gpt4free/g4f/models.py

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from __future__ import annotations
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from dataclasses import dataclass
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from .Provider import IterListProvider, ProviderType
from .Provider import (
AIChatFree,
Airforce,
Allyfy,
Bing,
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Binjie,
Bixin123,
Blackbox,
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ChatGpt,
Chatgpt4o,
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Chatgpt4Online,
ChatGptEs,
ChatgptFree,
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ChatHub,
DDG,
DeepInfra,
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DeepInfraChat,
DeepInfraImage,
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Free2GPT,
FreeChatgpt,
FreeGpt,
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FreeNetfly,
Gemini,
GeminiPro,
GigaChat,
GPROChat,
HuggingChat,
HuggingFace,
Koala,
Liaobots,
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LiteIcoding,
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MagickPen,
MetaAI,
Nexra,
OpenaiChat,
PerplexityLabs,
Pi,
Pizzagpt,
Reka,
Replicate,
ReplicateHome,
TeachAnything,
Upstage,
You,
)
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@dataclass(unsafe_hash=True)
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class Model:
"""
Represents a machine learning model configuration.
Attributes:
name (str): Name of the model.
base_provider (str): Default provider for the model.
best_provider (ProviderType): The preferred provider for the model, typically with retry logic.
"""
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name: str
base_provider: str
best_provider: ProviderType = None
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@staticmethod
def __all__() -> list[str]:
"""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 = "",
base_provider = "",
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best_provider = IterListProvider([
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DDG,
FreeChatgpt,
HuggingChat,
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Pizzagpt,
ReplicateHome,
Upstage,
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Blackbox,
Bixin123,
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Binjie,
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Free2GPT,
MagickPen,
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DeepInfraChat,
LiteIcoding,
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])
)
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############
### Text ###
############
### OpenAI ###
# gpt-3
gpt_3 = Model(
name = 'gpt-3',
base_provider = 'OpenAI',
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best_provider = Nexra
)
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# gpt-3.5
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gpt_35_turbo = Model(
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name = 'gpt-3.5-turbo',
base_provider = 'OpenAI',
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best_provider = IterListProvider([
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Allyfy, Nexra, Bixin123, Airforce,
])
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)
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# gpt-4
gpt_4o = Model(
name = 'gpt-4o',
base_provider = 'OpenAI',
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best_provider = IterListProvider([
Liaobots, Nexra, Airforce, Chatgpt4o, ChatGptEs,
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OpenaiChat
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])
)
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gpt_4o_mini = Model(
name = 'gpt-4o-mini',
base_provider = 'OpenAI',
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best_provider = IterListProvider([
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DDG, ChatGptEs, You, FreeNetfly, Pizzagpt, LiteIcoding, MagickPen, Liaobots, Airforce, ChatgptFree, Koala,
OpenaiChat, ChatGpt
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])
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)
gpt_4_turbo = Model(
name = 'gpt-4-turbo',
base_provider = 'OpenAI',
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best_provider = IterListProvider([
Nexra, Bixin123, Liaobots, Airforce, Bing
])
)
gpt_4 = Model(
name = 'gpt-4',
base_provider = 'OpenAI',
best_provider = IterListProvider([
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Nexra, Binjie, Airforce,
gpt_4_turbo.best_provider, gpt_4o.best_provider, gpt_4o_mini.best_provider,
Chatgpt4Online, Bing, OpenaiChat,
])
)
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### GigaChat ###
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gigachat = Model(
name = 'GigaChat:latest',
base_provider = 'gigachat',
best_provider = GigaChat
)
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### Meta ###
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meta = Model(
name = "meta-ai",
base_provider = "Meta",
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best_provider = MetaAI
)
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# llama 2
llama_2_13b = Model(
name = "llama-2-13b",
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base_provider = "Meta Llama",
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best_provider = Airforce
)
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# llama 3
llama_3_8b = Model(
name = "llama-3-8b",
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base_provider = "Meta Llama",
best_provider = IterListProvider([Airforce, DeepInfra, Replicate])
)
llama_3_70b = Model(
name = "llama-3-70b",
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base_provider = "Meta Llama",
best_provider = IterListProvider([ReplicateHome, Airforce, DeepInfra, Replicate])
)
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llama_3 = Model(
name = "llama-3",
base_provider = "Meta Llama",
best_provider = IterListProvider([llama_3_8b.best_provider, llama_3_70b.best_provider])
)
# llama 3.1
llama_3_1_8b = Model(
name = "llama-3.1-8b",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([Blackbox, DeepInfraChat, ChatHub, Airforce, PerplexityLabs])
)
llama_3_1_70b = Model(
name = "llama-3.1-70b",
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base_provider = "Meta Llama",
best_provider = IterListProvider([DDG, HuggingChat, Blackbox, FreeGpt, TeachAnything, Free2GPT, DeepInfraChat, Airforce, HuggingFace, PerplexityLabs])
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)
llama_3_1_405b = Model(
name = "llama-3.1-405b",
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base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, DeepInfraChat, Airforce])
)
llama_3_1 = Model(
name = "llama-3.1",
base_provider = "Meta Llama",
best_provider = IterListProvider([Nexra, llama_3_1_8b.best_provider, llama_3_1_70b.best_provider, llama_3_1_405b.best_provider,])
)
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### Mistral ###
mistral_7b = Model(
name = "mistral-7b",
base_provider = "Mistral",
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best_provider = IterListProvider([HuggingChat, DeepInfraChat, Airforce, HuggingFace, DeepInfra])
)
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mixtral_8x7b = Model(
name = "mixtral-8x7b",
base_provider = "Mistral",
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best_provider = IterListProvider([DDG, ReplicateHome, DeepInfraChat, ChatHub, Airforce, DeepInfra])
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)
mixtral_8x22b = Model(
name = "mixtral-8x22b",
base_provider = "Mistral",
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best_provider = IterListProvider([DeepInfraChat, Airforce])
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)
mistral_nemo = Model(
name = "mistral-nemo",
base_provider = "Mistral",
best_provider = IterListProvider([HuggingChat, HuggingFace])
)
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### NousResearch ###
mixtral_8x7b_dpo = Model(
name = "mixtral-8x7b-dpo",
base_provider = "NousResearch",
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best_provider = Airforce
)
hermes_3 = Model(
name = "hermes-3",
base_provider = "NousResearch",
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best_provider = IterListProvider([HuggingChat, HuggingFace])
)
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### Microsoft ###
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phi_3_medium_4k = Model(
name = "phi-3-medium-4k",
base_provider = "Microsoft",
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best_provider = DeepInfraChat
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)
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phi_3_5_mini = Model(
name = "phi-3.5-mini",
base_provider = "Microsoft",
best_provider = IterListProvider([HuggingChat, HuggingFace])
)
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### Google DeepMind ###
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# gemini
gemini_pro = Model(
name = 'gemini-pro',
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base_provider = 'Google DeepMind',
best_provider = IterListProvider([GeminiPro, LiteIcoding, Blackbox, AIChatFree, GPROChat, Nexra, Liaobots, Airforce])
)
gemini_flash = Model(
name = 'gemini-flash',
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base_provider = 'Google DeepMind',
best_provider = IterListProvider([Blackbox, Liaobots, Airforce])
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)
gemini = Model(
name = 'gemini',
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base_provider = 'Google DeepMind',
best_provider = IterListProvider([Gemini, gemini_flash.best_provider, gemini_pro.best_provider])
)
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# gemma
gemma_2b_9b = Model(
name = 'gemma-2b-9b',
base_provider = 'Google',
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best_provider = Airforce
)
gemma_2b_27b = Model(
name = 'gemma-2b-27b',
base_provider = 'Google',
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best_provider = IterListProvider([DeepInfraChat, Airforce])
)
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gemma_2b = Model(
name = 'gemma-2b',
base_provider = 'Google',
best_provider = IterListProvider([
ReplicateHome, Airforce,
gemma_2b_9b.best_provider, gemma_2b_27b.best_provider,
])
)
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gemma_2 = Model(
name = 'gemma-2',
base_provider = 'Google',
best_provider = ChatHub
)
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### Anthropic ###
claude_2 = Model(
name = 'claude-2',
base_provider = 'Anthropic',
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best_provider = You
)
claude_2_0 = Model(
name = 'claude-2.0',
base_provider = 'Anthropic',
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best_provider = Liaobots
)
claude_2_1 = Model(
name = 'claude-2.1',
base_provider = 'Anthropic',
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best_provider = Liaobots
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)
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# claude 3
claude_3_opus = Model(
name = 'claude-3-opus',
base_provider = 'Anthropic',
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best_provider = Liaobots
)
claude_3_sonnet = Model(
name = 'claude-3-sonnet',
base_provider = 'Anthropic',
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best_provider = Liaobots
)
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claude_3_haiku = Model(
name = 'claude-3-haiku',
base_provider = 'Anthropic',
best_provider = IterListProvider([DDG, Liaobots])
)
claude_3 = Model(
name = 'claude-3',
base_provider = 'Anthropic',
best_provider = IterListProvider([
claude_3_opus.best_provider, claude_3_sonnet.best_provider, claude_3_haiku.best_provider
])
)
# claude 3.5
claude_3_5_sonnet = Model(
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name = 'claude-3.5-sonnet',
base_provider = 'Anthropic',
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best_provider = IterListProvider([Blackbox, Liaobots])
)
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claude_3_5 = Model(
name = 'claude-3.5',
base_provider = 'Anthropic',
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best_provider = IterListProvider([
LiteIcoding,
claude_3_5_sonnet.best_provider
])
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)
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### Reka AI ###
reka_core = Model(
name = 'reka-core',
base_provider = 'Reka AI',
best_provider = Reka
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)
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### Blackbox AI ###
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blackbox = Model(
name = 'blackbox',
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base_provider = 'Blackbox AI',
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best_provider = Blackbox
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)
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### Databricks ###
dbrx_instruct = Model(
name = 'dbrx-instruct',
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base_provider = 'Databricks',
best_provider = IterListProvider([Airforce, DeepInfra])
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)
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### CohereForAI ###
command_r_plus = Model(
name = 'command-r-plus',
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base_provider = 'CohereForAI',
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best_provider = HuggingChat
)
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### iFlytek ###
sparkdesk_v1_1 = Model(
name = 'sparkdesk-v1.1',
base_provider = 'iFlytek',
best_provider = IterListProvider([FreeChatgpt, Airforce])
)
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### Qwen ###
qwen_1_5_14b = Model(
name = 'qwen-1.5-14b',
base_provider = 'Qwen',
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best_provider = FreeChatgpt
)
qwen_1_5_72b = Model(
name = 'qwen-1.5-72b',
base_provider = 'Qwen',
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best_provider = Airforce
)
qwen_1_5_110b = Model(
name = 'qwen-1.5-110b',
base_provider = 'Qwen',
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best_provider = Airforce
)
qwen_2_72b = Model(
name = 'qwen-2-72b',
base_provider = 'Qwen',
best_provider = IterListProvider([DeepInfraChat, HuggingChat, Airforce, HuggingFace])
)
qwen_turbo = Model(
name = 'qwen-turbo',
base_provider = 'Qwen',
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best_provider = Bixin123
)
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qwen = Model(
name = 'qwen',
base_provider = 'Qwen',
best_provider = IterListProvider([Nexra, qwen_1_5_14b.best_provider, qwen_1_5_72b.best_provider, qwen_1_5_110b.best_provider, qwen_2_72b.best_provider, qwen_turbo.best_provider])
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)
### Zhipu AI ###
glm_3_6b = Model(
name = 'glm-3-6b',
base_provider = 'Zhipu AI',
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best_provider = FreeChatgpt
)
glm_4_9b = Model(
name = 'glm-4-9B',
base_provider = 'Zhipu AI',
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best_provider = FreeChatgpt
)
glm_4 = Model(
name = 'glm-4',
base_provider = 'Zhipu AI',
best_provider = IterListProvider([
glm_3_6b.best_provider, glm_4_9b.best_provider
])
)
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### 01-ai ###
yi_1_5_9b = Model(
name = 'yi-1.5-9b',
base_provider = '01-ai',
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best_provider = FreeChatgpt
)
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yi_34b = Model(
name = 'yi-34b',
base_provider = '01-ai',
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best_provider = Airforce
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)
### Upstage ###
solar_1_mini = Model(
name = 'solar-1-mini',
base_provider = 'Upstage',
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best_provider = Upstage
)
solar_10_7b = Model(
name = 'solar-10-7b',
base_provider = 'Upstage',
best_provider = Airforce
)
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solar_pro = Model(
name = 'solar-pro',
base_provider = 'Upstage',
best_provider = Upstage
)
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### Inflection ###
pi = Model(
name = 'pi',
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base_provider = 'Inflection',
best_provider = Pi
)
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### DeepSeek ###
deepseek = Model(
name = 'deepseek',
base_provider = 'DeepSeek',
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best_provider = Airforce
)
### WizardLM ###
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wizardlm_2_7b = Model(
name = 'wizardlm-2-7b',
base_provider = 'WizardLM',
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best_provider = DeepInfraChat
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)
wizardlm_2_8x22b = Model(
name = 'wizardlm-2-8x22b',
base_provider = 'WizardLM',
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best_provider = IterListProvider([DeepInfraChat, Airforce])
)
### Together ###
sh_n_7b = Model(
name = 'sh-n-7b',
base_provider = 'Together',
best_provider = Airforce
)
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### Yorickvp ###
llava_13b = Model(
name = 'llava-13b',
base_provider = 'Yorickvp',
best_provider = ReplicateHome
)
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### OpenBMB ###
minicpm_llama_3_v2_5 = Model(
name = 'minicpm-llama-3-v2.5',
base_provider = 'OpenBMB',
best_provider = DeepInfraChat
)
### Lzlv ###
lzlv_70b = Model(
name = 'lzlv-70b',
base_provider = 'Lzlv',
best_provider = DeepInfraChat
)
### OpenChat ###
openchat_3_6_8b = Model(
name = 'openchat-3.6-8b',
base_provider = 'OpenChat',
best_provider = DeepInfraChat
)
### Phind ###
phind_codellama_34b_v2 = Model(
name = 'phind-codellama-34b-v2',
base_provider = 'Phind',
best_provider = DeepInfraChat
)
### Cognitive Computations ###
dolphin_2_9_1_llama_3_70b = Model(
name = 'dolphin-2.9.1-llama-3-70b',
base_provider = 'Cognitive Computations',
best_provider = DeepInfraChat
)
### x.ai ###
grok_2 = Model(
name = 'grok-2',
base_provider = 'x.ai',
best_provider = Liaobots
)
grok_2_mini = Model(
name = 'grok-2-mini',
base_provider = 'x.ai',
best_provider = Liaobots
)
# Perplexity AI
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sonar_online = Model(
name = 'sonar-online',
base_provider = 'Perplexity AI',
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best_provider = IterListProvider([ChatHub, PerplexityLabs])
)
sonar_chat = Model(
name = 'sonar-chat',
base_provider = 'Perplexity AI',
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best_provider = PerplexityLabs
)
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#############
### Image ###
#############
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### Stability AI ###
sdxl = Model(
name = 'sdxl',
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base_provider = 'Stability AI',
best_provider = IterListProvider([ReplicateHome, Nexra, DeepInfraImage])
)
sd_3 = Model(
name = 'sd-3',
base_provider = 'Stability AI',
best_provider = IterListProvider([ReplicateHome])
)
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### Playground ###
playground_v2_5 = Model(
name = 'playground-v2.5',
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base_provider = 'Playground AI',
best_provider = IterListProvider([ReplicateHome])
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)
### Flux AI ###
flux = Model(
name = 'flux',
base_provider = 'Flux AI',
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best_provider = IterListProvider([Airforce, Blackbox])
)
flux_realism = Model(
name = 'flux-realism',
base_provider = 'Flux AI',
best_provider = IterListProvider([Airforce])
)
flux_anime = Model(
name = 'flux-anime',
base_provider = 'Flux AI',
best_provider = IterListProvider([Airforce])
)
flux_3d = Model(
name = 'flux-3d',
base_provider = 'Flux AI',
best_provider = IterListProvider([Airforce])
)
flux_disney = Model(
name = 'flux-disney',
base_provider = 'Flux AI',
best_provider = IterListProvider([Airforce])
)
flux_pixel = Model(
name = 'flux-pixel',
base_provider = 'Flux AI',
best_provider = IterListProvider([Airforce])
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)
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flux_4o = Model(
name = 'flux-4o',
base_provider = 'Flux AI',
best_provider = IterListProvider([Airforce])
)
flux_schnell = Model(
name = 'flux-schnell',
base_provider = 'Flux AI',
best_provider = IterListProvider([ReplicateHome])
)
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### ###
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dalle_2 = Model(
name = 'dalle-2',
base_provider = '',
best_provider = IterListProvider([Nexra])
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)
dalle_3 = Model(
name = 'dalle-3',
base_provider = '',
best_provider = IterListProvider([Airforce])
)
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dalle = Model(
name = 'dalle',
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base_provider = '',
best_provider = IterListProvider([Nexra, dalle_2.best_provider, dalle_3.best_provider])
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)
dalle_mini = Model(
name = 'dalle-mini',
base_provider = '',
best_provider = IterListProvider([Nexra])
)
### Other ###
emi = Model(
name = 'emi',
base_provider = '',
best_provider = IterListProvider([Nexra])
)
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any_dark = Model(
name = 'any-dark',
base_provider = '',
best_provider = IterListProvider([Airforce])
)
prodia = Model(
name = 'prodia',
base_provider = '',
best_provider = IterListProvider([Nexra])
)
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class ModelUtils:
"""
Utility class for mapping string identifiers to Model instances.
Attributes:
convert (dict[str, Model]): Dictionary mapping model string identifiers to Model instances.
"""
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convert: dict[str, Model] = {
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############
### Text ###
############
### OpenAI ###
# gpt-3
'gpt-3': gpt_3,
# gpt-3.5
'gpt-3.5-turbo': gpt_35_turbo,
# gpt-4
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'gpt-4o': gpt_4o,
'gpt-4o-mini': gpt_4o_mini,
'gpt-4': gpt_4,
'gpt-4-turbo': gpt_4_turbo,
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### Meta ###
"meta-ai": meta,
# llama-2
'llama-2-13b': llama_2_13b,
# llama-3
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'llama-3': llama_3,
'llama-3-8b': llama_3_8b,
'llama-3-70b': llama_3_70b,
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# llama-3.1
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'llama-3.1': llama_3_1,
'llama-3.1-8b': llama_3_1_8b,
'llama-3.1-70b': llama_3_1_70b,
'llama-3.1-405b': llama_3_1_405b,
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### Mistral ###
'mistral-7b': mistral_7b,
'mixtral-8x7b': mixtral_8x7b,
'mixtral-8x22b': mixtral_8x22b,
'mistral-nemo': mistral_nemo,
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### NousResearch ###
'mixtral-8x7b-dpo': mixtral_8x7b_dpo,
'hermes-3': hermes_3,
'yi-34b': yi_34b,
### Microsoft ###
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'phi_3_medium-4k': phi_3_medium_4k,
'phi-3.5-mini': phi_3_5_mini,
### Google ###
# gemini
'gemini': gemini,
'gemini-pro': gemini_pro,
'gemini-flash': gemini_flash,
# gemma
'gemma-2b': gemma_2b,
'gemma-2b-9b': gemma_2b_9b,
'gemma-2b-27b': gemma_2b_27b,
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'gemma-2': gemma_2,
### Anthropic ###
'claude-2': claude_2,
'claude-2.0': claude_2_0,
'claude-2.1': claude_2_1,
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# claude 3
'claude-3': claude_3,
'claude-3-opus': claude_3_opus,
'claude-3-sonnet': claude_3_sonnet,
'claude-3-haiku': claude_3_haiku,
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# claude 3.5
'claude-3.5': claude_3_5,
'claude-3.5-sonnet': claude_3_5_sonnet,
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### Reka AI ###
'reka-core': reka_core,
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### Blackbox AI ###
'blackbox': blackbox,
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### CohereForAI ###
'command-r+': command_r_plus,
### Databricks ###
'dbrx-instruct': dbrx_instruct,
### GigaChat ###
'gigachat': gigachat,
### iFlytek ###
'sparkdesk-v1.1': sparkdesk_v1_1,
### Qwen ###
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'qwen': qwen,
'qwen-1.5-14b': qwen_1_5_14b,
'qwen-1.5-72b': qwen_1_5_72b,
'qwen-1.5-110b': qwen_1_5_110b,
'qwen-2-72b': qwen_2_72b,
'qwen-turbo': qwen_turbo,
### Zhipu AI ###
'glm-3-6b': glm_3_6b,
'glm-4-9b': glm_4_9b,
'glm-4': glm_4,
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### 01-ai ###
'yi-1.5-9b': yi_1_5_9b,
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### Upstage ###
'solar-1-mini': solar_1_mini,
'solar-10-7b': solar_10_7b,
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'solar-pro': solar_pro,
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### Inflection ###
'pi': pi,
### DeepSeek ###
'deepseek': deepseek,
### Together ###
'sh-n-7b': sh_n_7b,
### Yorickvp ###
'llava-13b': llava_13b,
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### WizardLM ###
'wizardlm-2-7b': wizardlm_2_7b,
'wizardlm-2-8x22b': wizardlm_2_8x22b,
### OpenBMB ###
'minicpm-llama-3-v2.5': minicpm_llama_3_v2_5,
### Lzlv ###
'lzlv-70b': lzlv_70b,
### OpenChat ###
'openchat-3.6-8b': openchat_3_6_8b,
### Phind ###
'phind-codellama-34b-v2': phind_codellama_34b_v2,
### Cognitive Computations ###
'dolphin-2.9.1-llama-3-70b': dolphin_2_9_1_llama_3_70b,
### x.ai ###
'grok-2': grok_2,
'grok-2-mini': grok_2_mini,
### Perplexity AI ###
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'sonar-online': sonar_online,
'sonar-chat': sonar_chat,
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#############
### Image ###
#############
### Stability AI ###
'sdxl': sdxl,
'sd-3': sd_3,
### Playground ###
'playground-v2.5': playground_v2_5,
### Flux AI ###
'flux': flux,
'flux-realism': flux_realism,
'flux-anime': flux_anime,
'flux-3d': flux_3d,
'flux-disney': flux_disney,
'flux-pixel': flux_pixel,
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'flux-4o': flux_4o,
'flux-schnell': flux_schnell,
### ###
'dalle': dalle,
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'dalle-2': dalle_2,
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'dalle-3': dalle_3,
'dalle-mini': dalle_mini,
'emi': emi,
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'any-dark': any_dark,
'prodia': prodia,
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}
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_all_models = list(ModelUtils.convert.keys())