from __future__ import annotations
from dataclasses import dataclass
from .Provider import IterListProvider, ProviderType
from .Provider import (
AI365VIP,
Bing,
Blackbox,
Chatgpt4o,
ChatgptFree,
DDG,
DeepInfra,
DeepInfraImage,
FreeChatgpt,
FreeGpt,
Gemini,
GeminiPro,
GeminiProChat,
GigaChat,
HuggingChat,
HuggingFace,
Koala,
Liaobots,
MetaAI,
OpenaiChat,
PerplexityLabs,
Pi,
Pizzagpt,
Reka,
Replicate,
ReplicateHome,
Vercel,
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,
OpenaiChat,
Koala,
ChatgptFree,
FreeChatgpt,
DDG,
AI365VIP,
Pizzagpt,
])
)
############
### 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,
OpenaiChat,
ChatgptFree,
FreeChatgpt,
DDG,
AI365VIP,
Pizzagpt,
])
)
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, Liaobots,
])
)
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 = Bing
)
gpt_4o = Model(
name = 'gpt-4o',
base_provider = 'openai',
best_provider = IterListProvider([
You, Liaobots, Chatgpt4o, AI365VIP
])
)
### GigaChat ###
gigachat = Model(
name = 'GigaChat:latest',
base_provider = 'gigachat',
best_provider = GigaChat
)
### Meta ###
meta = Model(
name = "meta",
base_provider = "meta",
best_provider = MetaAI
)
llama_2_70b_chat = Model(
name = "meta/llama-2-70b-chat",
base_provider = "meta",
best_provider = IterListProvider([ReplicateHome])
)
llama3_8b_instruct = Model(
name = "meta-llama/Meta-Llama-3-8B-Instruct",
base_provider = "meta",
best_provider = IterListProvider([DeepInfra, PerplexityLabs, Replicate])
)
llama3_70b_instruct = Model(
name = "meta-llama/Meta-Llama-3-70B-Instruct",
base_provider = "meta",
best_provider = IterListProvider([DeepInfra, PerplexityLabs, Replicate, HuggingChat, DDG])
)
codellama_34b_instruct = Model(
name = "codellama/CodeLlama-34b-Instruct-hf",
base_provider = "meta",
best_provider = HuggingChat
)
codellama_70b_instruct = Model(
name = "codellama/CodeLlama-70b-Instruct-hf",
base_provider = "meta",
best_provider = IterListProvider([DeepInfra])
)
### Mistral ###
mixtral_8x7b = Model(
name = "mistralai/Mixtral-8x7B-Instruct-v0.1",
base_provider = "huggingface",
best_provider = IterListProvider([DeepInfra, HuggingFace, PerplexityLabs, HuggingChat, DDG])
)
mistral_7b_v02 = Model(
name = "mistralai/Mistral-7B-Instruct-v0.2",
base_provider = "huggingface",
best_provider = IterListProvider([DeepInfra, HuggingFace, HuggingChat, ReplicateHome])
)
### 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, GeminiProChat])
)
# gemma
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_v2 = Model(
name = 'claude-v2',
base_provider = 'anthropic',
best_provider = IterListProvider([Vercel])
)
claude_3_opus = Model(
name = 'claude-3-opus',
base_provider = 'anthropic',
best_provider = You
)
claude_3_sonnet = Model(
name = 'claude-3-sonnet',
base_provider = 'anthropic',
best_provider = You
)
claude_3_haiku = Model(
name = 'claude-3-haiku',
base_provider = 'anthropic',
best_provider = IterListProvider([DDG, AI365VIP])
)
### 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])
)
### 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([ReplicateHome, DeepInfraImage])
)
### AI Forever ###
kandinsky_2_2 = Model(
name = 'ai-forever/kandinsky-2.2',
base_provider = 'AI Forever',
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-turbo-0613' : gpt_35_turbo_0613,
'gpt-3.5-turbo-16k' : gpt_35_turbo_16k,
'gpt-3.5-turbo-16k-0613' : gpt_35_turbo_16k_0613,
'gpt-3.5-long': gpt_35_long,
# gpt-4
'gpt-4o' : gpt_4o,
'gpt-4' : gpt_4,
'gpt-4-0613' : gpt_4_0613,
'gpt-4-32k' : gpt_4_32k,
'gpt-4-32k-0613' : gpt_4_32k_0613,
'gpt-4-turbo' : gpt_4_turbo,
### Meta ###
"meta-ai": meta,
'llama-2-70b-chat': llama_2_70b_chat,
'llama3-8b': llama3_8b_instruct, # alias
'llama3-70b': llama3_70b_instruct, # alias
'llama3-8b-instruct' : llama3_8b_instruct,
'llama3-70b-instruct': llama3_70b_instruct,
'codellama-34b-instruct': codellama_34b_instruct,
'codellama-70b-instruct': codellama_70b_instruct,
### 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-Chat': Yi_1_5_34B_Chat,
### Microsoft ###
'Phi-3-mini-4k-instruct': Phi_3_mini_4k_instruct,
### Google ###
# gemini
'gemini': gemini,
'gemini-pro': gemini_pro,
# gemma
'gemma-2-9b-it': gemma_2_9b_it,
'gemma-2-27b-it': gemma_2_27b_it,
### Anthropic ###
'claude-v2': claude_v2,
'claude-3-opus': claude_3_opus,
'claude-3-sonnet': claude_3_sonnet,
'claude-3-haiku': claude_3_haiku,
### Reka AI ###
'reka': reka_core,
### NVIDIA ###
'nemotron-4-340b-instruct': nemotron_4_340b_instruct,
### Blackbox ###
'blackbox': blackbox,
### CohereForAI ###
'command-r+': command_r_plus,
### Databricks ###
'dbrx-instruct': dbrx_instruct,
### GigaChat ###
'gigachat': gigachat,
# Other
'pi': pi,
#############
### Image ###
#############
### Stability AI ###
'sdxl': sdxl,
### AI Forever ###
'kandinsky-2.2': kandinsky_2_2,
}
_all_models = list(ModelUtils.convert.keys())
