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c69f5b07ba
Co-authored-by: -LAN- <laipz8200@outlook.com>
131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
from core.model_runtime.entities.model_entities import DefaultParameterName
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PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
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DefaultParameterName.TEMPERATURE: {
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"label": {
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"en_US": "Temperature",
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"zh_Hans": "温度",
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},
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"type": "float",
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"help": {
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"en_US": "Controls randomness. Lower temperature results in less random completions."
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" As the temperature approaches zero, the model will become deterministic and repetitive."
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" Higher temperature results in more random completions.",
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"zh_Hans": "温度控制随机性。较低的温度会导致较少的随机完成。随着温度接近零,模型将变得确定性和重复性。"
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"较高的温度会导致更多的随机完成。",
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},
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"required": False,
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"default": 0.0,
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"min": 0.0,
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"max": 1.0,
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"precision": 2,
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},
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DefaultParameterName.TOP_P: {
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"label": {
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"en_US": "Top P",
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"zh_Hans": "Top P",
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},
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"type": "float",
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"help": {
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"en_US": "Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options"
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" are considered.",
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"zh_Hans": "通过核心采样控制多样性:0.5表示考虑了一半的所有可能性加权选项。",
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},
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"required": False,
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"precision": 2,
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},
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DefaultParameterName.TOP_K: {
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"label": {
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"en_US": "Top K",
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"zh_Hans": "Top K",
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},
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"type": "int",
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"help": {
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"en_US": "Limits the number of tokens to consider for each step by keeping only the k most likely tokens.",
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"zh_Hans": "通过只保留每一步中最可能的 k 个标记来限制要考虑的标记数量。",
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},
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"required": False,
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"default": 50,
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"min": 1,
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"max": 100,
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"precision": 0,
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},
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DefaultParameterName.PRESENCE_PENALTY: {
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"label": {
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"en_US": "Presence Penalty",
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"zh_Hans": "存在惩罚",
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},
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"type": "float",
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"help": {
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"en_US": "Applies a penalty to the log-probability of tokens already in the text.",
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"zh_Hans": "对文本中已有的标记的对数概率施加惩罚。",
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},
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"required": False,
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"default": 0.0,
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"min": 0.0,
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"max": 1.0,
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"precision": 2,
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},
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DefaultParameterName.FREQUENCY_PENALTY: {
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"label": {
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"en_US": "Frequency Penalty",
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"zh_Hans": "频率惩罚",
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},
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"type": "float",
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"help": {
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"en_US": "Applies a penalty to the log-probability of tokens that appear in the text.",
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"zh_Hans": "对文本中出现的标记的对数概率施加惩罚。",
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},
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"required": False,
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"default": 0.0,
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"min": 0.0,
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"max": 1.0,
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"precision": 2,
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},
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DefaultParameterName.MAX_TOKENS: {
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"label": {
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"en_US": "Max Tokens",
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"zh_Hans": "最大标记",
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},
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"type": "int",
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"help": {
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"en_US": "Specifies the upper limit on the length of generated results."
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" If the generated results are truncated, you can increase this parameter.",
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"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。",
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},
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"required": False,
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"default": 64,
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"min": 1,
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"max": 2048,
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"precision": 0,
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},
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DefaultParameterName.RESPONSE_FORMAT: {
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"label": {
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"en_US": "Response Format",
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"zh_Hans": "回复格式",
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},
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"type": "string",
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"help": {
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"en_US": "Set a response format, ensure the output from llm is a valid code block as possible,"
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" such as JSON, XML, etc.",
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"zh_Hans": "设置一个返回格式,确保llm的输出尽可能是有效的代码块,如JSON、XML等",
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},
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"required": False,
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"options": ["JSON", "XML"],
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},
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DefaultParameterName.JSON_SCHEMA: {
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"label": {
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"en_US": "JSON Schema",
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},
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"type": "text",
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"help": {
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"en_US": "Set a response json schema will ensure LLM to adhere it.",
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"zh_Hans": "设置返回的json schema,llm将按照它返回",
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},
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"required": False,
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},
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}
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