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677 lines
13 KiB
Python
677 lines
13 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from .Provider import IterListProvider, ProviderType
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from .Provider import (
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AiChatOnline,
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Allyfy,
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Bing,
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Binjie,
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Bixin123,
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Blackbox,
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ChatGot,
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Chatgpt4Online,
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Chatgpt4o,
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ChatgptFree,
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CodeNews,
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DDG,
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DeepInfra,
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DeepInfraImage,
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FluxAirforce,
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Free2GPT,
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FreeChatgpt,
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FreeGpt,
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FreeNetfly,
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Gemini,
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GeminiPro,
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GigaChat,
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HuggingChat,
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HuggingFace,
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Koala,
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Liaobots,
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MagickPen,
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MetaAI,
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Nexra,
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OpenaiChat,
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PerplexityLabs,
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Pi,
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Pizzagpt,
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Reka,
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Replicate,
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ReplicateHome,
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Snova,
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TeachAnything,
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TwitterBio,
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Upstage,
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You,
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)
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@dataclass(unsafe_hash=True)
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class Model:
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"""
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Represents a machine learning model configuration.
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Attributes:
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name (str): Name of the model.
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base_provider (str): Default provider for the model.
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best_provider (ProviderType): The preferred provider for the model, typically with retry logic.
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"""
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name: str
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base_provider: str
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best_provider: ProviderType = None
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@staticmethod
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def __all__() -> list[str]:
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"""Returns a list of all model names."""
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return _all_models
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default = Model(
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name = "",
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base_provider = "",
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best_provider = IterListProvider([
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DDG,
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FreeChatgpt,
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HuggingChat,
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Pizzagpt,
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ChatgptFree,
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ReplicateHome,
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Upstage,
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Blackbox,
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Bixin123,
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Binjie,
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Free2GPT,
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MagickPen,
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])
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)
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############
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### Text ###
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############
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### OpenAI ###
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# gpt-3
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gpt_3 = Model(
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name = 'gpt-3',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([
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Nexra,
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])
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)
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# gpt-3.5
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gpt_35_turbo = Model(
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name = 'gpt-3.5-turbo',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([
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Allyfy, TwitterBio, Nexra, Bixin123, CodeNews,
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])
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)
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# gpt-4
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gpt_4o = Model(
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name = 'gpt-4o',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([
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Liaobots, Chatgpt4o, OpenaiChat,
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])
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)
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gpt_4o_mini = Model(
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name = 'gpt-4o-mini',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([
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DDG, Liaobots, You, FreeNetfly, Pizzagpt, ChatgptFree, AiChatOnline, CodeNews,
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MagickPen, OpenaiChat, Koala,
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])
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)
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gpt_4_turbo = Model(
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name = 'gpt-4-turbo',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([
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Nexra, Bixin123, Liaobots, Bing
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])
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)
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gpt_4 = Model(
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name = 'gpt-4',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([
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Chatgpt4Online, Nexra, Binjie, Bing,
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gpt_4_turbo.best_provider, gpt_4o.best_provider, gpt_4o_mini.best_provider
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])
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)
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### GigaChat ###
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gigachat = Model(
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name = 'GigaChat:latest',
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base_provider = 'gigachat',
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best_provider = GigaChat
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)
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### Meta ###
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meta = Model(
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name = "meta-ai",
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base_provider = "Meta",
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best_provider = MetaAI
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)
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llama_3_8b = Model(
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name = "llama-3-8b",
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base_provider = "Meta",
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best_provider = IterListProvider([DeepInfra, Replicate])
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)
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llama_3_70b = Model(
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name = "llama-3-70b",
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base_provider = "Meta",
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best_provider = IterListProvider([ReplicateHome, DeepInfra, PerplexityLabs, Replicate])
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)
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llama_3_1_8b = Model(
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name = "llama-3.1-8b",
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base_provider = "Meta",
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best_provider = IterListProvider([Blackbox])
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)
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llama_3_1_70b = Model(
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name = "llama-3.1-70b",
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base_provider = "Meta",
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best_provider = IterListProvider([DDG, HuggingChat, FreeGpt, Blackbox, TeachAnything, Free2GPT, HuggingFace])
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)
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llama_3_1_405b = Model(
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name = "llama-3.1-405b",
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base_provider = "Meta",
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best_provider = IterListProvider([HuggingChat, Blackbox, HuggingFace])
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)
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### Mistral ###
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mixtral_8x7b = Model(
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name = "mixtral-8x7b",
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base_provider = "Mistral",
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best_provider = IterListProvider([HuggingChat, DDG, ReplicateHome, TwitterBio, DeepInfra, HuggingFace,])
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)
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mistral_7b = Model(
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name = "mistral-7b",
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base_provider = "Mistral",
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best_provider = IterListProvider([HuggingChat, HuggingFace, DeepInfra])
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)
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### 01-ai ###
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yi_1_5_34b = Model(
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name = "yi-1.5-34b",
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base_provider = "01-ai",
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best_provider = IterListProvider([HuggingChat, HuggingFace])
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)
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### Microsoft ###
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phi_3_mini_4k = Model(
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name = "phi-3-mini-4k",
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base_provider = "Microsoft",
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best_provider = IterListProvider([HuggingFace, HuggingChat])
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)
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### Google ###
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# gemini
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gemini = Model(
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name = 'gemini',
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base_provider = 'Google',
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best_provider = Gemini
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)
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gemini_pro = Model(
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name = 'gemini-pro',
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base_provider = 'Google',
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best_provider = IterListProvider([GeminiPro, ChatGot, Liaobots])
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)
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gemini_flash = Model(
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name = 'gemini-flash',
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base_provider = 'Google',
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best_provider = IterListProvider([Liaobots, Blackbox])
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)
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# gemma
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gemma_2b = Model(
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name = 'gemma-2b',
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base_provider = 'Google',
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best_provider = IterListProvider([ReplicateHome])
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)
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### Anthropic ###
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claude_2 = Model(
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name = 'claude-2',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([You])
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)
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claude_2_0 = Model(
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name = 'claude-2.0',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([Liaobots])
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)
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claude_2_1 = Model(
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name = 'claude-2.1',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([Liaobots])
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)
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claude_3_opus = Model(
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name = 'claude-3-opus',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([Liaobots])
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)
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claude_3_sonnet = Model(
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name = 'claude-3-sonnet',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([Liaobots])
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)
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claude_3_5_sonnet = Model(
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name = 'claude-3-5-sonnet',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([Liaobots])
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)
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claude_3_haiku = Model(
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name = 'claude-3-haiku',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([DDG, Liaobots])
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)
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### Reka AI ###
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reka_core = Model(
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name = 'reka-core',
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base_provider = 'Reka AI',
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best_provider = Reka
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)
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### Blackbox ###
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blackbox = Model(
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name = 'blackbox',
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base_provider = 'Blackbox',
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best_provider = Blackbox
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)
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### Databricks ###
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dbrx_instruct = Model(
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name = 'dbrx-instruct',
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base_provider = 'Databricks',
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best_provider = IterListProvider([DeepInfra])
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)
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### CohereForAI ###
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command_r_plus = Model(
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name = 'command-r-plus',
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base_provider = 'CohereForAI',
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best_provider = IterListProvider([HuggingChat])
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)
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### iFlytek ###
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sparkdesk_v1_1 = Model(
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name = 'sparkdesk-v1.1',
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base_provider = 'iFlytek',
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best_provider = IterListProvider([FreeChatgpt])
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)
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### Qwen ###
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qwen_1_5_14b = Model(
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name = 'qwen-1.5-14b',
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base_provider = 'Qwen',
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best_provider = IterListProvider([FreeChatgpt])
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)
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qwen_turbo = Model(
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name = 'qwen-turbo',
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base_provider = 'Qwen',
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best_provider = IterListProvider([Bixin123])
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)
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### Zhipu AI ###
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glm_3_6b = Model(
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name = 'glm-3-6b',
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base_provider = 'Zhipu AI',
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best_provider = IterListProvider([FreeChatgpt])
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)
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glm_4_9b = Model(
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name = 'glm-4-9B',
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base_provider = 'Zhipu AI',
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best_provider = IterListProvider([FreeChatgpt])
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)
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glm_4 = Model(
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name = 'glm-4',
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base_provider = 'Zhipu AI',
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best_provider = IterListProvider([CodeNews, glm_4_9b.best_provider,])
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)
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### 01-ai ###
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yi_1_5_9b = Model(
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name = 'yi-1.5-9b',
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base_provider = '01-ai',
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best_provider = IterListProvider([FreeChatgpt])
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)
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### Pi ###
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solar_1_mini = Model(
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name = 'solar-1-mini',
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base_provider = 'Upstage',
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best_provider = IterListProvider([Upstage])
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)
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### Pi ###
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pi = Model(
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name = 'pi',
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base_provider = 'inflection',
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best_provider = Pi
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)
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### SambaNova ###
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samba_coe_v0_1 = Model(
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name = 'samba-coe-v0.1',
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base_provider = 'SambaNova',
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best_provider = Snova
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)
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### Trong-Hieu Nguyen-Mau ###
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v1olet_merged_7b = Model(
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name = 'v1olet-merged-7b',
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base_provider = 'Trong-Hieu Nguyen-Mau',
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best_provider = Snova
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)
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### Macadeliccc ###
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westlake_7b_v2 = Model(
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name = 'westlake-7b-v2',
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base_provider = 'Macadeliccc',
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best_provider = Snova
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)
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### CookinAI ###
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donutlm_v1 = Model(
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name = 'donutlm-v1',
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base_provider = 'CookinAI',
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best_provider = Snova
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)
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### DeepSeek ###
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deepseek = Model(
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name = 'deepseek',
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base_provider = 'DeepSeek',
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best_provider = CodeNews
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)
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#############
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### Image ###
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#############
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### Stability AI ###
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sdxl = Model(
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name = 'sdxl',
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base_provider = 'Stability AI',
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best_provider = IterListProvider([ReplicateHome, DeepInfraImage])
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)
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sd_3 = Model(
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name = 'sd-3',
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base_provider = 'Stability AI',
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best_provider = IterListProvider([ReplicateHome])
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)
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### Playground ###
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playground_v2_5 = Model(
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name = 'playground-v2.5',
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base_provider = 'Stability AI',
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best_provider = IterListProvider([ReplicateHome])
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)
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### Flux AI ###
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flux = Model(
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name = 'flux',
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base_provider = 'Flux AI',
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best_provider = IterListProvider([FluxAirforce])
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)
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flux_realism = Model(
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name = 'flux-realism',
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base_provider = 'Flux AI',
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best_provider = IterListProvider([FluxAirforce])
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)
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flux_anime = Model(
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name = 'flux-anime',
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base_provider = 'Flux AI',
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best_provider = IterListProvider([FluxAirforce])
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)
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flux_3d = Model(
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name = 'flux-3d',
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base_provider = 'Flux AI',
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best_provider = IterListProvider([FluxAirforce])
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)
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flux_disney = Model(
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name = 'flux-disney',
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base_provider = 'Flux AI',
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best_provider = IterListProvider([FluxAirforce])
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)
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### ###
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dalle = Model(
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name = 'dalle',
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base_provider = '',
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best_provider = IterListProvider([Nexra])
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)
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dalle_mini = Model(
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name = 'dalle-mini',
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base_provider = '',
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best_provider = IterListProvider([Nexra])
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)
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emi = Model(
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name = 'emi',
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base_provider = '',
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best_provider = IterListProvider([Nexra])
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)
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class ModelUtils:
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"""
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Utility class for mapping string identifiers to Model instances.
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Attributes:
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convert (dict[str, Model]): Dictionary mapping model string identifiers to Model instances.
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"""
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convert: dict[str, Model] = {
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############
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### Text ###
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############
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### OpenAI ###
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# gpt-3
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'gpt-3': gpt_3,
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# gpt-3.5
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'gpt-3.5-turbo': gpt_35_turbo,
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# gpt-4
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'gpt-4o' : gpt_4o,
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'gpt-4o-mini' : gpt_4o_mini,
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'gpt-4' : gpt_4,
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'gpt-4-turbo' : gpt_4_turbo,
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### Meta ###
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"meta-ai": meta,
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# llama-3
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'llama-3-8b': llama_3_8b,
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'llama-3-70b': llama_3_70b,
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# llama-3.1
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'llama-3.1-8b': llama_3_1_8b,
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'llama-3.1-70b': llama_3_1_70b,
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'llama-3.1-405b': llama_3_1_405b,
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### Mistral ###
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'mixtral-8x7b': mixtral_8x7b,
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'mistral-7b': mistral_7b,
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### 01-ai ###
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'yi-1.5-34b': yi_1_5_34b,
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### Microsoft ###
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'phi-3-mini-4k': phi_3_mini_4k,
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### Google ###
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# gemini
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'gemini': gemini,
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'gemini-pro': gemini_pro,
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'gemini-flash': gemini_flash,
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# gemma
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'gemma-2b': gemma_2b,
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### Anthropic ###
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'claude-2': claude_2,
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'claude-2.0': claude_2_0,
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'claude-2.1': claude_2_1,
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'claude-3-opus': claude_3_opus,
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'claude-3-sonnet': claude_3_sonnet,
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'claude-3-5-sonnet': claude_3_5_sonnet,
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'claude-3-haiku': claude_3_haiku,
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### Reka AI ###
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'reka-core': reka_core,
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### Blackbox ###
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'blackbox': blackbox,
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### CohereForAI ###
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'command-r+': command_r_plus,
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### Databricks ###
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'dbrx-instruct': dbrx_instruct,
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### GigaChat ###
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'gigachat': gigachat,
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### iFlytek ###
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'sparkdesk-v1.1': sparkdesk_v1_1,
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### Qwen ###
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'qwen-1.5-14b': qwen_1_5_14b,
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'qwen-turbo': qwen_turbo,
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### Zhipu AI ###
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'glm-3-6b': glm_3_6b,
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'glm-4-9b': glm_4_9b,
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'glm-4': glm_4,
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### 01-ai ###
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'yi-1.5-9b': yi_1_5_9b,
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### Upstage ###
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'solar-1-mini': solar_1_mini,
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### Pi ###
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'pi': pi,
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### SambaNova ###
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'samba-coe-v0.1': samba_coe_v0_1,
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### Trong-Hieu Nguyen-Mau ###
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'v1olet-merged-7b': v1olet_merged_7b,
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### Macadeliccc ###
|
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'westlake-7b-v2': westlake_7b_v2,
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### CookinAI ###
|
|
'donutlm-v1': donutlm_v1,
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|
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### DeepSeek ###
|
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'deepseek': deepseek,
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#############
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### Image ###
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#############
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### Stability AI ###
|
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'sdxl': sdxl,
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'sd-3': sd_3,
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### Playground ###
|
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'playground-v2.5': playground_v2_5,
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|
|
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|
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### Flux AI ###
|
|
'flux': flux,
|
|
'flux-realism': flux_realism,
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'flux-anime': flux_anime,
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'flux-3d': flux_3d,
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'flux-disney': flux_disney,
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### ###
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'dalle': dalle,
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|
'dalle-mini': dalle_mini,
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|
'emi': emi,
|
|
}
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_all_models = list(ModelUtils.convert.keys())
|