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gpt4free/g4f/models.py

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from __future__ import annotations
from dataclasses import dataclass
from .Provider import IterListProvider, ProviderType
from .Provider import (
Airforce,
Allyfy,
Bing,
Binjie,
Bixin123,
Blackbox,
ChatGpt,
Chatgpt4o,
Chatgpt4Online,
ChatGptEs,
ChatgptFree,
DDG,
DeepInfra,
DeepInfraChat,
DeepInfraImage,
Free2GPT,
FreeChatgpt,
FreeGpt,
FreeNetfly,
Gemini,
GeminiPro,
GigaChat,
HuggingChat,
HuggingFace,
Koala,
Liaobots,
LiteIcoding,
MagickPen,
MetaAI,
Nexra,
OpenaiChat,
PerplexityLabs,
Pi,
Pizzagpt,
Reka,
Replicate,
ReplicateHome,
TeachAnything,
Upstage,
You,
)
@dataclass(unsafe_hash=True)
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.
"""
name: str
base_provider: str
best_provider: ProviderType = None
@staticmethod
def __all__() -> list[str]:
"""Returns a list of all model names."""
return _all_models
default = Model(
name = "",
base_provider = "",
best_provider = IterListProvider([
DDG,
FreeChatgpt,
HuggingChat,
Pizzagpt,
ReplicateHome,
Upstage,
Blackbox,
Bixin123,
Binjie,
Free2GPT,
MagickPen,
DeepInfraChat,
LiteIcoding,
])
)
############
### Text ###
############
### OpenAI ###
# gpt-3
gpt_3 = Model(
name = 'gpt-3',
base_provider = 'OpenAI',
best_provider = IterListProvider([
Nexra,
])
)
# gpt-3.5
gpt_35_turbo = Model(
name = 'gpt-3.5-turbo',
base_provider = 'OpenAI',
best_provider = IterListProvider([
Allyfy, Nexra, Bixin123, Airforce,
])
)
# gpt-4
gpt_4o = Model(
name = 'gpt-4o',
base_provider = 'OpenAI',
best_provider = IterListProvider([
Liaobots, Airforce, Chatgpt4o, ChatGptEs,
OpenaiChat
])
)
gpt_4o_mini = Model(
name = 'gpt-4o-mini',
base_provider = 'OpenAI',
best_provider = IterListProvider([
DDG, ChatGptEs, You, FreeNetfly, Pizzagpt, LiteIcoding, MagickPen, Liaobots, Airforce, ChatgptFree, Koala,
OpenaiChat, ChatGpt
])
)
gpt_4_turbo = Model(
name = 'gpt-4-turbo',
base_provider = 'OpenAI',
best_provider = IterListProvider([
Nexra, Bixin123, Liaobots, Airforce, Bing
])
)
gpt_4 = Model(
name = 'gpt-4',
base_provider = 'OpenAI',
best_provider = IterListProvider([
Nexra, Binjie, Airforce,
gpt_4_turbo.best_provider, gpt_4o.best_provider, gpt_4o_mini.best_provider,
Chatgpt4Online, Bing, OpenaiChat,
])
)
# o1
o1 = Model(
name = 'o1',
base_provider = 'OpenAI',
best_provider = IterListProvider([
Liaobots
])
)
o1_mini = Model(
name = 'o1-mini',
base_provider = 'OpenAI',
best_provider = IterListProvider([
Liaobots
])
)
### GigaChat ###
gigachat = Model(
name = 'GigaChat:latest',
base_provider = 'gigachat',
best_provider = GigaChat
)
### Meta ###
meta = Model(
name = "meta-ai",
base_provider = "Meta",
best_provider = MetaAI
)
# llama 2
llama_2_13b = Model(
name = "llama-2-13b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Airforce])
)
# llama 3
llama_3_8b = Model(
name = "llama-3-8b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Airforce, DeepInfra, Replicate])
)
llama_3_70b = Model(
name = "llama-3-70b",
base_provider = "Meta Llama",
best_provider = IterListProvider([ReplicateHome, Airforce, DeepInfra, Replicate])
)
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",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, DeepInfraChat, Airforce, PerplexityLabs])
)
llama_3_1_70b = Model(
name = "llama-3.1-70b",
base_provider = "Meta Llama",
best_provider = IterListProvider([DDG, HuggingChat, Blackbox, FreeGpt, TeachAnything, Free2GPT, DeepInfraChat, Airforce, HuggingFace, PerplexityLabs])
)
llama_3_1_405b = Model(
name = "llama-3.1-405b",
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([llama_3_1_8b.best_provider, llama_3_1_70b.best_provider, llama_3_1_405b.best_provider,])
)
### Mistral ###
mistral_7b = Model(
name = "mistral-7b",
base_provider = "Mistral",
best_provider = IterListProvider([HuggingChat, DeepInfraChat, Airforce, HuggingFace, DeepInfra])
)
mixtral_8x7b = Model(
name = "mixtral-8x7b",
base_provider = "Mistral",
best_provider = IterListProvider([HuggingChat, DDG, ReplicateHome, DeepInfraChat, Airforce, DeepInfra, HuggingFace])
)
mixtral_8x22b = Model(
name = "mixtral-8x22b",
base_provider = "Mistral",
best_provider = IterListProvider([DeepInfraChat, Airforce])
)
### NousResearch ###
mixtral_8x7b_dpo = Model(
name = "mixtral-8x7b-dpo",
base_provider = "NousResearch",
best_provider = IterListProvider([HuggingChat, Airforce, HuggingFace])
)
### Microsoft ###
phi_3_mini_4k = Model(
name = "phi-3-mini-4k",
base_provider = "Microsoft",
best_provider = IterListProvider([HuggingChat, HuggingFace])
)
phi_3_medium_4k = Model(
name = "phi-3-medium-4k",
base_provider = "Microsoft",
best_provider = IterListProvider([DeepInfraChat])
)
### Google DeepMind ###
# gemini
gemini_pro = Model(
name = 'gemini-pro',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([GeminiPro, LiteIcoding, Blackbox, Liaobots, Airforce])
)
gemini_flash = Model(
name = 'gemini-flash',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Blackbox, Liaobots, Airforce])
)
gemini = Model(
name = 'gemini',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([
Gemini,
gemini_flash.best_provider, gemini_pro.best_provider
])
)
# gemma
gemma_2b_9b = Model(
name = 'gemma-2b-9b',
base_provider = 'Google',
best_provider = IterListProvider([Airforce])
)
gemma_2b_27b = Model(
name = 'gemma-2b-27b',
base_provider = 'Google',
best_provider = IterListProvider([DeepInfraChat, Airforce])
)
gemma_2b = Model(
name = 'gemma-2b',
base_provider = 'Google',
best_provider = IterListProvider([
ReplicateHome, Airforce,
gemma_2b_9b.best_provider, gemma_2b_27b.best_provider,
])
)
### Anthropic ###
claude_2 = Model(
name = 'claude-2',
base_provider = 'Anthropic',
best_provider = IterListProvider([You])
)
claude_2_0 = Model(
name = 'claude-2.0',
base_provider = 'Anthropic',
best_provider = IterListProvider([Liaobots])
)
claude_2_1 = Model(
name = 'claude-2.1',
base_provider = 'Anthropic',
best_provider = IterListProvider([Liaobots])
)
# claude 3
claude_3_opus = Model(
name = 'claude-3-opus',
base_provider = 'Anthropic',
best_provider = IterListProvider([Liaobots])
)
claude_3_sonnet = Model(
name = 'claude-3-sonnet',
base_provider = 'Anthropic',
best_provider = IterListProvider([Liaobots])
)
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(
name = 'claude-3.5-sonnet',
base_provider = 'Anthropic',
best_provider = IterListProvider([Blackbox, Liaobots])
)
claude_3_5 = Model(
name = 'claude-3.5',
base_provider = 'Anthropic',
best_provider = IterListProvider([
LiteIcoding,
claude_3_5_sonnet.best_provider
])
)
### Reka AI ###
reka_core = Model(
name = 'reka-core',
base_provider = 'Reka AI',
best_provider = Reka
)
### Blackbox AI ###
blackbox = Model(
name = 'blackbox',
base_provider = 'Blackbox AI',
best_provider = IterListProvider([Blackbox])
)
### Databricks ###
dbrx_instruct = Model(
name = 'dbrx-instruct',
base_provider = 'Databricks',
best_provider = IterListProvider([Airforce, DeepInfra])
)
### CohereForAI ###
command_r_plus = Model(
name = 'command-r-plus',
base_provider = 'CohereForAI',
best_provider = IterListProvider([HuggingChat])
)
### iFlytek ###
sparkdesk_v1_1 = Model(
name = 'sparkdesk-v1.1',
base_provider = 'iFlytek',
best_provider = IterListProvider([FreeChatgpt, Airforce])
)
### Qwen ###
qwen_1_5_14b = Model(
name = 'qwen-1.5-14b',
base_provider = 'Qwen',
best_provider = IterListProvider([FreeChatgpt])
)
qwen_1_5_72b = Model(
name = 'qwen-1.5-72b',
base_provider = 'Qwen',
best_provider = IterListProvider([Airforce])
)
qwen_1_5_110b = Model(
name = 'qwen-1.5-110b',
base_provider = 'Qwen',
best_provider = IterListProvider([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',
best_provider = IterListProvider([Bixin123])
)
qwen = Model(
name = 'qwen',
base_provider = 'Qwen',
best_provider = IterListProvider([
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
])
)
### Zhipu AI ###
glm_3_6b = Model(
name = 'glm-3-6b',
base_provider = 'Zhipu AI',
best_provider = IterListProvider([FreeChatgpt])
)
glm_4_9b = Model(
name = 'glm-4-9B',
base_provider = 'Zhipu AI',
best_provider = IterListProvider([FreeChatgpt])
)
glm_4 = Model(
name = 'glm-4',
base_provider = 'Zhipu AI',
best_provider = IterListProvider([
glm_3_6b.best_provider, glm_4_9b.best_provider
])
)
### 01-ai ###
yi_1_5_9b = Model(
name = 'yi-1.5-9b',
base_provider = '01-ai',
best_provider = IterListProvider([FreeChatgpt])
)
yi_34b = Model(
name = 'yi-34b',
base_provider = '01-ai',
best_provider = IterListProvider([Airforce])
)
### Upstage ###
solar_1_mini = Model(
name = 'solar-1-mini',
base_provider = 'Upstage',
best_provider = IterListProvider([Upstage])
)
solar_10_7b = Model(
name = 'solar-10-7b',
base_provider = 'Upstage',
best_provider = Airforce
)
solar_pro = Model(
name = 'solar-pro',
base_provider = 'Upstage',
best_provider = Upstage
)
### Inflection ###
pi = Model(
name = 'pi',
base_provider = 'Inflection',
best_provider = Pi
)
### DeepSeek ###
deepseek = Model(
name = 'deepseek',
base_provider = 'DeepSeek',
best_provider = IterListProvider([Airforce])
)
### WizardLM ###
wizardlm_2_7b = Model(
name = 'wizardlm-2-7b',
base_provider = 'WizardLM',
best_provider = IterListProvider([DeepInfraChat])
)
wizardlm_2_8x22b = Model(
name = 'wizardlm-2-8x22b',
base_provider = 'WizardLM',
best_provider = IterListProvider([DeepInfraChat, Airforce])
)
### Together ###
sh_n_7b = Model(
name = 'sh-n-7b',
base_provider = 'Together',
best_provider = Airforce
)
### Yorickvp ###
llava_13b = Model(
name = 'llava-13b',
base_provider = 'Yorickvp',
best_provider = ReplicateHome
)
### 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
)
#############
### Image ###
#############
### Stability AI ###
sdxl = Model(
name = 'sdxl',
base_provider = 'Stability AI',
best_provider = IterListProvider([ReplicateHome, DeepInfraImage])
)
sd_3 = Model(
name = 'sd-3',
base_provider = 'Stability AI',
best_provider = IterListProvider([ReplicateHome])
)
### Playground ###
playground_v2_5 = Model(
name = 'playground-v2.5',
base_provider = 'Playground AI',
best_provider = IterListProvider([ReplicateHome])
)
### Flux AI ###
flux = Model(
name = 'flux',
base_provider = 'Flux AI',
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])
)
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])
)
### ###
dalle_2 = Model(
name = 'dalle-2',
base_provider = '',
best_provider = IterListProvider([Nexra])
)
dalle_3 = Model(
name = 'dalle-3',
base_provider = '',
best_provider = IterListProvider([Airforce])
)
dalle = Model(
name = 'dalle',
base_provider = '',
best_provider = IterListProvider([
Nexra,
dalle_2.best_provider, dalle_3.best_provider,
])
)
dalle_mini = Model(
name = 'dalle-mini',
base_provider = '',
best_provider = IterListProvider([Nexra])
)
### ###
emi = Model(
name = 'emi',
base_provider = '',
best_provider = IterListProvider([Nexra])
)
any_dark = Model(
name = 'any-dark',
base_provider = '',
best_provider = IterListProvider([Airforce])
)
class ModelUtils:
"""
Utility class for mapping string identifiers to Model instances.
Attributes:
convert (dict[str, Model]): Dictionary mapping model string identifiers to Model instances.
"""
convert: dict[str, Model] = {
############
### Text ###
############
### OpenAI ###
# gpt-3
'gpt-3': gpt_3,
# gpt-3.5
'gpt-3.5-turbo': gpt_35_turbo,
# gpt-4
'gpt-4o': gpt_4o,
'gpt-4o-mini': gpt_4o_mini,
'gpt-4': gpt_4,
'gpt-4-turbo': gpt_4_turbo,
# o1
'o1': o1,
'o1-mini': o1_mini,
### Meta ###
"meta-ai": meta,
# llama-2
'llama-2-13b': llama_2_13b,
# llama-3
'llama-3': llama_3,
'llama-3-8b': llama_3_8b,
'llama-3-70b': llama_3_70b,
# llama-3.1
'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,
### Mistral ###
'mistral-7b': mistral_7b,
'mixtral-8x7b': mixtral_8x7b,
'mixtral-8x22b': mixtral_8x22b,
### NousResearch ###
'mixtral-8x7b-dpo': mixtral_8x7b_dpo,
'yi-34b': yi_34b,
### Microsoft ###
'phi-3-mini-4k': phi_3_mini_4k,
'phi_3_medium-4k': phi_3_medium_4k,
### 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,
### Anthropic ###
'claude-2': claude_2,
'claude-2.0': claude_2_0,
'claude-2.1': claude_2_1,
# claude 3
'claude-3': claude_3,
'claude-3-opus': claude_3_opus,
'claude-3-sonnet': claude_3_sonnet,
'claude-3-haiku': claude_3_haiku,
# claude 3.5
'claude-3.5': claude_3_5,
'claude-3.5-sonnet': claude_3_5_sonnet,
### Reka AI ###
'reka-core': reka_core,
### Blackbox AI ###
'blackbox': blackbox,
### CohereForAI ###
'command-r+': command_r_plus,
### Databricks ###
'dbrx-instruct': dbrx_instruct,
### GigaChat ###
'gigachat': gigachat,
### iFlytek ###
'sparkdesk-v1.1': sparkdesk_v1_1,
### Qwen ###
'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,
### 01-ai ###
'yi-1.5-9b': yi_1_5_9b,
### Upstage ###
'solar-1-mini': solar_1_mini,
'solar-10-7b': solar_10_7b,
'solar-pro': solar_pro,
### Inflection ###
'pi': pi,
### DeepSeek ###
'deepseek': deepseek,
### Together ###
'sh-n-7b': sh_n_7b,
### Yorickvp ###
'llava-13b': llava_13b,
### 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,
#############
### 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,
'flux-4o': flux_4o,
'flux-schnell': flux_schnell,
### ###
'dalle': dalle,
'dalle-2': dalle_2,
'dalle-3': dalle_3,
'dalle-mini': dalle_mini,
'emi': emi,
'any-dark': any_dark,
}
_all_models = list(ModelUtils.convert.keys())
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