gpt4free/g4f/models.py
H Lohaus eee038a73e
Add GeminiProChat Provider (#1419)
* Add GeminiProChat Provider
2024-01-02 14:58:28 +01:00

364 lines
8.9 KiB
Python

from __future__ import annotations
from dataclasses import dataclass
from .Provider import RetryProvider, ProviderType
from .Provider import (
Chatgpt4Online,
ChatgptDemoAi,
ChatgptNext,
HuggingChat,
ChatgptDemo,
GptForLove,
ChatgptAi,
DeepInfra,
OnlineGpt,
ChatBase,
Liaobots,
GeekGpt,
FakeGpt,
FreeGpt,
Berlin,
Llama2,
Vercel,
Phind,
Koala,
GptGo,
Gpt6,
Bard,
Bing,
You,
H2o,
Pi,
)
@dataclass(unsafe_hash=True)
class Model:
name: str
base_provider: str
best_provider: ProviderType = None
@staticmethod
def __all__() -> list[str]:
return _all_models
default = Model(
name = "",
base_provider = "",
best_provider = RetryProvider([
Bing,
ChatgptAi, GptGo, GeekGpt,
You,
Chatgpt4Online
])
)
# 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,
GeekGpt, FakeGpt,
Berlin, Koala,
Chatgpt4Online,
ChatgptDemoAi,
OnlineGpt,
ChatgptNext,
ChatgptDemo,
Gpt6,
])
)
# GPT-3.5 / GPT-4
gpt_35_turbo = Model(
name = 'gpt-3.5-turbo',
base_provider = 'openai',
best_provider=RetryProvider([
GptGo, You,
GptForLove, ChatBase,
Chatgpt4Online,
])
)
gpt_4 = Model(
name = 'gpt-4',
base_provider = 'openai',
best_provider = RetryProvider([
Bing, Phind, Liaobots
])
)
gpt_4_turbo = Model(
name = 'gpt-4-turbo',
base_provider = 'openai',
best_provider = Bing
)
llama2_7b = Model(
name = "meta-llama/Llama-2-7b-chat-hf",
base_provider = 'huggingface',
best_provider = RetryProvider([Llama2, DeepInfra])
)
llama2_13b = Model(
name = "meta-llama/Llama-2-13b-chat-hf",
base_provider = 'huggingface',
best_provider = RetryProvider([Llama2, DeepInfra])
)
llama2_70b = Model(
name = "meta-llama/Llama-2-70b-chat-hf",
base_provider = "huggingface",
best_provider = RetryProvider([Llama2, DeepInfra, HuggingChat])
)
# Mistal
mixtral_8x7b = Model(
name = "mistralai/Mixtral-8x7B-Instruct-v0.1",
base_provider = "huggingface",
best_provider = RetryProvider([DeepInfra, HuggingChat])
)
mistral_7b = Model(
name = "mistralai/Mistral-7B-Instruct-v0.1",
base_provider = "huggingface",
best_provider = RetryProvider([DeepInfra, HuggingChat])
)
openchat_35 = Model(
name = "openchat/openchat_3.5",
base_provider = "huggingface",
best_provider = RetryProvider([DeepInfra, HuggingChat])
)
# Bard
palm = Model(
name = 'palm',
base_provider = 'google',
best_provider = Bard)
# H2o
falcon_7b = Model(
name = 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
base_provider = 'huggingface',
best_provider = H2o)
falcon_40b = Model(
name = 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
base_provider = 'huggingface',
best_provider = H2o)
llama_13b = Model(
name = 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b',
base_provider = 'huggingface',
best_provider = H2o)
# Vercel
claude_instant_v1 = Model(
name = 'claude-instant-v1',
base_provider = 'anthropic',
best_provider = Vercel)
claude_v1 = Model(
name = 'claude-v1',
base_provider = 'anthropic',
best_provider = Vercel)
claude_v2 = Model(
name = 'claude-v2',
base_provider = 'anthropic',
best_provider = Vercel)
command_light_nightly = Model(
name = 'command-light-nightly',
base_provider = 'cohere',
best_provider = Vercel)
command_nightly = Model(
name = 'command-nightly',
base_provider = 'cohere',
best_provider = Vercel)
gpt_neox_20b = Model(
name = 'EleutherAI/gpt-neox-20b',
base_provider = 'huggingface',
best_provider = Vercel)
oasst_sft_1_pythia_12b = Model(
name = 'OpenAssistant/oasst-sft-1-pythia-12b',
base_provider = 'huggingface',
best_provider = Vercel)
oasst_sft_4_pythia_12b_epoch_35 = Model(
name = 'OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
base_provider = 'huggingface',
best_provider = Vercel)
santacoder = Model(
name = 'bigcode/santacoder',
base_provider = 'huggingface',
best_provider = Vercel)
bloom = Model(
name = 'bigscience/bloom',
base_provider = 'huggingface',
best_provider = Vercel)
flan_t5_xxl = Model(
name = 'google/flan-t5-xxl',
base_provider = 'huggingface',
best_provider = Vercel)
code_davinci_002 = Model(
name = 'code-davinci-002',
base_provider = 'openai',
best_provider = Vercel)
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
)
text_ada_001 = Model(
name = 'text-ada-001',
base_provider = 'openai',
best_provider = Vercel)
text_babbage_001 = Model(
name = 'text-babbage-001',
base_provider = 'openai',
best_provider = Vercel)
text_curie_001 = Model(
name = 'text-curie-001',
base_provider = 'openai',
best_provider = Vercel)
text_davinci_002 = Model(
name = 'text-davinci-002',
base_provider = 'openai',
best_provider = Vercel)
text_davinci_003 = Model(
name = 'text-davinci-003',
base_provider = 'openai',
best_provider = Vercel)
llama13b_v2_chat = Model(
name = 'replicate:a16z-infra/llama13b-v2-chat',
base_provider = 'replicate',
best_provider = Vercel)
llama7b_v2_chat = Model(
name = 'replicate:a16z-infra/llama7b-v2-chat',
base_provider = 'replicate',
best_provider = Vercel)
llama70b_v2_chat = Model(
name = 'replicate/llama70b-v2-chat',
base_provider = 'replicate',
best_provider = Vercel)
pi = Model(
name = 'pi',
base_provider = 'inflection',
best_provider=Pi
)
class ModelUtils:
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 2
'llama2-7b' : llama2_7b,
'llama2-13b': llama2_13b,
'llama2-70b': llama2_70b,
# Mistral
'mixtral-8x7b': mixtral_8x7b,
'mistral-7b': mistral_7b,
'openchat_3.5': openchat_35,
# Bard
'palm2' : palm,
'palm' : palm,
'google' : palm,
'google-bard' : palm,
'google-palm' : palm,
'bard' : palm,
# H2o
'falcon-40b' : falcon_40b,
'falcon-7b' : falcon_7b,
'llama-13b' : llama_13b,
# Vercel
#'claude-instant-v1' : claude_instant_v1,
#'claude-v1' : claude_v1,
#'claude-v2' : claude_v2,
'command-nightly' : command_nightly,
'gpt-neox-20b' : gpt_neox_20b,
'santacoder' : santacoder,
'bloom' : bloom,
'flan-t5-xxl' : flan_t5_xxl,
'code-davinci-002' : code_davinci_002,
'text-ada-001' : text_ada_001,
'text-babbage-001' : text_babbage_001,
'text-curie-001' : text_curie_001,
'text-davinci-002' : text_davinci_002,
'text-davinci-003' : text_davinci_003,
'llama70b-v2-chat' : llama70b_v2_chat,
'llama13b-v2-chat' : llama13b_v2_chat,
'llama7b-v2-chat' : llama7b_v2_chat,
'oasst-sft-1-pythia-12b' : oasst_sft_1_pythia_12b,
'oasst-sft-4-pythia-12b-epoch-3.5' : oasst_sft_4_pythia_12b_epoch_35,
'command-light-nightly' : command_light_nightly,
'pi': pi
}
_all_models = list(ModelUtils.convert.keys())