from __future__ import annotations from dataclasses import dataclass from .Provider import IterListProvider, ProviderType from .Provider import ( Airforce, Allyfy, Bing, Binjie, Bixin123, Blackbox, ChatGpt, Chatgpt4o, Chatgpt4Online, ChatGptEs, ChatgptFree, DDG, DeepInfra, DeepInfraChat, DeepInfraImage, Free2GPT, FreeChatgpt, FreeGpt, FreeNetfly, Gemini, GeminiPro, GigaChat, HuggingChat, HuggingFace, Koala, Liaobots, LiteIcoding, MagickPen, MetaAI, Nexra, OpenaiChat, PerplexityLabs, Pi, Pizzagpt, Reka, Replicate, ReplicateHome, TeachAnything, 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([ DDG, FreeChatgpt, HuggingChat, Pizzagpt, ReplicateHome, Upstage, Blackbox, Bixin123, Binjie, Free2GPT, MagickPen, DeepInfraChat, LiteIcoding, ]) ) ############ ### 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, Nexra, Bixin123, Airforce, ]) ) # gpt-4 gpt_4o = Model( name = 'gpt-4o', base_provider = 'OpenAI', best_provider = IterListProvider([ Liaobots, Airforce, Chatgpt4o, ChatGptEs, OpenaiChat ]) ) gpt_4o_mini = Model( name = 'gpt-4o-mini', base_provider = 'OpenAI', best_provider = IterListProvider([ DDG, ChatGptEs, You, FreeNetfly, Pizzagpt, LiteIcoding, MagickPen, Liaobots, Airforce, ChatgptFree, Koala, OpenaiChat, ChatGpt ]) ) gpt_4_turbo = Model( name = 'gpt-4-turbo', base_provider = 'OpenAI', best_provider = IterListProvider([ Nexra, Bixin123, Liaobots, Airforce, Bing ]) ) gpt_4 = Model( name = 'gpt-4', base_provider = 'OpenAI', best_provider = IterListProvider([ Nexra, Binjie, Airforce, gpt_4_turbo.best_provider, gpt_4o.best_provider, gpt_4o_mini.best_provider, Chatgpt4Online, Bing, OpenaiChat, ]) ) # o1 o1 = Model( name = 'o1', base_provider = 'OpenAI', best_provider = IterListProvider([ Liaobots ]) ) o1_mini = Model( name = 'o1-mini', base_provider = 'OpenAI', best_provider = IterListProvider([ Liaobots ]) ) ### 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 2 llama_2_13b = Model( name = "llama-2-13b", base_provider = "Meta Llama", best_provider = IterListProvider([Airforce]) ) # llama 3 llama_3_8b = Model( name = "llama-3-8b", base_provider = "Meta Llama", best_provider = IterListProvider([Airforce, DeepInfra, Replicate]) ) llama_3_70b = Model( name = "llama-3-70b", base_provider = "Meta Llama", best_provider = IterListProvider([ReplicateHome, Airforce, DeepInfra, Replicate]) ) llama_3 = Model( name = "llama-3", base_provider = "Meta Llama", best_provider = IterListProvider([llama_3_8b.best_provider, llama_3_70b.best_provider]) ) # llama 3.1 llama_3_1_8b = Model( name = "llama-3.1-8b", base_provider = "Meta Llama", best_provider = IterListProvider([Blackbox, DeepInfraChat, Airforce, PerplexityLabs]) ) llama_3_1_70b = Model( name = "llama-3.1-70b", base_provider = "Meta Llama", best_provider = IterListProvider([DDG, HuggingChat, Blackbox, FreeGpt, TeachAnything, Free2GPT, DeepInfraChat, Airforce, HuggingFace, PerplexityLabs]) ) llama_3_1_405b = Model( name = "llama-3.1-405b", base_provider = "Meta Llama", best_provider = IterListProvider([Blackbox, DeepInfraChat, Airforce]) ) llama_3_1 = Model( name = "llama-3.1", base_provider = "Meta Llama", best_provider = IterListProvider([llama_3_1_8b.best_provider, llama_3_1_70b.best_provider, llama_3_1_405b.best_provider,]) ) ### Mistral ### mistral_7b = Model( name = "mistral-7b", base_provider = "Mistral", best_provider = IterListProvider([HuggingChat, DeepInfraChat, Airforce, HuggingFace, DeepInfra]) ) mixtral_8x7b = Model( name = "mixtral-8x7b", base_provider = "Mistral", best_provider = IterListProvider([DDG, ReplicateHome, DeepInfraChat, Airforce, DeepInfra]) ) mixtral_8x22b = Model( name = "mixtral-8x22b", base_provider = "Mistral", best_provider = IterListProvider([DeepInfraChat, Airforce]) ) mistral_nemo = Model( name = "mistral-nemo", base_provider = "Mistral", best_provider = IterListProvider([HuggingChat, HuggingFace]) ) ### NousResearch ### mixtral_8x7b_dpo = Model( name = "mixtral-8x7b-dpo", base_provider = "NousResearch", best_provider = IterListProvider([Airforce]) ) hermes_3 = Model( name = "hermes-3", base_provider = "NousResearch", best_provider = IterListProvider([HuggingChat, HuggingFace]) ) ### Microsoft ### phi_3_medium_4k = Model( name = "phi-3-medium-4k", base_provider = "Microsoft", best_provider = IterListProvider([DeepInfraChat]) ) phi_3_5_mini = Model( name = "phi-3.5-mini", base_provider = "Microsoft", best_provider = IterListProvider([HuggingChat, HuggingFace]) ) ### Google DeepMind ### # gemini gemini_pro = Model( name = 'gemini-pro', base_provider = 'Google DeepMind', best_provider = IterListProvider([GeminiPro, LiteIcoding, Blackbox, Liaobots, Airforce]) ) gemini_flash = Model( name = 'gemini-flash', base_provider = 'Google DeepMind', best_provider = IterListProvider([Blackbox, Liaobots, Airforce]) ) gemini = Model( name = 'gemini', base_provider = 'Google DeepMind', best_provider = IterListProvider([ Gemini, gemini_flash.best_provider, gemini_pro.best_provider ]) ) # gemma gemma_2b_9b = Model( name = 'gemma-2b-9b', base_provider = 'Google', best_provider = IterListProvider([Airforce]) ) gemma_2b_27b = Model( name = 'gemma-2b-27b', base_provider = 'Google', best_provider = IterListProvider([DeepInfraChat, Airforce]) ) gemma_2b = Model( name = 'gemma-2b', base_provider = 'Google', best_provider = IterListProvider([ ReplicateHome, Airforce, gemma_2b_9b.best_provider, gemma_2b_27b.best_provider, ]) ) ### 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 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_haiku = Model( name = 'claude-3-haiku', base_provider = 'Anthropic', best_provider = IterListProvider([DDG, Liaobots]) ) claude_3 = Model( name = 'claude-3', base_provider = 'Anthropic', best_provider = IterListProvider([ claude_3_opus.best_provider, claude_3_sonnet.best_provider, claude_3_haiku.best_provider ]) ) # claude 3.5 claude_3_5_sonnet = Model( name = 'claude-3.5-sonnet', base_provider = 'Anthropic', best_provider = IterListProvider([Blackbox, Liaobots]) ) claude_3_5 = Model( name = 'claude-3.5', base_provider = 'Anthropic', best_provider = IterListProvider([ LiteIcoding, claude_3_5_sonnet.best_provider ]) ) ### Reka AI ### reka_core = Model( name = 'reka-core', base_provider = 'Reka AI', best_provider = Reka ) ### Blackbox AI ### blackbox = Model( name = 'blackbox', base_provider = 'Blackbox AI', best_provider = IterListProvider([Blackbox]) ) ### Databricks ### dbrx_instruct = Model( name = 'dbrx-instruct', base_provider = 'Databricks', best_provider = IterListProvider([Airforce, 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, Airforce]) ) ### Qwen ### qwen_1_5_14b = Model( name = 'qwen-1.5-14b', base_provider = 'Qwen', best_provider = IterListProvider([FreeChatgpt]) ) qwen_1_5_72b = Model( name = 'qwen-1.5-72b', base_provider = 'Qwen', best_provider = IterListProvider([Airforce]) ) qwen_1_5_110b = Model( name = 'qwen-1.5-110b', base_provider = 'Qwen', best_provider = IterListProvider([Airforce]) ) qwen_2_72b = Model( name = 'qwen-2-72b', base_provider = 'Qwen', best_provider = IterListProvider([DeepInfraChat, HuggingChat, Airforce, HuggingFace]) ) qwen_turbo = Model( name = 'qwen-turbo', base_provider = 'Qwen', best_provider = IterListProvider([Bixin123]) ) qwen = Model( name = 'qwen', base_provider = 'Qwen', best_provider = IterListProvider([ qwen_1_5_14b.best_provider, qwen_1_5_72b.best_provider, qwen_1_5_110b.best_provider, qwen_2_72b.best_provider, qwen_turbo.best_provider ]) ) ### 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([ glm_3_6b.best_provider, 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]) ) yi_34b = Model( name = 'yi-34b', base_provider = '01-ai', best_provider = IterListProvider([Airforce]) ) ### Upstage ### solar_1_mini = Model( name = 'solar-1-mini', base_provider = 'Upstage', best_provider = IterListProvider([Upstage]) ) solar_10_7b = Model( name = 'solar-10-7b', base_provider = 'Upstage', best_provider = Airforce ) solar_pro = Model( name = 'solar-pro', base_provider = 'Upstage', best_provider = Upstage ) ### Inflection ### pi = Model( name = 'pi', base_provider = 'Inflection', best_provider = Pi ) ### DeepSeek ### deepseek = Model( name = 'deepseek', base_provider = 'DeepSeek', best_provider = IterListProvider([Airforce]) ) ### WizardLM ### wizardlm_2_7b = Model( name = 'wizardlm-2-7b', base_provider = 'WizardLM', best_provider = IterListProvider([DeepInfraChat]) ) wizardlm_2_8x22b = Model( name = 'wizardlm-2-8x22b', base_provider = 'WizardLM', best_provider = IterListProvider([DeepInfraChat, Airforce]) ) ### Together ### sh_n_7b = Model( name = 'sh-n-7b', base_provider = 'Together', best_provider = Airforce ) ### Yorickvp ### llava_13b = Model( name = 'llava-13b', base_provider = 'Yorickvp', best_provider = ReplicateHome ) ### OpenBMB ### minicpm_llama_3_v2_5 = Model( name = 'minicpm-llama-3-v2.5', base_provider = 'OpenBMB', best_provider = DeepInfraChat ) ### Lzlv ### lzlv_70b = Model( name = 'lzlv-70b', base_provider = 'Lzlv', best_provider = DeepInfraChat ) ### OpenChat ### openchat_3_6_8b = Model( name = 'openchat-3.6-8b', base_provider = 'OpenChat', best_provider = DeepInfraChat ) ### Phind ### phind_codellama_34b_v2 = Model( name = 'phind-codellama-34b-v2', base_provider = 'Phind', best_provider = DeepInfraChat ) ### Cognitive Computations ### dolphin_2_9_1_llama_3_70b = Model( name = 'dolphin-2.9.1-llama-3-70b', base_provider = 'Cognitive Computations', best_provider = DeepInfraChat ) ### x.ai ### grok_2 = Model( name = 'grok-2', base_provider = 'x.ai', best_provider = Liaobots ) grok_2_mini = Model( name = 'grok-2-mini', base_provider = 'x.ai', best_provider = Liaobots ) ############# ### 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 = 'Playground AI', best_provider = IterListProvider([ReplicateHome]) ) ### Flux AI ### flux = Model( name = 'flux', base_provider = 'Flux AI', best_provider = IterListProvider([Airforce, Blackbox]) ) flux_realism = Model( name = 'flux-realism', base_provider = 'Flux AI', best_provider = IterListProvider([Airforce]) ) flux_anime = Model( name = 'flux-anime', base_provider = 'Flux AI', best_provider = IterListProvider([Airforce]) ) flux_3d = Model( name = 'flux-3d', base_provider = 'Flux AI', best_provider = IterListProvider([Airforce]) ) flux_disney = Model( name = 'flux-disney', base_provider = 'Flux AI', best_provider = IterListProvider([Airforce]) ) flux_pixel = Model( name = 'flux-pixel', base_provider = 'Flux AI', best_provider = IterListProvider([Airforce]) ) flux_4o = Model( name = 'flux-4o', base_provider = 'Flux AI', best_provider = IterListProvider([Airforce]) ) flux_schnell = Model( name = 'flux-schnell', base_provider = 'Flux AI', best_provider = IterListProvider([ReplicateHome]) ) ### ### dalle_2 = Model( name = 'dalle-2', base_provider = '', best_provider = IterListProvider([Nexra]) ) dalle_3 = Model( name = 'dalle-3', base_provider = '', best_provider = IterListProvider([Airforce]) ) dalle = Model( name = 'dalle', base_provider = '', best_provider = IterListProvider([ Nexra, dalle_2.best_provider, dalle_3.best_provider, ]) ) dalle_mini = Model( name = 'dalle-mini', base_provider = '', best_provider = IterListProvider([Nexra]) ) ### ### emi = Model( name = 'emi', base_provider = '', best_provider = IterListProvider([Nexra]) ) any_dark = Model( name = 'any-dark', base_provider = '', best_provider = IterListProvider([Airforce]) ) 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, # o1 'o1': o1, 'o1-mini': o1_mini, ### Meta ### "meta-ai": meta, # llama-2 'llama-2-13b': llama_2_13b, # llama-3 'llama-3': llama_3, 'llama-3-8b': llama_3_8b, 'llama-3-70b': llama_3_70b, # llama-3.1 'llama-3.1': 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 ### 'mistral-7b': mistral_7b, 'mixtral-8x7b': mixtral_8x7b, 'mixtral-8x22b': mixtral_8x22b, 'mistral-nemo': mistral_nemo, ### NousResearch ### 'mixtral-8x7b-dpo': mixtral_8x7b_dpo, 'hermes-3': hermes_3, 'yi-34b': yi_34b, ### Microsoft ### 'phi_3_medium-4k': phi_3_medium_4k, 'phi-3.5-mini': phi_3_5_mini, ### Google ### # gemini 'gemini': gemini, 'gemini-pro': gemini_pro, 'gemini-flash': gemini_flash, # gemma 'gemma-2b': gemma_2b, 'gemma-2b-9b': gemma_2b_9b, 'gemma-2b-27b': gemma_2b_27b, ### Anthropic ### 'claude-2': claude_2, 'claude-2.0': claude_2_0, 'claude-2.1': claude_2_1, # claude 3 'claude-3': claude_3, 'claude-3-opus': claude_3_opus, 'claude-3-sonnet': claude_3_sonnet, 'claude-3-haiku': claude_3_haiku, # claude 3.5 'claude-3.5': claude_3_5, 'claude-3.5-sonnet': claude_3_5_sonnet, ### Reka AI ### 'reka-core': reka_core, ### Blackbox AI ### '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': qwen, 'qwen-1.5-14b': qwen_1_5_14b, 'qwen-1.5-72b': qwen_1_5_72b, 'qwen-1.5-110b': qwen_1_5_110b, 'qwen-2-72b': qwen_2_72b, '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, 'solar-10-7b': solar_10_7b, 'solar-pro': solar_pro, ### Inflection ### 'pi': pi, ### DeepSeek ### 'deepseek': deepseek, ### Together ### 'sh-n-7b': sh_n_7b, ### Yorickvp ### 'llava-13b': llava_13b, ### WizardLM ### 'wizardlm-2-7b': wizardlm_2_7b, 'wizardlm-2-8x22b': wizardlm_2_8x22b, ### OpenBMB ### 'minicpm-llama-3-v2.5': minicpm_llama_3_v2_5, ### Lzlv ### 'lzlv-70b': lzlv_70b, ### OpenChat ### 'openchat-3.6-8b': openchat_3_6_8b, ### Phind ### 'phind-codellama-34b-v2': phind_codellama_34b_v2, ### Cognitive Computations ### 'dolphin-2.9.1-llama-3-70b': dolphin_2_9_1_llama_3_70b, ### x.ai ### 'grok-2': grok_2, 'grok-2-mini': grok_2_mini, ############# ### 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, 'flux-pixel': flux_pixel, 'flux-4o': flux_4o, 'flux-schnell': flux_schnell, ### ### 'dalle': dalle, 'dalle-2': dalle_2, 'dalle-3': dalle_3, 'dalle-mini': dalle_mini, 'emi': emi, 'any-dark': any_dark, } _all_models = list(ModelUtils.convert.keys())