mirror of
https://github.com/xtekky/gpt4free.git
synced 2024-11-10 19:11:01 +00:00
498 lines
10 KiB
Python
498 lines
10 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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Bing,
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Blackbox,
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Chatgpt4o,
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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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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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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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Vercel,
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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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OpenaiChat,
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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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])
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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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OpenaiChat,
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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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])
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)
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gpt_35_turbo_16k = Model(
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name = 'gpt-3.5-turbo-16k',
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base_provider = 'openai',
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best_provider = gpt_35_long.best_provider
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)
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gpt_35_turbo_16k_0613 = Model(
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name = 'gpt-3.5-turbo-16k-0613',
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base_provider = 'openai',
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best_provider = gpt_35_long.best_provider
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)
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gpt_35_turbo_0613 = Model(
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name = 'gpt-3.5-turbo-0613',
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base_provider = 'openai',
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best_provider = gpt_35_turbo.best_provider
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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, Liaobots,
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])
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)
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gpt_4_0613 = Model(
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name = 'gpt-4-0613',
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base_provider = 'openai',
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best_provider = gpt_4.best_provider
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)
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gpt_4_32k = Model(
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name = 'gpt-4-32k',
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base_provider = 'openai',
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best_provider = gpt_4.best_provider
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)
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gpt_4_32k_0613 = Model(
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name = 'gpt-4-32k-0613',
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base_provider = 'openai',
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best_provider = gpt_4.best_provider
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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 = Bing
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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
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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_2_70b_chat = Model(
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name = "meta/llama-2-70b-chat",
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base_provider = "meta",
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best_provider = IterListProvider([ReplicateHome])
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)
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llama3_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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llama3_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, HuggingChat, DDG])
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)
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codellama_34b_instruct = Model(
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name = "codellama/CodeLlama-34b-Instruct-hf",
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base_provider = "meta",
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best_provider = HuggingChat
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)
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codellama_70b_instruct = Model(
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name = "codellama/CodeLlama-70b-Instruct-hf",
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base_provider = "meta",
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best_provider = IterListProvider([DeepInfra])
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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])
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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, ReplicateHome])
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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, GeminiProChat])
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)
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# gemma
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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_v2 = Model(
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name = 'claude-v2',
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base_provider = 'anthropic',
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best_provider = IterListProvider([Vercel])
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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 = You
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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 = You
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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])
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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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### 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([ReplicateHome, DeepInfraImage])
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)
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### AI Forever ###
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kandinsky_2_2 = Model(
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name = 'ai-forever/kandinsky-2.2',
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base_provider = 'AI Forever',
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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-turbo-0613' : gpt_35_turbo_0613,
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'gpt-3.5-turbo-16k' : gpt_35_turbo_16k,
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'gpt-3.5-turbo-16k-0613' : gpt_35_turbo_16k_0613,
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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-4' : gpt_4,
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'gpt-4-0613' : gpt_4_0613,
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'gpt-4-32k' : gpt_4_32k,
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'gpt-4-32k-0613' : gpt_4_32k_0613,
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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-2-70b-chat': llama_2_70b_chat,
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'llama3-8b': llama3_8b_instruct, # alias
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'llama3-70b': llama3_70b_instruct, # alias
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'llama3-8b-instruct' : llama3_8b_instruct,
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'llama3-70b-instruct': llama3_70b_instruct,
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'codellama-34b-instruct': codellama_34b_instruct,
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'codellama-70b-instruct': codellama_70b_instruct,
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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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# gemma
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'gemma-2-9b-it': gemma_2_9b_it,
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'gemma-2-27b-it': gemma_2_27b_it,
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### Anthropic ###
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'claude-v2': claude_v2,
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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-haiku': claude_3_haiku,
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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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# Other
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'pi': pi,
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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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### AI Forever ###
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'kandinsky-2.2': kandinsky_2_2,
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}
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_all_models = list(ModelUtils.convert.keys())
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