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609 lines
14 KiB
Python
609 lines
14 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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AI365VIP,
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Allyfy,
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Bing,
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Blackbox,
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ChatGot,
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Chatgpt4o,
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Chatgpt4Online,
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ChatgptFree,
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DDG,
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DeepInfra,
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DeepInfraImage,
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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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GeminiProChat,
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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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LiteIcoding,
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MagickPenAsk,
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MagickPenChat,
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Marsyoo,
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MetaAI,
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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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TeachAnything,
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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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Bing,
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You,
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OpenaiChat,
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FreeChatgpt,
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AI365VIP,
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Chatgpt4o,
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DDG,
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ChatgptFree,
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Koala,
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Pizzagpt,
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])
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)
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# GPT-3.5 too, but all providers supports long requests and responses
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gpt_35_long = 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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FreeGpt,
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You,
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Koala,
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ChatgptFree,
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FreeChatgpt,
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DDG,
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AI365VIP,
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Pizzagpt,
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Allyfy,
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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-3.5 / GPT-4 ###
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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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FreeGpt,
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You,
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Koala,
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ChatgptFree,
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FreeChatgpt,
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AI365VIP,
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Pizzagpt,
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Allyfy,
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])
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)
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# gpt-4
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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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Bing, Chatgpt4Online
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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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Bing, Liaobots, LiteIcoding
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])
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)
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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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You, Liaobots, Chatgpt4o, AI365VIP, OpenaiChat, Marsyoo, LiteIcoding, MagickPenAsk,
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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, OpenaiChat, You, FreeNetfly, MagickPenChat,
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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",
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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_instruct = Model(
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name = "meta-llama/Meta-Llama-3-8B-Instruct",
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base_provider = "meta",
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best_provider = IterListProvider([DeepInfra, PerplexityLabs, Replicate])
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)
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llama_3_70b_instruct = Model(
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name = "meta-llama/Meta-Llama-3-70B-Instruct",
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base_provider = "meta",
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best_provider = IterListProvider([DeepInfra, PerplexityLabs, Replicate])
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)
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llama_3_70b_instruct = Model(
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name = "meta/meta-llama-3-70b-instruct",
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base_provider = "meta",
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best_provider = IterListProvider([ReplicateHome, TeachAnything])
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)
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llama_3_70b_chat_hf = Model(
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name = "meta-llama/Llama-3-70b-chat-hf",
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base_provider = "meta",
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best_provider = IterListProvider([DDG])
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)
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llama_3_1_70b_instruct = Model(
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name = "meta-llama/Meta-Llama-3.1-70B-Instruct",
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base_provider = "meta",
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best_provider = IterListProvider([HuggingChat, HuggingFace])
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)
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llama_3_1_405b_instruct_FP8 = Model(
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name = "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8",
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base_provider = "meta",
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best_provider = IterListProvider([HuggingChat, HuggingFace])
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)
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### Mistral ###
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mixtral_8x7b = Model(
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name = "mistralai/Mixtral-8x7B-Instruct-v0.1",
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base_provider = "huggingface",
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best_provider = IterListProvider([DeepInfra, HuggingFace, PerplexityLabs, HuggingChat, DDG, ReplicateHome])
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)
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mistral_7b_v02 = Model(
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name = "mistralai/Mistral-7B-Instruct-v0.2",
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base_provider = "huggingface",
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best_provider = IterListProvider([DeepInfra, HuggingFace, HuggingChat])
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)
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### NousResearch ###
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Nous_Hermes_2_Mixtral_8x7B_DPO = Model(
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name = "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
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base_provider = "NousResearch",
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best_provider = IterListProvider([HuggingFace, HuggingChat])
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)
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### 01-ai ###
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Yi_1_5_34B_chat = Model(
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name = "01-ai/Yi-1.5-34B-Chat",
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base_provider = "01-ai",
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best_provider = IterListProvider([HuggingFace, HuggingChat])
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)
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### Microsoft ###
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Phi_3_mini_4k_instruct = Model(
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name = "microsoft/Phi-3-mini-4k-instruct",
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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, You, ChatGot, GeminiProChat, Liaobots, LiteIcoding])
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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])
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)
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gemini_1_5 = Model(
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name = 'gemini-1.5',
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base_provider = 'Google',
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best_provider = IterListProvider([LiteIcoding])
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)
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# gemma
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gemma_2b_it = Model(
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name = 'gemma-2b-it',
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base_provider = 'Google',
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best_provider = IterListProvider([ReplicateHome])
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)
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gemma_2_9b_it = Model(
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name = 'gemma-2-9b-it',
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base_provider = 'Google',
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best_provider = IterListProvider([PerplexityLabs])
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)
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gemma_2_27b_it = Model(
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name = 'gemma-2-27b-it',
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base_provider = 'Google',
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best_provider = IterListProvider([PerplexityLabs])
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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([You, 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([You, 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, AI365VIP, Liaobots])
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)
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claude_3 = Model(
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name = 'claude-3',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([LiteIcoding])
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)
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claude_3_5 = Model(
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name = 'claude-3.5',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([LiteIcoding])
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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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### NVIDIA ###
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nemotron_4_340b_instruct = Model(
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name = 'nemotron-4-340b-instruct',
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base_provider = 'NVIDIA',
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best_provider = IterListProvider([PerplexityLabs])
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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 = 'databricks/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 = 'CohereForAI/c4ai-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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### DeepSeek ###
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deepseek_coder = Model(
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name = 'deepseek-coder',
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base_provider = 'DeepSeek',
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best_provider = IterListProvider([FreeChatgpt])
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)
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deepseek_chat = Model(
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name = 'deepseek-chat',
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base_provider = 'DeepSeek',
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best_provider = IterListProvider([FreeChatgpt])
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)
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### Qwen ###
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Qwen2_7B_instruct = Model(
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name = 'Qwen2-7B-Instruct',
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base_provider = 'Qwen',
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best_provider = IterListProvider([FreeChatgpt])
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)
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### Zhipu AI ###
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glm4_9B_chat = Model(
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name = 'glm4-9B-chat',
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base_provider = 'Zhipu AI',
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best_provider = IterListProvider([FreeChatgpt])
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)
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chatglm3_6B = Model(
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name = 'chatglm3-6B',
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base_provider = 'Zhipu AI',
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best_provider = IterListProvider([FreeChatgpt])
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)
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### 01-ai ###
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Yi_1_5_9B_chat = Model(
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name = 'Yi-1.5-9B-Chat',
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base_provider = '01-ai',
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best_provider = IterListProvider([FreeChatgpt])
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)
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### Other ###
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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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#############
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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 = 'stability-ai/sdxl',
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base_provider = 'Stability AI',
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best_provider = IterListProvider([DeepInfraImage])
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)
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stable_diffusion_3 = Model(
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name = 'stability-ai/stable-diffusion-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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sdxl_lightning_4step = Model(
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name = 'bytedance/sdxl-lightning-4step',
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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_v2_5_1024px_aesthetic = Model(
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name = 'playgroundai/playground-v2.5-1024px-aesthetic',
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base_provider = 'Stability AI',
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best_provider = IterListProvider([ReplicateHome])
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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-3.5 / GPT-4 ###
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# gpt-3.5
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'gpt-3.5-turbo': gpt_35_turbo,
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'gpt-3.5-long': gpt_35_long,
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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-3-8b-instruct': llama_3_8b_instruct,
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'llama-3-70b-instruct': llama_3_70b_instruct,
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'llama-3-70b-chat': llama_3_70b_chat_hf,
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'llama-3-70b-instruct': llama_3_70b_instruct,
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'llama-3.1-70b-instruct': llama_3_1_70b_instruct,
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'llama-3.1-405b-instruct': llama_3_1_405b_instruct_FP8,
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### Mistral (Opensource) ###
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'mixtral-8x7b': mixtral_8x7b,
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'mistral-7b-v02': mistral_7b_v02,
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### NousResearch ###
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'Nous-Hermes-2-Mixtral-8x7B-DPO': Nous_Hermes_2_Mixtral_8x7B_DPO,
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### 01-ai ###
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'Yi-1.5-34b-chat': Yi_1_5_34B_chat,
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### Microsoft ###
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'Phi-3-mini-4k-instruct': Phi_3_mini_4k_instruct,
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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-pro': gemini_1_5,
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'gemini-flash': gemini_flash,
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# gemma
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'gemma-2b': gemma_2b_it,
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'gemma-2-9b': gemma_2_9b_it,
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'gemma-2-27b': gemma_2_27b_it,
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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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'claude-3-opus': claude_3,
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'claude-3-5-sonnet': claude_3_5,
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### Reka AI ###
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'reka': reka_core,
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### NVIDIA ###
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'nemotron-4-340b-instruct': nemotron_4_340b_instruct,
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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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### DeepSeek ###
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'deepseek-coder': deepseek_coder,
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'deepseek-chat': deepseek_chat,
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### Qwen ###
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'Qwen2-7b-instruct': Qwen2_7B_instruct,
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|
|
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### Zhipu AI ###
|
|
'glm4-9b-chat': glm4_9B_chat,
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'chatglm3-6b': chatglm3_6B,
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|
### 01-ai ###
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'Yi-1.5-9b-chat': Yi_1_5_9B_chat,
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# Other
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'pi': pi,
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|
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|
#############
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|
### Image ###
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|
#############
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|
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### Stability AI ###
|
|
'sdxl': sdxl,
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|
'stable-diffusion-3': stable_diffusion_3,
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|
|
|
### ByteDance ###
|
|
'sdxl-lightning': sdxl_lightning_4step,
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|
|
|
### Playground ###
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'playground-v2.5': playground_v2_5_1024px_aesthetic,
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|
|
|
}
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|
_all_models = list(ModelUtils.convert.keys())
|