gpt4free/g4f/models.py
2024-04-22 20:02:17 +02:00

373 lines
8.7 KiB
Python

from __future__ import annotations
from dataclasses import dataclass
from .Provider import RetryProvider, ProviderType
from .Provider import (
Aichatos,
Bing,
Blackbox,
Chatgpt4Online,
ChatgptAi,
ChatgptNext,
Cohere,
Cnote,
DeepInfra,
Feedough,
FreeGpt,
Gemini,
GeminiProChat,
GigaChat,
HuggingChat,
HuggingFace,
Koala,
Liaobots,
Llama,
OpenaiChat,
PerplexityLabs,
Replicate,
Pi,
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 = RetryProvider([
Bing,
ChatgptAi,
You,
Chatgpt4Online,
OpenaiChat
])
)
# 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 = RetryProvider([
FreeGpt,
You,
ChatgptNext,
OpenaiChat,
])
)
# GPT-3.5 / GPT-4
gpt_35_turbo = Model(
name = 'gpt-3.5-turbo',
base_provider = 'openai',
best_provider = RetryProvider([
FreeGpt,
You,
ChatgptNext,
Koala,
OpenaiChat,
Aichatos,
Cnote,
Feedough,
])
)
gpt_4 = Model(
name = 'gpt-4',
base_provider = 'openai',
best_provider = RetryProvider([
Bing, Liaobots,
])
)
gpt_4_turbo = Model(
name = 'gpt-4-turbo',
base_provider = 'openai',
best_provider = Bing
)
gigachat = Model(
name = 'GigaChat:latest',
base_provider = 'gigachat',
best_provider = GigaChat
)
gigachat_plus = Model(
name = 'GigaChat-Plus',
base_provider = 'gigachat',
best_provider = GigaChat
)
gigachat_pro = Model(
name = 'GigaChat-Pro',
base_provider = 'gigachat',
best_provider = GigaChat
)
llama2_7b = Model(
name = "meta-llama/Llama-2-7b-chat-hf",
base_provider = 'meta',
best_provider = RetryProvider([Llama, DeepInfra])
)
llama2_13b = Model(
name = "meta-llama/Llama-2-13b-chat-hf",
base_provider = 'meta',
best_provider = RetryProvider([Llama, DeepInfra])
)
llama2_70b = Model(
name = "meta-llama/Llama-2-70b-chat-hf",
base_provider = "meta",
best_provider = RetryProvider([Llama, DeepInfra])
)
llama3_8b_instruct = Model(
name = "meta-llama/Meta-Llama-3-8B-Instruct",
base_provider = "meta",
best_provider = RetryProvider([Llama, DeepInfra, Replicate])
)
llama3_70b_instruct = Model(
name = "meta-llama/Meta-Llama-3-70B-Instruct",
base_provider = "meta",
best_provider = RetryProvider([Llama, DeepInfra])
)
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 = RetryProvider([DeepInfra, PerplexityLabs])
)
# Mistral
mixtral_8x7b = Model(
name = "mistralai/Mixtral-8x7B-Instruct-v0.1",
base_provider = "huggingface",
best_provider = RetryProvider([DeepInfra, HuggingFace, PerplexityLabs])
)
mistral_7b = Model(
name = "mistralai/Mistral-7B-Instruct-v0.1",
base_provider = "huggingface",
best_provider = RetryProvider([HuggingChat, HuggingFace, PerplexityLabs])
)
mistral_7b_v02 = Model(
name = "mistralai/Mistral-7B-Instruct-v0.2",
base_provider = "huggingface",
best_provider = DeepInfra
)
mixtral_8x22b = Model(
name = "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
base_provider = "huggingface",
best_provider = DeepInfra
)
# Misc models
dolphin_mixtral_8x7b = Model(
name = "cognitivecomputations/dolphin-2.6-mixtral-8x7b",
base_provider = "huggingface",
best_provider = DeepInfra
)
lzlv_70b = Model(
name = "lizpreciatior/lzlv_70b_fp16_hf",
base_provider = "huggingface",
best_provider = DeepInfra
)
airoboros_70b = Model(
name = "deepinfra/airoboros-70b",
base_provider = "huggingface",
best_provider = DeepInfra
)
openchat_35 = Model(
name = "openchat/openchat_3.5",
base_provider = "huggingface",
best_provider = DeepInfra
)
# Bard
gemini = bard = palm = Model(
name = 'gemini',
base_provider = 'google',
best_provider = Gemini
)
claude_v2 = Model(
name = 'claude-v2',
base_provider = 'anthropic',
best_provider = RetryProvider([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
)
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_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
)
gemini_pro = Model(
name = 'gemini-pro',
base_provider = 'google',
best_provider = RetryProvider([GeminiProChat, You])
)
pi = Model(
name = 'pi',
base_provider = 'inflection',
best_provider = Pi
)
dbrx_instruct = Model(
name = 'databricks/dbrx-instruct',
base_provider = 'mistral',
best_provider = RetryProvider([DeepInfra, PerplexityLabs])
)
command_r_plus = Model(
name = 'CohereForAI/c4ai-command-r-plus',
base_provider = 'mistral',
best_provider = RetryProvider([HuggingChat, Cohere])
)
blackbox = Model(
name = 'blackbox',
base_provider = 'blackbox',
best_provider = Blackbox
)
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] = {
# 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-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,
# Llama
'llama2-7b' : llama2_7b,
'llama2-13b': llama2_13b,
'llama2-70b': llama2_70b,
'llama3-8b-instruct' : llama3_8b_instruct,
'llama3-70b-instruct': llama3_70b_instruct,
'codellama-34b-instruct': codellama_34b_instruct,
'codellama-70b-instruct': codellama_70b_instruct,
# GigaChat
'gigachat' : gigachat,
'gigachat_plus': gigachat_plus,
'gigachat_pro' : gigachat_pro,
# Mistral Opensource
'mixtral-8x7b': mixtral_8x7b,
'mistral-7b': mistral_7b,
'mistral-7b-v02': mistral_7b_v02,
'mixtral-8x22b': mixtral_8x22b,
'dolphin-mixtral-8x7b': dolphin_mixtral_8x7b,
# google gemini
'gemini': gemini,
'gemini-pro': gemini_pro,
# anthropic
'claude-v2': claude_v2,
'claude-3-opus': claude_3_opus,
'claude-3-sonnet': claude_3_sonnet,
# other
'blackbox': blackbox,
'command-r+': command_r_plus,
'dbrx-instruct': dbrx_instruct,
'lzlv-70b': lzlv_70b,
'airoboros-70b': airoboros_70b,
'openchat_3.5': openchat_35,
'pi': pi
}
_all_models = list(ModelUtils.convert.keys())