the randomness; the larger the value, the stronger the randomness. Generally speaking, you can adjust one of the two parameters top_p and temperature.
  - name: frequency_penalty
    use_template: frequency_penalty
    default: 0
    min: -2.0
    max: 2.0
    help:
      zh_Hans: 介于 -2.0 和 2.0 之间的数字。如果该值为正，那么新 token 会根据其在已有文本中的出现频率受到相应的惩罚，降低模型重复相同内容的可能性。
      en_US: A number between -2.0 and 2.0. If the value is positive, new tokens are penalized based on their frequency of occurrence in existing text, reducing the likelihood that the model will repeat the same content.
pricing:
  input: "0.14"
  output: "0.28"
  unit: "0.000001"
  currency: USD
