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397 lines
10 KiB
Python
397 lines
10 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from .Provider import RetryProvider, ProviderType
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from .Provider import (
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Chatgpt4Online,
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ChatgptDemoAi,
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GeminiProChat,
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ChatgptNext,
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HuggingChat,
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ChatgptDemo,
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FreeChatgpt,
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GptForLove,
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ChatgptAi,
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DeepInfra,
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ChatBase,
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Liaobots,
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GeekGpt,
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FakeGpt,
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FreeGpt,
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Llama2,
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Vercel,
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Phind,
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GptGo,
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Gpt6,
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Bard,
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Bing,
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You,
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H2o,
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Pi,
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)
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@dataclass(unsafe_hash=True)
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class Model:
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"""
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Represents a machine learning model configuration.
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Attributes:
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name (str): Name of the model.
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base_provider (str): Default provider for the model.
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best_provider (ProviderType): The preferred provider for the model, typically with retry logic.
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"""
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name: str
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base_provider: str
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best_provider: ProviderType = None
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@staticmethod
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def __all__() -> list[str]:
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"""Returns a list of all model names."""
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return _all_models
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default = Model(
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name = "",
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base_provider = "",
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best_provider = RetryProvider([
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Bing,
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ChatgptAi, GptGo, GeekGpt,
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You,
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Chatgpt4Online
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])
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)
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# GPT-3.5 too, but all providers supports long requests and responses
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gpt_35_long = Model(
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name = 'gpt-3.5-turbo',
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base_provider = 'openai',
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best_provider = RetryProvider([
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FreeGpt, You,
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GeekGpt, FakeGpt,
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Chatgpt4Online,
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ChatgptDemoAi,
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ChatgptNext,
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ChatgptDemo,
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Gpt6,
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])
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)
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# GPT-3.5 / GPT-4
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gpt_35_turbo = Model(
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name = 'gpt-3.5-turbo',
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base_provider = 'openai',
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best_provider=RetryProvider([
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GptGo, You,
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GptForLove, ChatBase,
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Chatgpt4Online,
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])
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)
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gpt_4 = Model(
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name = 'gpt-4',
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base_provider = 'openai',
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best_provider = RetryProvider([
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Bing, Phind, Liaobots,
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])
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)
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gpt_4_turbo = Model(
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name = 'gpt-4-turbo',
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base_provider = 'openai',
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best_provider = Bing
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)
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llama2_7b = Model(
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name = "meta-llama/Llama-2-7b-chat-hf",
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base_provider = 'huggingface',
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best_provider = RetryProvider([Llama2, DeepInfra])
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)
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llama2_13b = Model(
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name = "meta-llama/Llama-2-13b-chat-hf",
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base_provider = 'huggingface',
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best_provider = RetryProvider([Llama2, DeepInfra])
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)
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llama2_70b = Model(
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name = "meta-llama/Llama-2-70b-chat-hf",
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base_provider = "huggingface",
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best_provider = RetryProvider([Llama2, DeepInfra, HuggingChat])
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)
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# Mistal
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mixtral_8x7b = Model(
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name = "mistralai/Mixtral-8x7B-Instruct-v0.1",
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base_provider = "huggingface",
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best_provider = RetryProvider([DeepInfra, HuggingChat])
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)
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mistral_7b = Model(
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name = "mistralai/Mistral-7B-Instruct-v0.1",
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base_provider = "huggingface",
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best_provider = RetryProvider([DeepInfra, HuggingChat])
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)
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# Dolphin
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dolphin_mixtral_8x7b = Model(
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name = "cognitivecomputations/dolphin-2.6-mixtral-8x7b",
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base_provider = "huggingface",
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best_provider = DeepInfra
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)
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# OpenChat
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openchat_35 = Model(
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name = "openchat/openchat_3.5",
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base_provider = "huggingface",
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best_provider = RetryProvider([DeepInfra, HuggingChat])
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)
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# Bard
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palm = Model(
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name = 'palm',
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base_provider = 'google',
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best_provider = Bard)
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# H2o
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falcon_7b = Model(
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name = 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
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base_provider = 'huggingface',
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best_provider = H2o)
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falcon_40b = Model(
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name = 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
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base_provider = 'huggingface',
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best_provider = H2o)
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llama_13b = Model(
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name = 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b',
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base_provider = 'huggingface',
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best_provider = H2o)
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# Vercel
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claude_instant_v1 = Model(
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name = 'claude-instant-v1',
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base_provider = 'anthropic',
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best_provider = Vercel)
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claude_v1 = Model(
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name = 'claude-v1',
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base_provider = 'anthropic',
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best_provider = Vercel)
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claude_v2 = Model(
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name = 'claude-v2',
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base_provider = 'anthropic',
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best_provider = RetryProvider([FreeChatgpt, Vercel]))
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command_light_nightly = Model(
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name = 'command-light-nightly',
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base_provider = 'cohere',
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best_provider = Vercel)
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command_nightly = Model(
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name = 'command-nightly',
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base_provider = 'cohere',
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best_provider = Vercel)
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gpt_neox_20b = Model(
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name = 'EleutherAI/gpt-neox-20b',
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base_provider = 'huggingface',
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best_provider = Vercel)
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oasst_sft_1_pythia_12b = Model(
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name = 'OpenAssistant/oasst-sft-1-pythia-12b',
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base_provider = 'huggingface',
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best_provider = Vercel)
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oasst_sft_4_pythia_12b_epoch_35 = Model(
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name = 'OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
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base_provider = 'huggingface',
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best_provider = Vercel)
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santacoder = Model(
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name = 'bigcode/santacoder',
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base_provider = 'huggingface',
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best_provider = Vercel)
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bloom = Model(
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name = 'bigscience/bloom',
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base_provider = 'huggingface',
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best_provider = Vercel)
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flan_t5_xxl = Model(
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name = 'google/flan-t5-xxl',
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base_provider = 'huggingface',
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best_provider = Vercel)
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code_davinci_002 = Model(
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name = 'code-davinci-002',
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base_provider = 'openai',
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best_provider = Vercel)
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gpt_35_turbo_16k = Model(
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name = 'gpt-3.5-turbo-16k',
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base_provider = 'openai',
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best_provider = gpt_35_long.best_provider)
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gpt_35_turbo_16k_0613 = Model(
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name = 'gpt-3.5-turbo-16k-0613',
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base_provider = 'openai',
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best_provider = gpt_35_long.best_provider
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)
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gpt_35_turbo_0613 = Model(
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name = 'gpt-3.5-turbo-0613',
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base_provider = 'openai',
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best_provider = gpt_35_turbo.best_provider
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)
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gpt_4_0613 = Model(
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name = 'gpt-4-0613',
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base_provider = 'openai',
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best_provider = gpt_4.best_provider
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)
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gpt_4_32k = Model(
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name = 'gpt-4-32k',
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base_provider = 'openai',
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best_provider = gpt_4.best_provider
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)
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gpt_4_32k_0613 = Model(
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name = 'gpt-4-32k-0613',
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base_provider = 'openai',
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best_provider = gpt_4.best_provider
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)
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gemini_pro = Model(
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name = 'gemini-pro',
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base_provider = 'google',
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best_provider = RetryProvider([FreeChatgpt, GeminiProChat])
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)
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text_ada_001 = Model(
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name = 'text-ada-001',
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base_provider = 'openai',
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best_provider = Vercel)
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text_babbage_001 = Model(
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name = 'text-babbage-001',
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base_provider = 'openai',
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best_provider = Vercel)
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text_curie_001 = Model(
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name = 'text-curie-001',
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base_provider = 'openai',
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best_provider = Vercel)
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text_davinci_002 = Model(
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name = 'text-davinci-002',
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base_provider = 'openai',
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best_provider = Vercel)
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text_davinci_003 = Model(
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name = 'text-davinci-003',
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base_provider = 'openai',
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best_provider = Vercel)
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llama13b_v2_chat = Model(
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name = 'replicate:a16z-infra/llama13b-v2-chat',
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base_provider = 'replicate',
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best_provider = Vercel)
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llama7b_v2_chat = Model(
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name = 'replicate:a16z-infra/llama7b-v2-chat',
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base_provider = 'replicate',
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best_provider = Vercel)
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llama70b_v2_chat = Model(
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name = 'replicate/llama70b-v2-chat',
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base_provider = 'replicate',
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best_provider = Vercel)
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pi = Model(
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name = 'pi',
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base_provider = 'inflection',
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best_provider=Pi
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)
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class ModelUtils:
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"""
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Utility class for mapping string identifiers to Model instances.
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Attributes:
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convert (dict[str, Model]): Dictionary mapping model string identifiers to Model instances.
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"""
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convert: dict[str, Model] = {
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# gpt-3.5
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'gpt-3.5-turbo' : gpt_35_turbo,
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'gpt-3.5-turbo-0613' : gpt_35_turbo_0613,
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'gpt-3.5-turbo-16k' : gpt_35_turbo_16k,
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'gpt-3.5-turbo-16k-0613' : gpt_35_turbo_16k_0613,
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'gpt-3.5-long': gpt_35_long,
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# gpt-4
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'gpt-4' : gpt_4,
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'gpt-4-0613' : gpt_4_0613,
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'gpt-4-32k' : gpt_4_32k,
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'gpt-4-32k-0613' : gpt_4_32k_0613,
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'gpt-4-turbo' : gpt_4_turbo,
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# Llama 2
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'llama2-7b' : llama2_7b,
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'llama2-13b': llama2_13b,
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'llama2-70b': llama2_70b,
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# Mistral
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'mixtral-8x7b': mixtral_8x7b,
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'mistral-7b': mistral_7b,
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# Dolphin
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'dolphin-mixtral-8x7b': dolphin_mixtral_8x7b,
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# OpenChat
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'openchat_3.5': openchat_35,
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# Gemini Pro
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'gemini-pro': gemini_pro,
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# Bard
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'palm2' : palm,
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'palm' : palm,
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'google' : palm,
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'google-bard' : palm,
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'google-palm' : palm,
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'bard' : palm,
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# H2o
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'falcon-40b' : falcon_40b,
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'falcon-7b' : falcon_7b,
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'llama-13b' : llama_13b,
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# Vercel
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#'claude-instant-v1' : claude_instant_v1,
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#'claude-v1' : claude_v1,
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#'claude-v2' : claude_v2,
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'command-nightly' : command_nightly,
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'gpt-neox-20b' : gpt_neox_20b,
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'santacoder' : santacoder,
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'bloom' : bloom,
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'flan-t5-xxl' : flan_t5_xxl,
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'code-davinci-002' : code_davinci_002,
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'text-ada-001' : text_ada_001,
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'text-babbage-001' : text_babbage_001,
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'text-curie-001' : text_curie_001,
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'text-davinci-002' : text_davinci_002,
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'text-davinci-003' : text_davinci_003,
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'llama70b-v2-chat' : llama70b_v2_chat,
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'llama13b-v2-chat' : llama13b_v2_chat,
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'llama7b-v2-chat' : llama7b_v2_chat,
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'oasst-sft-1-pythia-12b' : oasst_sft_1_pythia_12b,
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'oasst-sft-4-pythia-12b-epoch-3.5' : oasst_sft_4_pythia_12b_epoch_35,
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'command-light-nightly' : command_light_nightly,
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'pi': pi
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}
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_all_models = list(ModelUtils.convert.keys())
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