|
@@ -0,0 +1,64 @@
|
|
|
+model: grok-vision-beta
|
|
|
+label:
|
|
|
+ en_US: Grok Vision Beta
|
|
|
+model_type: llm
|
|
|
+features:
|
|
|
+ - agent-thought
|
|
|
+ - vision
|
|
|
+model_properties:
|
|
|
+ mode: chat
|
|
|
+ context_size: 8192
|
|
|
+parameter_rules:
|
|
|
+ - name: temperature
|
|
|
+ label:
|
|
|
+ en_US: "Temperature"
|
|
|
+ zh_Hans: "采样温度"
|
|
|
+ type: float
|
|
|
+ default: 0.7
|
|
|
+ min: 0.0
|
|
|
+ max: 2.0
|
|
|
+ precision: 1
|
|
|
+ required: true
|
|
|
+ help:
|
|
|
+ en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
|
|
|
+ zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内,值越高,输出越随机和创造性;值越低,输出越稳定。建议根据需求调整 top_p 或 temperature 参数,避免同时调整两者。"
|
|
|
+
|
|
|
+ - name: top_p
|
|
|
+ label:
|
|
|
+ en_US: "Top P"
|
|
|
+ zh_Hans: "Top P"
|
|
|
+ type: float
|
|
|
+ default: 0.7
|
|
|
+ min: 0.0
|
|
|
+ max: 1.0
|
|
|
+ precision: 1
|
|
|
+ required: true
|
|
|
+ help:
|
|
|
+ en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
|
|
|
+ zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens;当 top_p 为 0 时,此参数无效。建议根据需求调整 top_p 或 temperature 参数,避免同时调整两者。"
|
|
|
+
|
|
|
+ - name: frequency_penalty
|
|
|
+ use_template: frequency_penalty
|
|
|
+ label:
|
|
|
+ en_US: "Frequency Penalty"
|
|
|
+ zh_Hans: "频率惩罚"
|
|
|
+ type: float
|
|
|
+ default: 0
|
|
|
+ min: 0
|
|
|
+ max: 2.0
|
|
|
+ precision: 1
|
|
|
+ required: false
|
|
|
+ help:
|
|
|
+ en_US: "Number between 0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
|
|
|
+ zh_Hans: "介于0和2.0之间的数字。正值会根据新标记在文本中迄今为止的现有频率来惩罚它们,从而降低模型一字不差地重复同一句话的可能性。"
|
|
|
+
|
|
|
+ - name: user
|
|
|
+ use_template: text
|
|
|
+ label:
|
|
|
+ en_US: "User"
|
|
|
+ zh_Hans: "用户"
|
|
|
+ type: string
|
|
|
+ required: false
|
|
|
+ help:
|
|
|
+ en_US: "Used to track and differentiate conversation requests from different users."
|
|
|
+ zh_Hans: "用于追踪和区分不同用户的对话请求。"
|