from __future__ import annotations
from dataclasses import dataclass
from .Provider import IterListProvider, ProviderType
from .Provider import (
Allyfy,
Bing,
Blackbox,
ChatGot,
Chatgpt4o,
Chatgpt4Online,
ChatgptFree,
DDG,
DeepInfra,
DeepInfraImage,
FluxAirforce,
FreeChatgpt,
FreeGpt,
FreeNetfly,
Gemini,
GeminiPro,
GigaChat,
HuggingChat,
HuggingFace,
Koala,
Liaobots,
MagickPenAsk,
MagickPenChat,
MetaAI,
OpenaiChat,
PerplexityLabs,
Pi,
Pizzagpt,
Reka,
Replicate,
ReplicateHome,
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([
ChatGot,
Chatgpt4Online,
DDG,
FreeChatgpt,
FreeNetfly,
Gemini,
HuggingChat,
MagickPenAsk,
MagickPenChat,
Pizzagpt,
ChatgptFree,
ReplicateHome,
Upstage,
])
)
############
### Text ###
############
### OpenAI ###
### GPT-3.5 / GPT-4 ###
# gpt-3.5
gpt_35_turbo = Model(
name = 'gpt-3.5-turbo',
base_provider = 'openai',
best_provider = IterListProvider([
Allyfy,
])
)
# gpt-4
gpt_4o = Model(
name = 'gpt-4o',
base_provider = 'openai',
best_provider = IterListProvider([
Liaobots, Chatgpt4o, OpenaiChat,
])
)
gpt_4o_mini = Model(
name = 'gpt-4o-mini',
base_provider = 'openai',
best_provider = IterListProvider([
DDG, Liaobots, You, FreeNetfly, MagickPenAsk, MagickPenChat, Pizzagpt, ChatgptFree, OpenaiChat, Koala,
])
)
gpt_4_turbo = Model(
name = 'gpt-4-turbo',
base_provider = 'openai',
best_provider = IterListProvider([
Liaobots, Bing
])
)
gpt_4 = Model(
name = 'gpt-4',
base_provider = 'openai',
best_provider = IterListProvider([
Chatgpt4Online, Bing,
gpt_4_turbo.best_provider, gpt_4o.best_provider, gpt_4o_mini.best_provider
])
)
### 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_3_8b = Model(
name = "llama-3-8b",
base_provider = "meta",
best_provider = IterListProvider([DeepInfra, Replicate])
)
llama_3_70b = Model(
name = "llama-3-70b",
base_provider = "meta",
best_provider = IterListProvider([ReplicateHome, DeepInfra, PerplexityLabs, Replicate])
)
llama_3_1_8b = Model(
name = "llama-3.1-8b",
base_provider = "meta",
best_provider = IterListProvider([Blackbox])
)
llama_3_1_70b = Model(
name = "llama-3.1-70b",
base_provider = "meta",
best_provider = IterListProvider([DDG, HuggingChat, FreeGpt, Blackbox, HuggingFace])
)
llama_3_1_405b = Model(
name = "llama-3.1-405b",
base_provider = "meta",
best_provider = IterListProvider([HuggingChat, Blackbox, HuggingFace])
)
### Mistral ###
mixtral_8x7b = Model(
name = "mixtral-8x7b",
base_provider = "huggingface",
best_provider = IterListProvider([HuggingChat, DDG, ReplicateHome, DeepInfra, HuggingFace,])
)
mistral_7b = Model(
name = "mistral-7b",
base_provider = "huggingface",
best_provider = IterListProvider([HuggingChat, HuggingFace, DeepInfra])
)
### 01-ai ###
yi_1_5_34b = Model(
name = "yi-1.5-34b",
base_provider = "01-ai",
best_provider = IterListProvider([HuggingChat, HuggingFace])
)
### Microsoft ###
phi_3_mini_4k = Model(
name = "phi-3-mini-4k",
base_provider = "Microsoft",
best_provider = IterListProvider([HuggingFace, HuggingChat])
)
### Google ###
# gemini
gemini = Model(
name = 'gemini',
base_provider = 'Google',
best_provider = Gemini
)
gemini_pro = Model(
name = 'gemini-pro',
base_provider = 'Google',
best_provider = IterListProvider([GeminiPro, ChatGot, Liaobots])
)
gemini_flash = Model(
name = 'gemini-flash',
base_provider = 'Google',
best_provider = IterListProvider([Liaobots, Blackbox])
)
# gemma
gemma_2b = Model(
name = 'gemma-2b',
base_provider = 'Google',
best_provider = IterListProvider([ReplicateHome])
)
### 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_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_5_sonnet = Model(
name = 'claude-3-5-sonnet',
base_provider = 'Anthropic',
best_provider = IterListProvider([Liaobots])
)
claude_3_haiku = Model(
name = 'claude-3-haiku',
base_provider = 'Anthropic',
best_provider = IterListProvider([DDG, Liaobots])
)
### Reka AI ###
reka_core = Model(
name = 'reka-core',
base_provider = 'Reka AI',
best_provider = Reka
)
### Blackbox ###
blackbox = Model(
name = 'blackbox',
base_provider = 'Blackbox',
best_provider = Blackbox
)
### Databricks ###
dbrx_instruct = Model(
name = 'dbrx-instruct',
base_provider = 'Databricks',
best_provider = IterListProvider([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])
)
### Qwen ###
qwen_1_5_14b = Model(
name = 'qwen-1.5-14b',
base_provider = 'Qwen',
best_provider = IterListProvider([FreeChatgpt])
)
### Zhipu AI ###
glm4_9b = Model(
name = 'glm4-9B',
base_provider = 'Zhipu AI',
best_provider = IterListProvider([FreeChatgpt])
)
chatglm3_6b = Model(
name = 'chatglm3-6b',
base_provider = 'Zhipu AI',
best_provider = IterListProvider([FreeChatgpt])
)
### 01-ai ###
yi_1_5_9b = Model(
name = 'yi-1.5-9b',
base_provider = '01-ai',
best_provider = IterListProvider([FreeChatgpt])
)
### Pi ###
solar_1_mini = Model(
name = 'solar-1-mini',
base_provider = 'Upstage',
best_provider = IterListProvider([Upstage])
)
### Pi ###
pi = Model(
name = 'pi',
base_provider = 'inflection',
best_provider = Pi
)
#############
### 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 = 'Stability AI',
best_provider = IterListProvider([ReplicateHome])
)
### Flux AI ###
flux = Model(
name = 'flux',
base_provider = 'Flux AI',
best_provider = IterListProvider([FluxAirforce])
)
flux_realism = Model(
name = 'flux-realism',
base_provider = 'Flux AI',
best_provider = IterListProvider([FluxAirforce])
)
flux_anime = Model(
name = 'flux-anime',
base_provider = 'Flux AI',
best_provider = IterListProvider([FluxAirforce])
)
flux_3d = Model(
name = 'flux-3d',
base_provider = 'Flux AI',
best_provider = IterListProvider([FluxAirforce])
)
flux_disney = Model(
name = 'flux-disney',
base_provider = 'Flux AI',
best_provider = IterListProvider([FluxAirforce])
)
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-3.5
'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,
### Meta ###
"meta-ai": meta,
# llama-3
'llama-3-8b': llama_3_8b,
'llama-3-70b': llama_3_70b,
# 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 ###
'mixtral-8x7b': mixtral_8x7b,
'mistral-7b': mistral_7b,
### 01-ai ###
'yi-1.5-34b': yi_1_5_34b,
### Microsoft ###
'phi-3-mini-4k': phi_3_mini_4k,
### Google ###
# gemini
'gemini': gemini,
'gemini-pro': gemini_pro,
'gemini-flash': gemini_flash,
# gemma
'gemma-2b': gemma_2b,
### Anthropic ###
'claude-2': claude_2,
'claude-2.0': claude_2_0,
'claude-2.1': claude_2_1,
'claude-3-opus': claude_3_opus,
'claude-3-sonnet': claude_3_sonnet,
'claude-3-5-sonnet': claude_3_5_sonnet,
'claude-3-haiku': claude_3_haiku,
### Reka AI ###
'reka-core': reka_core,
### Blackbox ###
'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-1.5-14b': qwen_1_5_14b,
### Zhipu AI ###
'glm4-9b': glm4_9b,
'chatglm3-6b': chatglm3_6b,
### 01-ai ###
'yi-1.5-9b': yi_1_5_9b,
### Upstage ###
'solar-1-mini': solar_1_mini,
### Pi ###
'pi': pi,
#############
### 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,
}
_all_models = list(ModelUtils.convert.keys())
