from __future__ import annotations
from dataclasses import dataclass
from .Provider import IterListProvider, ProviderType
from .Provider import (
AI365VIP,
Allyfy,
Bing,
Blackbox,
ChatGot,
Chatgpt4o,
Chatgpt4Online,
ChatgptFree,
DDG,
DeepInfra,
DeepInfraImage,
FreeChatgpt,
FreeGpt,
Gemini,
GeminiPro,
GeminiProChat,
GigaChat,
HuggingChat,
HuggingFace,
Koala,
Liaobots,
Marsyoo,
MetaAI,
OpenaiChat,
PerplexityLabs,
Pi,
Pizzagpt,
Reka,
Replicate,
ReplicateHome,
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([
Bing,
You,
OpenaiChat,
FreeChatgpt,
AI365VIP,
Chatgpt4o,
DDG,
ChatgptFree,
Koala,
Pizzagpt,
])
)
# GPT-3.5 too, but all providers supports long requests and responses
gpt_35_long = Model(
name = 'gpt-3.5-turbo',
base_provider = 'openai',
best_provider = IterListProvider([
FreeGpt,
You,
Koala,
ChatgptFree,
FreeChatgpt,
DDG,
AI365VIP,
Pizzagpt,
Allyfy,
])
)
############
### 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([
FreeGpt,
You,
Koala,
ChatgptFree,
FreeChatgpt,
DDG,
AI365VIP,
Pizzagpt,
Allyfy,
])
)
gpt_35_turbo_16k = Model(
name = 'gpt-3.5-turbo-16k',
base_provider = 'openai',
best_provider = gpt_35_long.best_provider
)
gpt_35_turbo_16k_0613 = Model(
name = 'gpt-3.5-turbo-16k-0613',
base_provider = 'openai',
best_provider = gpt_35_long.best_provider
)
gpt_35_turbo_0613 = Model(
name = 'gpt-3.5-turbo-0613',
base_provider = 'openai',
best_provider = gpt_35_turbo.best_provider
)
# gpt-4
gpt_4 = Model(
name = 'gpt-4',
base_provider = 'openai',
best_provider = IterListProvider([
Bing, Chatgpt4Online
])
)
gpt_4_0613 = Model(
name = 'gpt-4-0613',
base_provider = 'openai',
best_provider = gpt_4.best_provider
)
gpt_4_32k = Model(
name = 'gpt-4-32k',
base_provider = 'openai',
best_provider = gpt_4.best_provider
)
gpt_4_32k_0613 = Model(
name = 'gpt-4-32k-0613',
base_provider = 'openai',
best_provider = gpt_4.best_provider
)
gpt_4_turbo = Model(
name = 'gpt-4-turbo',
base_provider = 'openai',
best_provider = IterListProvider([
Bing, Liaobots
])
)
gpt_4o = Model(
name = 'gpt-4o',
base_provider = 'openai',
best_provider = IterListProvider([
You, Liaobots, Chatgpt4o, AI365VIP, OpenaiChat, Marsyoo
])
)
gpt_4o_mini = Model(
name = 'gpt-4o-mini',
base_provider = 'openai',
best_provider = IterListProvider([
Liaobots, OpenaiChat, You,
])
)
### GigaChat ###
gigachat = Model(
name = 'GigaChat:latest',
base_provider = 'gigachat',
best_provider = GigaChat
)
### Meta ###
meta = Model(
name = "meta",
base_provider = "meta",
best_provider = MetaAI
)
llama_3_8b_instruct = Model(
name = "meta-llama/Meta-Llama-3-8B-Instruct",
base_provider = "meta",
best_provider = IterListProvider([DeepInfra, PerplexityLabs, Replicate])
)
llama_3_70b_instruct = Model(
name = "meta-llama/Meta-Llama-3-70B-Instruct",
base_provider = "meta",
best_provider = IterListProvider([DeepInfra, PerplexityLabs, Replicate])
)
llama3_70b_instruct = Model(
name = "meta/meta-llama-3-70b-instruct",
base_provider = "meta",
best_provider = IterListProvider([ReplicateHome])
)
llama_3_70b_chat_hf = Model(
name = "meta-llama/Llama-3-70b-chat-hf",
base_provider = "meta",
best_provider = IterListProvider([DDG])
)
llama_3_1_70b_Instruct = Model(
name = "meta-llama/Meta-Llama-3.1-70B-Instruct",
base_provider = "meta",
best_provider = IterListProvider([HuggingChat, HuggingFace])
)
llama_3_1_405b_Instruct_FP8 = Model(
name = "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8",
base_provider = "meta",
best_provider = IterListProvider([HuggingChat, HuggingFace])
)
### Mistral ###
mixtral_8x7b = Model(
name = "mistralai/Mixtral-8x7B-Instruct-v0.1",
base_provider = "huggingface",
best_provider = IterListProvider([DeepInfra, HuggingFace, PerplexityLabs, HuggingChat, DDG, ReplicateHome])
)
mistral_7b_v02 = Model(
name = "mistralai/Mistral-7B-Instruct-v0.2",
base_provider = "huggingface",
best_provider = IterListProvider([DeepInfra, HuggingFace, HuggingChat])
)
### NousResearch ###
Nous_Hermes_2_Mixtral_8x7B_DPO = Model(
name = "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
base_provider = "NousResearch",
best_provider = IterListProvider([HuggingFace, HuggingChat])
)
### 01-ai ###
Yi_1_5_34B_Chat = Model(
name = "01-ai/Yi-1.5-34B-Chat",
base_provider = "01-ai",
best_provider = IterListProvider([HuggingFace, HuggingChat])
)
### Microsoft ###
Phi_3_mini_4k_instruct = Model(
name = "microsoft/Phi-3-mini-4k-instruct",
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, You, ChatGot, GeminiProChat, Liaobots])
)
gemini_flash = Model(
name = 'gemini-flash',
base_provider = 'Google',
best_provider = IterListProvider([Liaobots])
)
# gemma
gemma_2b_it = Model(
name = 'gemma-2b-it',
base_provider = 'Google',
best_provider = IterListProvider([ReplicateHome])
)
gemma_2_9b_it = Model(
name = 'gemma-2-9b-it',
base_provider = 'Google',
best_provider = IterListProvider([PerplexityLabs])
)
gemma_2_27b_it = Model(
name = 'gemma-2-27b-it',
base_provider = 'Google',
best_provider = IterListProvider([PerplexityLabs])
)
### 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([You, Liaobots])
)
claude_3_sonnet = Model(
name = 'claude-3-sonnet',
base_provider = 'Anthropic',
best_provider = IterListProvider([You, 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, AI365VIP, Liaobots])
)
### Reka AI ###
reka_core = Model(
name = 'reka-core',
base_provider = 'Reka AI',
best_provider = Reka
)
### NVIDIA ###
nemotron_4_340b_instruct = Model(
name = 'nemotron-4-340b-instruct',
base_provider = 'NVIDIA',
best_provider = IterListProvider([PerplexityLabs])
)
### Blackbox ###
blackbox = Model(
name = 'blackbox',
base_provider = 'Blackbox',
best_provider = Blackbox
)
### Databricks ###
dbrx_instruct = Model(
name = 'databricks/dbrx-instruct',
base_provider = 'Databricks',
best_provider = IterListProvider([DeepInfra])
)
### CohereForAI ###
command_r_plus = Model(
name = 'CohereForAI/c4ai-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])
)
### DeepSeek ###
deepseek_coder = Model(
name = 'deepseek-coder',
base_provider = 'DeepSeek',
best_provider = IterListProvider([FreeChatgpt])
)
deepseek_chat = Model(
name = 'deepseek-chat',
base_provider = 'DeepSeek',
best_provider = IterListProvider([FreeChatgpt])
)
### Qwen ###
Qwen2_7B_Instruct = Model(
name = 'Qwen2-7B-Instruct',
base_provider = 'Qwen',
best_provider = IterListProvider([FreeChatgpt])
)
### Zhipu AI ###
glm4_9B_chat = Model(
name = 'glm4-9B-chat',
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_Chat = Model(
name = 'Yi-1.5-9B-Chat',
base_provider = '01-ai',
best_provider = IterListProvider([FreeChatgpt])
)
### Other ###
pi = Model(
name = 'pi',
base_provider = 'inflection',
best_provider = Pi
)
#############
### Image ###
#############
### Stability AI ###
sdxl = Model(
name = 'stability-ai/sdxl',
base_provider = 'Stability AI',
best_provider = IterListProvider([DeepInfraImage])
)
stable_diffusion_3 = Model(
name = 'stability-ai/stable-diffusion-3',
base_provider = 'Stability AI',
best_provider = IterListProvider([ReplicateHome])
)
sdxl_lightning_4step = Model(
name = 'bytedance/sdxl-lightning-4step',
base_provider = 'Stability AI',
best_provider = IterListProvider([ReplicateHome])
)
playground_v2_5_1024px_aesthetic = Model(
name = 'playgroundai/playground-v2.5-1024px-aesthetic',
base_provider = 'Stability AI',
best_provider = IterListProvider([ReplicateHome])
)
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-4 ###
# gpt-3.5
'gpt-3.5-turbo': gpt_35_turbo,
'gpt-3.5-long': gpt_35_long,
# 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-3-8b': llama_3_8b_instruct,
'llama-3-70b': llama_3_70b_instruct,
'llama-3-70b-chat': llama_3_70b_chat_hf,
'llama-3-70b-instruct': llama3_70b_instruct,
'llama-3.1-70b': llama_3_1_70b_Instruct,
'llama-3.1-405b': llama_3_1_405b_Instruct_FP8,
### Mistral (Opensource) ###
'mixtral-8x7b': mixtral_8x7b,
'mistral-7b-v02': mistral_7b_v02,
### NousResearch ###
'Nous-Hermes-2-Mixtral-8x7B-DPO': Nous_Hermes_2_Mixtral_8x7B_DPO,
### 01-ai ###
'Yi-1.5-34b': Yi_1_5_34B_Chat,
### Microsoft ###
'Phi-3-mini-4k': Phi_3_mini_4k_instruct,
### Google ###
# gemini
'gemini': gemini,
'gemini-pro': gemini_pro,
'gemini-flash': gemini_flash,
# gemma
'gemma-2b': gemma_2b_it,
'gemma-2-9b': gemma_2_9b_it,
'gemma-2-27b': gemma_2_27b_it,
### 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': reka_core,
### NVIDIA ###
'nemotron-4-340b': nemotron_4_340b_instruct,
### Blackbox ###
'blackbox': blackbox,
### CohereForAI ###
'command-r+': command_r_plus,
### Databricks ###
'dbrx-instruct': dbrx_instruct,
### GigaChat ###
'gigachat': gigachat,
### iFlytek ###
'SparkDesk-v1.1': SparkDesk_v1_1,
### DeepSeek ###
'deepseek-coder': deepseek_coder,
'deepseek-chat': deepseek_chat,
### Qwen ###
'Qwen2-7b': Qwen2_7B_Instruct,
### Zhipu AI ###
'glm4-9b': glm4_9B_chat,
'chatglm3-6b': chatglm3_6B,
### 01-ai ###
'Yi-1.5-9b': Yi_1_5_9B_Chat,
# Other
'pi': pi,
#############
### Image ###
#############
### Stability AI ###
'sdxl': sdxl,
'stable-diffusion-3': stable_diffusion_3,
### ByteDance ###
'sdxl-lightning': sdxl_lightning_4step,
### Playground ###
'playground-v2.5': playground_v2_5_1024px_aesthetic,
}
_all_models = list(ModelUtils.convert.keys())
