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feat(model): add deepseek-r1 for openrouter (#13312)
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sinomoe authored Feb 7, 2025
1 parent 04d13a8 commit 6e5c915
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- mistralai/mistral-7b-instruct
- qwen/qwen-2.5-72b-instruct
- qwen/qwen-2-72b-instruct
- deepseek/deepseek-r1
- deepseek/deepseek-chat
- deepseek/deepseek-coder
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Expand Up @@ -53,7 +53,7 @@ parameter_rules:
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"
input: "0.49"
output: "0.89"
unit: "0.000001"
currency: USD
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model: deepseek/deepseek-r1
label:
en_US: deepseek-r1
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 163840
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: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- 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: "3"
output: "8"
unit: "0.000001"
currency: USD

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