from __future__ import annotations from dataclasses import dataclass from .Provider import IterListProvider, ProviderType from .Provider import ( AiChatOnline, Allyfy, Bing, Bixin123, Blackbox, ChatGot, Chatgpt4Online, Chatgpt4o, ChatgptFree, CodeNews, DDG, DeepInfra, DeepInfraImage, FluxAirforce, FreeChatgpt, FreeGpt, FreeNetfly, Gemini, GeminiPro, GigaChat, HuggingChat, HuggingFace, Koala, Liaobots, MagickPenAsk, MagickPenChat, MetaAI, Nexra, OpenaiChat, PerplexityLabs, Pi, Pizzagpt, Reka, Replicate, ReplicateHome, Snova, TeachAnything, TwitterBio, Upstage, 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([ ChatGot, Chatgpt4Online, DDG, FreeChatgpt, FreeNetfly, HuggingChat, MagickPenAsk, MagickPenChat, Pizzagpt, ChatgptFree, ReplicateHome, Upstage, Blackbox, Bixin123, ]) ) ############ ### Text ### ############ ### OpenAI ### # gpt-3 gpt_3 = Model( name = 'gpt-3', base_provider = 'OpenAI', best_provider = IterListProvider([ Nexra, ]) ) # gpt-3.5 gpt_35_turbo = Model( name = 'gpt-3.5-turbo', base_provider = 'OpenAI', best_provider = IterListProvider([ Allyfy, TwitterBio, Nexra, Bixin123, CodeNews, ]) ) # gpt-4 gpt_4o = Model( name = 'gpt-4o', base_provider = 'OpenAI', best_provider = IterListProvider([ Liaobots, Chatgpt4o, OpenaiChat, ]) ) gpt_4o_mini = Model( name = 'gpt-4o-mini', base_provider = 'OpenAI', best_provider = IterListProvider([ DDG, Liaobots, You, FreeNetfly, MagickPenAsk, MagickPenChat, Pizzagpt, ChatgptFree, AiChatOnline, CodeNews, OpenaiChat, Koala, ]) ) gpt_4_turbo = Model( name = 'gpt-4-turbo', base_provider = 'OpenAI', best_provider = IterListProvider([ Nexra, Bixin123, Liaobots, Bing ]) ) gpt_4 = Model( name = 'gpt-4', base_provider = 'OpenAI', best_provider = IterListProvider([ Chatgpt4Online, Nexra, Bing, gpt_4_turbo.best_provider, gpt_4o.best_provider, gpt_4o_mini.best_provider ]) ) ### GigaChat ### gigachat = Model( name = 'GigaChat:latest', base_provider = 'gigachat', best_provider = GigaChat ) ### Meta ### meta = Model( name = "meta-ai", base_provider = "Meta", best_provider = MetaAI ) llama_3_8b = Model( name = "llama-3-8b", base_provider = "Meta", best_provider = IterListProvider([DeepInfra, Replicate]) ) llama_3_70b = Model( name = "llama-3-70b", base_provider = "Meta", best_provider = IterListProvider([ReplicateHome, DeepInfra, PerplexityLabs, Replicate]) ) llama_3_1_8b = Model( name = "llama-3.1-8b", base_provider = "Meta", best_provider = IterListProvider([Blackbox]) ) llama_3_1_70b = Model( name = "llama-3.1-70b", base_provider = "Meta", best_provider = IterListProvider([DDG, HuggingChat, FreeGpt, Blackbox, TeachAnything, HuggingFace]) ) llama_3_1_405b = Model( name = "llama-3.1-405b", base_provider = "Meta", best_provider = IterListProvider([HuggingChat, Blackbox, HuggingFace]) ) ### Mistral ### mixtral_8x7b = Model( name = "mixtral-8x7b", base_provider = "Mistral", best_provider = IterListProvider([HuggingChat, DDG, ReplicateHome, TwitterBio, DeepInfra, HuggingFace,]) ) mistral_7b = Model( name = "mistral-7b", base_provider = "Mistral", best_provider = IterListProvider([HuggingChat, HuggingFace, DeepInfra]) ) ### 01-ai ### yi_1_5_34b = Model( name = "yi-1.5-34b", base_provider = "01-ai", best_provider = IterListProvider([HuggingChat, HuggingFace]) ) ### Microsoft ### phi_3_mini_4k = Model( name = "phi-3-mini-4k", 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, ChatGot, Liaobots]) ) gemini_flash = Model( name = 'gemini-flash', base_provider = 'Google', best_provider = IterListProvider([Liaobots, Blackbox]) ) # gemma gemma_2b = Model( name = 'gemma-2b', base_provider = 'Google', best_provider = IterListProvider([ReplicateHome]) ) ### 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([Liaobots]) ) claude_3_sonnet = Model( name = 'claude-3-sonnet', base_provider = 'Anthropic', best_provider = IterListProvider([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, Liaobots]) ) ### Reka AI ### reka_core = Model( name = 'reka-core', base_provider = 'Reka AI', best_provider = Reka ) ### Blackbox ### blackbox = Model( name = 'blackbox', base_provider = 'Blackbox', best_provider = Blackbox ) ### Databricks ### dbrx_instruct = Model( name = 'dbrx-instruct', base_provider = 'Databricks', best_provider = IterListProvider([DeepInfra]) ) ### CohereForAI ### command_r_plus = Model( name = '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]) ) ### Qwen ### qwen_1_5_14b = Model( name = 'qwen-1.5-14b', base_provider = 'Qwen', best_provider = IterListProvider([FreeChatgpt]) ) qwen_turbo = Model( name = 'qwen-turbo', base_provider = 'Qwen', best_provider = IterListProvider([Bixin123]) ) ### Zhipu AI ### glm_3_6b = Model( name = 'glm-3-6b', base_provider = 'Zhipu AI', best_provider = IterListProvider([FreeChatgpt]) ) glm_4_9b = Model( name = 'glm-4-9B', base_provider = 'Zhipu AI', best_provider = IterListProvider([FreeChatgpt]) ) glm_4 = Model( name = 'glm-4', base_provider = 'Zhipu AI', best_provider = IterListProvider([CodeNews, glm_4_9b.best_provider,]) ) ### 01-ai ### yi_1_5_9b = Model( name = 'yi-1.5-9b', base_provider = '01-ai', best_provider = IterListProvider([FreeChatgpt]) ) ### Pi ### solar_1_mini = Model( name = 'solar-1-mini', base_provider = 'Upstage', best_provider = IterListProvider([Upstage]) ) ### Pi ### pi = Model( name = 'pi', base_provider = 'inflection', best_provider = Pi ) ### SambaNova ### samba_coe_v0_1 = Model( name = 'samba-coe-v0.1', base_provider = 'SambaNova', best_provider = Snova ) ### Trong-Hieu Nguyen-Mau ### v1olet_merged_7b = Model( name = 'v1olet-merged-7b', base_provider = 'Trong-Hieu Nguyen-Mau', best_provider = Snova ) ### Macadeliccc ### westlake_7b_v2 = Model( name = 'westlake-7b-v2', base_provider = 'Macadeliccc', best_provider = Snova ) ### CookinAI ### donutlm_v1 = Model( name = 'donutlm-v1', base_provider = 'CookinAI', best_provider = Snova ) ### DeepSeek ### deepseek = Model( name = 'deepseek', base_provider = 'DeepSeek', best_provider = CodeNews ) ############# ### Image ### ############# ### Stability AI ### sdxl = Model( name = 'sdxl', base_provider = 'Stability AI', best_provider = IterListProvider([ReplicateHome, DeepInfraImage]) ) sd_3 = Model( name = 'sd-3', base_provider = 'Stability AI', best_provider = IterListProvider([ReplicateHome]) ) ### Playground ### playground_v2_5 = Model( name = 'playground-v2.5', base_provider = 'Stability AI', best_provider = IterListProvider([ReplicateHome]) ) ### Flux AI ### flux = Model( name = 'flux', base_provider = 'Flux AI', best_provider = IterListProvider([FluxAirforce]) ) flux_realism = Model( name = 'flux-realism', base_provider = 'Flux AI', best_provider = IterListProvider([FluxAirforce]) ) flux_anime = Model( name = 'flux-anime', base_provider = 'Flux AI', best_provider = IterListProvider([FluxAirforce]) ) flux_3d = Model( name = 'flux-3d', base_provider = 'Flux AI', best_provider = IterListProvider([FluxAirforce]) ) flux_disney = Model( name = 'flux-disney', base_provider = 'Flux AI', best_provider = IterListProvider([FluxAirforce]) ) ### ### dalle = Model( name = 'dalle', base_provider = '', best_provider = IterListProvider([Nexra]) ) dalle_mini = Model( name = 'dalle-mini', base_provider = '', best_provider = IterListProvider([Nexra]) ) emi = Model( name = 'emi', base_provider = '', best_provider = IterListProvider([Nexra]) ) 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 'gpt-3': gpt_3, # gpt-3.5 'gpt-3.5-turbo': gpt_35_turbo, # 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 'llama-3-8b': llama_3_8b, 'llama-3-70b': llama_3_70b, # llama-3.1 'llama-3.1-8b': llama_3_1_8b, 'llama-3.1-70b': llama_3_1_70b, 'llama-3.1-405b': llama_3_1_405b, ### Mistral ### 'mixtral-8x7b': mixtral_8x7b, 'mistral-7b': mistral_7b, ### 01-ai ### 'yi-1.5-34b': yi_1_5_34b, ### Microsoft ### 'phi-3-mini-4k': phi_3_mini_4k, ### Google ### # gemini 'gemini': gemini, 'gemini-pro': gemini_pro, 'gemini-flash': gemini_flash, # gemma 'gemma-2b': gemma_2b, ### 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-core': reka_core, ### Blackbox ### 'blackbox': blackbox, ### CohereForAI ### 'command-r+': command_r_plus, ### Databricks ### 'dbrx-instruct': dbrx_instruct, ### GigaChat ### 'gigachat': gigachat, ### iFlytek ### 'sparkdesk-v1.1': sparkdesk_v1_1, ### Qwen ### 'qwen-1.5-14b': qwen_1_5_14b, 'qwen-turbo': qwen_turbo, ### Zhipu AI ### 'glm-3-6b': glm_3_6b, 'glm-4-9b': glm_4_9b, 'glm-4': glm_4, ### 01-ai ### 'yi-1.5-9b': yi_1_5_9b, ### Upstage ### 'solar-1-mini': solar_1_mini, ### Pi ### 'pi': pi, ### SambaNova ### 'samba-coe-v0.1': samba_coe_v0_1, ### Trong-Hieu Nguyen-Mau ### 'v1olet-merged-7b': v1olet_merged_7b, ### Macadeliccc ### 'westlake-7b-v2': westlake_7b_v2, ### CookinAI ### 'donutlm-v1': donutlm_v1, ### DeepSeek ### 'deepseek': deepseek, ############# ### Image ### ############# ### Stability AI ### 'sdxl': sdxl, 'sd-3': sd_3, ### Playground ### 'playground-v2.5': playground_v2_5, ### Flux AI ### 'flux': flux, 'flux-realism': flux_realism, 'flux-anime': flux_anime, 'flux-3d': flux_3d, 'flux-disney': flux_disney, ### ### 'dalle': dalle, 'dalle-mini': dalle_mini, 'emi': emi, } _all_models = list(ModelUtils.convert.keys())