Model (fundebug-java-notifier 1.0.2 API)

java.lang.Object
- java.lang.Enum<Model>
- - cn.jianwoo.openai.chatgptapi.constants.Model

所有已实现的接口:

Serializable, Comparable<Model>
```
public enum Model
extends Enum<Model>
```
常用的模型枚举
虽然PostApiService.models 接口中返回了69 中模型，这里只列举 https://platform.openai.com/docs/models/ 中所常用的几个模型

作者:

gulihua

枚举常量概要

枚举常量
枚举常量和说明
`ADA` Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost.
`BABBAGE` Capable of straightforward tasks, very fast, and lower cost.
`CODE_CUSHMAN_001` Almost as capable as Davinci Codex, but slightly faster.
`CODE_DAVINCI_002` Most capable Codex model.
`CURIE` Very capable, but faster and lower cost than Davinci.
`CURIE_INSTRUCT_BETA` curie-instruct-beta
`DAVINCI` Most capable GPT-3 model.
`DAVINCI_INSTRUCT_BETA` davinci-instruct-beta
`GPT_35_TURBO` Most capable GPT-3.5 model and optimized for chat at 1/10th the cost of text-davinci-003.
`GPT_35_TURBO_0301` Snapshot of gpt-3.5-turbo from March 1st 2023.
`TEXT_ADA_001` Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost.
`TEXT_BABBAGE_001` Capable of straightforward tasks, very fast, and lower cost.
`TEXT_CURIE_001` Very capable, faster and lower cost than Davinci.
`TEXT_DAVINCI_001` text-davinci-001
`TEXT_DAVINCI_002` Similar capabilities to text-davinci-003 but trained with supervised fine-tuning instead of reinforcement learning
`TEXT_DAVINCI_003` Can do any language task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models.
`TEXT_EMBEDDING_ADA_002` Embeddings are a numerical representation of text that can be used to measure the relateness between two pieces of text.
`WHISPER_1` Whisper is a general-purpose speech recognition model.

方法概要

所有方法静态方法实例方法具体方法
限定符和类型	方法和说明
`String`	`getName()`
`void`	`setName(String name)`
`static Model`	`valueOf(String name)` 返回带有指定名称的该类型的枚举常量。
`static Model[]`	`values()` 按照声明该枚举类型的常量的顺序, 返回包含这些常量的数组。

从类继承的方法 java.lang.Enum
clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf

从类继承的方法 java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- 枚举常量详细资料
  - DAVINCI
```
public static final Model DAVINCI
```
    Most capable GPT-3 model. Can do any task the other models can do, often with higher quality.
  - TEXT_DAVINCI_003
```
public static final Model TEXT_DAVINCI_003
```
    Can do any language task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models. Also supports inserting completions within text.
  - TEXT_DAVINCI_001
```
public static final Model TEXT_DAVINCI_001
```
    text-davinci-001
  - CODE_DAVINCI_002
```
public static final Model CODE_DAVINCI_002
```
    Most capable Codex model. Particularly good at translating natural language to code. In addition to completing code, also supports inserting completions within code.
  - TEXT_CURIE_001
```
public static final Model TEXT_CURIE_001
```
    Very capable, faster and lower cost than Davinci.
  - CURIE
```
public static final Model CURIE
```
    Very capable, but faster and lower cost than Davinci.
  - TEXT_BABBAGE_001
```
public static final Model TEXT_BABBAGE_001
```
    Capable of straightforward tasks, very fast, and lower cost.
  - TEXT_ADA_001
```
public static final Model TEXT_ADA_001
```
    Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost.
  - ADA
```
public static final Model ADA
```
    Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost.
  - DAVINCI_INSTRUCT_BETA
```
public static final Model DAVINCI_INSTRUCT_BETA
```
    davinci-instruct-beta
  - CURIE_INSTRUCT_BETA
```
public static final Model CURIE_INSTRUCT_BETA
```
    curie-instruct-beta
  - CODE_CUSHMAN_001
```
public static final Model CODE_CUSHMAN_001
```
    Almost as capable as Davinci Codex, but slightly faster. This speed advantage may make it preferable for real-time applications.
  - BABBAGE
```
public static final Model BABBAGE
```
    Capable of straightforward tasks, very fast, and lower cost.
  - TEXT_DAVINCI_002
```
public static final Model TEXT_DAVINCI_002
```
    Similar capabilities to text-davinci-003 but trained with supervised fine-tuning instead of reinforcement learning
  - GPT_35_TURBO
```
public static final Model GPT_35_TURBO
```
    Most capable GPT-3.5 model and optimized for chat at 1/10th the cost of text-davinci-003. Will be updated with our latest model iteration.
  - GPT_35_TURBO_0301
```
public static final Model GPT_35_TURBO_0301
```
    Snapshot of gpt-3.5-turbo from March 1st 2023. Unlike gpt-3.5-turbo, this model will not receive updates, and will only be supported for a three month period ending on June 1st 2023.
  - WHISPER_1
```
public static final Model WHISPER_1
```
    Whisper is a general-purpose speech recognition model.
  - TEXT_EMBEDDING_ADA_002
```
public static final Model TEXT_EMBEDDING_ADA_002
```
    Embeddings are a numerical representation of text that can be used to measure the relateness between two pieces of text.
- 方法详细资料
  - values
```
public static Model[] values()
```
    按照声明该枚举类型的常量的顺序, 返回包含这些常量的数组。该方法可用于迭代常量, 如下所示:
```
for (Model c : Model.values())
    System.out.println(c);
```
    返回:
    
    按照声明该枚举类型的常量的顺序返回的包含这些常量的数组
  - valueOf
```
public static Model valueOf(String name)
```
    返回带有指定名称的该类型的枚举常量。字符串必须与用于声明该类型的枚举常量的标识符完全匹配。(不允许有多余的空格字符。)
    
    参数:
    
    name - 要返回的枚举常量的名称。
    
    返回:
    
    返回带有指定名称的枚举常量
    
    抛出:
    
    IllegalArgumentException - 如果该枚举类型没有带有指定名称的常量
    
    NullPointerException - 如果参数为空值
  - getName
```
public String getName()
```
  - setName
```
public void setName(String name)
```

枚举 Model

枚举常量概要

方法概要

从类继承的方法 java.lang.Enum

从类继承的方法 java.lang.Object

枚举常量详细资料

DAVINCI

TEXT_DAVINCI_003

TEXT_DAVINCI_001

CODE_DAVINCI_002

TEXT_CURIE_001

CURIE

TEXT_BABBAGE_001

TEXT_ADA_001

ADA

DAVINCI_INSTRUCT_BETA

CURIE_INSTRUCT_BETA

CODE_CUSHMAN_001

BABBAGE

TEXT_DAVINCI_002

GPT_35_TURBO

GPT_35_TURBO_0301

WHISPER_1

TEXT_EMBEDDING_ADA_002

方法详细资料

values

valueOf

getName

setName