mirror of
https://github.com/xtekky/gpt4free.git
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d824d77d65
- Updated `PollinationsAI.py` to strip trailing periods and newlines from the prompt before encoding. - Modified the encoding of the prompt to remove trailing percent signs after URL encoding. - Simplified the audio response handling in `PollinationsAI.py` by removing unnecessary checks and yielding chunks directly. - Renamed `ARTA.py` to `deprecated/ARTA.py` and updated import paths accordingly in `__init__.py`. - Changed the `working` status of the `ARTA` class to `False` to indicate it is deprecated. - Enhanced the `Video` class in `Video.py` to include aspect ratio handling and improved URL response caching. - Updated the `RequestConfig` class to use a dictionary for storing URLs associated with prompts. - Removed references to the `ARTA` provider in various files, including `models.py` and `any_provider.py`. - Adjusted the `best_provider` assignments in `models.py` to exclude `ARTA` and include `HuggingFaceMedia` where applicable. - Updated the response handling in `Video.py` to yield cached responses when available.
1025 lines
24 KiB
Python
1025 lines
24 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, List, Optional
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from .Provider import IterListProvider, ProviderType
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from .Provider import (
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### No Auth Required ###
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Blackbox,
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Chatai,
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Cloudflare,
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Copilot,
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DeepInfraChat,
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Free2GPT,
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FreeGpt,
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HuggingSpace,
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Grok,
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DeepseekAI_JanusPro7b,
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DeepSeekAPI,
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ImageLabs,
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LambdaChat,
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OIVSCodeSer2,
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OIVSCodeSer0501,
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OpenAIFM,
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PerplexityLabs,
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Pi,
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PollinationsAI,
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PollinationsImage,
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TeachAnything,
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Together,
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Websim,
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WeWordle,
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Yqcloud,
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### Needs Auth ###
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BingCreateImages,
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CopilotAccount,
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Gemini,
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GeminiPro,
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HailuoAI,
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HuggingChat,
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HuggingFace,
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HuggingFaceMedia,
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HuggingFaceAPI,
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MetaAI,
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MicrosoftDesigner,
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OpenaiAccount,
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OpenaiChat,
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)
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class ModelRegistry:
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"""Simplified registry for automatic model discovery"""
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_models: Dict[str, 'Model'] = {}
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_aliases: Dict[str, str] = {}
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@classmethod
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def register(cls, model: 'Model', aliases: List[str] = None):
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"""Register a model and optional aliases"""
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if model.name:
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cls._models[model.name] = model
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if aliases:
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for alias in aliases:
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cls._aliases[alias] = model.name
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@classmethod
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def get(cls, name: str) -> Optional['Model']:
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"""Get model by name or alias"""
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if name in cls._models:
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return cls._models[name]
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if name in cls._aliases:
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return cls._models[cls._aliases[name]]
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return None
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@classmethod
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def all_models(cls) -> Dict[str, 'Model']:
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"""Get all registered models"""
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return cls._models.copy()
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@classmethod
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def clear(cls):
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"""Clear registry (for testing)"""
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cls._models.clear()
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cls._aliases.clear()
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@classmethod
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def list_models_by_provider(cls, provider_name: str) -> List[str]:
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"""List all models that use specific provider"""
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return [name for name, model in cls._models.items()
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if provider_name in str(model.best_provider)]
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@classmethod
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def validate_all_models(cls) -> Dict[str, List[str]]:
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"""Validate all models and return issues"""
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issues = {}
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for name, model in cls._models.items():
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model_issues = []
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if not model.name:
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model_issues.append("Empty name")
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if not model.base_provider:
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model_issues.append("Empty base_provider")
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if model.best_provider is None:
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model_issues.append("No best_provider")
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if model_issues:
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issues[name] = model_issues
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return issues
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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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def __post_init__(self):
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"""Auto-register model after initialization"""
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if self.name:
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ModelRegistry.register(self)
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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 list(ModelRegistry.all_models().keys())
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class ImageModel(Model):
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pass
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class AudioModel(Model):
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pass
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class VideoModel(Model):
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pass
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class VisionModel(Model):
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pass
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### Default ###
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default = Model(
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name = "",
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base_provider = "",
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best_provider = IterListProvider([
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OIVSCodeSer0501,
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OIVSCodeSer2,
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Blackbox,
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Copilot,
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DeepInfraChat,
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LambdaChat,
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PollinationsAI,
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Together,
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Free2GPT,
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FreeGpt,
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Chatai,
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WeWordle,
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OpenaiChat,
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Cloudflare,
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])
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)
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default_vision = VisionModel(
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name = "",
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base_provider = "",
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best_provider = IterListProvider([
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Blackbox,
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DeepInfraChat,
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OIVSCodeSer2,
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OIVSCodeSer0501,
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PollinationsAI,
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Together,
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HuggingSpace,
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GeminiPro,
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HuggingFaceAPI,
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CopilotAccount,
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OpenaiAccount,
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Gemini,
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], shuffle=False)
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)
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# gpt-4
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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 = IterListProvider([Blackbox, PollinationsAI, Copilot, Yqcloud, WeWordle, OpenaiChat])
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)
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# gpt-4o
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gpt_4o = VisionModel(
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name = 'gpt-4o',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([Blackbox, PollinationsAI, OpenaiChat])
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)
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gpt_4o_mini = Model(
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name = 'gpt-4o-mini',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([Blackbox, OIVSCodeSer2, PollinationsAI, Chatai, OpenaiChat])
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)
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gpt_4o_mini_audio = AudioModel(
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name = 'gpt-4o-mini-audio',
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base_provider = 'OpenAI',
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best_provider = PollinationsAI
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)
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gpt_4o_mini_tts = AudioModel(
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name = 'gpt-4o-mini-tts',
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base_provider = 'OpenAI',
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best_provider = OpenAIFM
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)
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# o1
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o1 = Model(
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name = 'o1',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([Copilot, OpenaiAccount])
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)
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o1_mini = Model(
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name = 'o1-mini',
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base_provider = 'OpenAI',
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best_provider = OpenaiAccount
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)
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# o3
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o3_mini = Model(
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name = 'o3-mini',
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base_provider = 'OpenAI',
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best_provider = OpenaiChat
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)
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o3_mini_high = Model(
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name = 'o3-mini-high',
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base_provider = 'OpenAI',
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best_provider = OpenaiAccount
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)
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# o4
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o4_mini = Model(
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name = 'o4-mini',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([PollinationsAI, OpenaiChat])
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)
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o4_mini_high = Model(
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name = 'o4-mini-high',
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base_provider = 'OpenAI',
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best_provider = OpenaiChat
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)
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# gpt-4.1
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gpt_4_1 = Model(
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name = 'gpt-4.1',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([PollinationsAI, OpenaiChat])
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)
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gpt_4_1_mini = Model(
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name = 'gpt-4.1-mini',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([Blackbox, OIVSCodeSer0501, PollinationsAI])
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)
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gpt_4_1_nano = Model(
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name = 'gpt-4.1-nano',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([Blackbox, PollinationsAI])
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)
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gpt_4_5 = Model(
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name = 'gpt-4.5',
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base_provider = 'OpenAI',
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best_provider = OpenaiChat
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)
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# dall-e
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dall_e_3 = ImageModel(
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name = 'dall-e-3',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([PollinationsImage, CopilotAccount, OpenaiAccount, MicrosoftDesigner, BingCreateImages])
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)
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gpt_image = ImageModel(
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name = 'gpt-image',
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base_provider = 'OpenAI',
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best_provider = IterListProvider([PollinationsImage])
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)
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### Meta ###
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meta = Model(
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name = "meta-ai",
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base_provider = "Meta",
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best_provider = MetaAI
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)
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# llama 2
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llama_2_7b = Model(
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name = "llama-2-7b",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([Cloudflare])
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)
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llama_2_70b = Model(
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name = "llama-2-70b",
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base_provider = "Meta Llama",
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best_provider = Together
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)
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# llama-3
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llama_3_8b = Model(
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name = "llama-3-8b",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([Together, Cloudflare])
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)
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llama_3_70b = Model(
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name = "llama-3-70b",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([Together])
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)
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# llama-3.1
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llama_3_1_8b = Model(
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name = "llama-3.1-8b",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([DeepInfraChat, Together, Cloudflare])
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)
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llama_3_1_70b = Model(
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name = "llama-3.1-70b",
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base_provider = "Meta Llama",
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best_provider = Together
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)
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llama_3_1_405b = Model(
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name = "llama-3.1-405b",
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base_provider = "Meta Llama",
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best_provider = Together
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)
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# llama-3.2
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llama_3_2_1b = Model(
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name = "llama-3.2-1b",
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base_provider = "Meta Llama",
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best_provider = Cloudflare
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)
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llama_3_2_3b = Model(
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name = "llama-3.2-3b",
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base_provider = "Meta Llama",
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best_provider = Together
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)
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llama_3_2_11b = VisionModel(
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name = "llama-3.2-11b",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([Together, HuggingChat, HuggingFace])
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)
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llama_3_2_90b = Model(
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name = "llama-3.2-90b",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([DeepInfraChat, Together])
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)
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# llama-3.3
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llama_3_3_70b = Model(
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name = "llama-3.3-70b",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([DeepInfraChat, LambdaChat, PollinationsAI, Together, HuggingChat, HuggingFace])
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)
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# llama-4
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llama_4_scout = Model(
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name = "llama-4-scout",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([DeepInfraChat, LambdaChat, PollinationsAI, Together, Cloudflare])
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)
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llama_4_maverick = Model(
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name = "llama-4-maverick",
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base_provider = "Meta Llama",
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best_provider = IterListProvider([DeepInfraChat, LambdaChat, Together])
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)
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### MistralAI ###
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mistral_7b = Model(
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name = "mistral-7b",
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base_provider = "Mistral AI",
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best_provider = Together
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)
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mixtral_8x7b = Model(
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name = "mixtral-8x7b",
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base_provider = "Mistral AI",
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best_provider = Together
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)
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mixtral_8x22b = Model(
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name = "mixtral-8x22b",
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base_provider = "Mistral AI",
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best_provider = DeepInfraChat
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)
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mistral_nemo = Model(
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name = "mistral-nemo",
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base_provider = "Mistral AI",
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best_provider = IterListProvider([HuggingChat, HuggingFace])
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)
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mistral_small_24b = Model(
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name = "mistral-small-24b",
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base_provider = "Mistral AI",
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best_provider = IterListProvider([DeepInfraChat, Together])
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)
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mistral_small_3_1_24b = Model(
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name = "mistral-small-3.1-24b",
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base_provider = "Mistral AI",
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best_provider = PollinationsAI
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)
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### NousResearch ###
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# hermes-2
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hermes_2_dpo = Model(
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name = "hermes-2-dpo",
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base_provider = "NousResearch",
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best_provider = Together
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)
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# phi-3.5
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phi_3_5_mini = Model(
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name = "phi-3.5-mini",
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base_provider = "Microsoft",
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best_provider = HuggingChat
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)
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# phi-4
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phi_4 = Model(
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name = "phi-4",
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base_provider = "Microsoft",
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best_provider = IterListProvider([DeepInfraChat, PollinationsAI, HuggingSpace])
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)
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phi_4_multimodal = VisionModel(
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name = "phi-4-multimodal",
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base_provider = "Microsoft",
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best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
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)
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phi_4_reasoning_plus = Model(
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name = "phi-4-reasoning-plus",
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base_provider = "Microsoft",
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best_provider = DeepInfraChat
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)
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# wizardlm
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wizardlm_2_7b = Model(
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name = 'wizardlm-2-7b',
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base_provider = 'Microsoft',
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best_provider = DeepInfraChat
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)
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wizardlm_2_8x22b = Model(
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name = 'wizardlm-2-8x22b',
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base_provider = 'Microsoft',
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best_provider = DeepInfraChat
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)
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### Google DeepMind ###
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# gemini
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gemini = Model(
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name = 'gemini-2.0',
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base_provider = 'Google',
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best_provider = Gemini
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)
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# gemini-1.5
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gemini_1_5_flash = Model(
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name = 'gemini-1.5-flash',
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base_provider = 'Google',
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best_provider = IterListProvider([Free2GPT, FreeGpt, TeachAnything, Websim, GeminiPro])
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)
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gemini_1_5_pro = Model(
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name = 'gemini-1.5-pro',
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base_provider = 'Google',
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best_provider = IterListProvider([Free2GPT, FreeGpt, TeachAnything, Websim, GeminiPro])
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)
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# gemini-2.0
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gemini_2_0_flash = Model(
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name = 'gemini-2.0-flash',
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base_provider = 'Google',
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best_provider = IterListProvider([Gemini, GeminiPro])
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)
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gemini_2_0_flash_thinking = Model(
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name = 'gemini-2.0-flash-thinking',
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base_provider = 'Google',
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best_provider = IterListProvider([PollinationsAI, Gemini])
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)
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gemini_2_0_flash_thinking_with_apps = Model(
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name = 'gemini-2.0-flash-thinking-with-apps',
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base_provider = 'Google',
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best_provider = Gemini
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)
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# gemini-2.5
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gemini_2_5_flash = Model(
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name = 'gemini-2.5-flash',
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base_provider = 'Google',
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best_provider = IterListProvider([PollinationsAI, Gemini])
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)
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gemini_2_5_pro = Model(
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name = 'gemini-2.5-pro',
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base_provider = 'Google',
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best_provider = IterListProvider([Gemini])
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)
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# gemma-2
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gemma_2_27b = Model(
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name = 'gemma-2-27b',
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base_provider = 'Google',
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best_provider = Together
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)
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# gemma-3
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gemma_3_12b = Model(
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name = 'gemma-3-12b',
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base_provider = 'Google',
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best_provider = DeepInfraChat
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)
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gemma_3_27b = Model(
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name = 'gemma-3-27b',
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base_provider = 'Google',
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best_provider = DeepInfraChat
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)
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### Blackbox AI ###
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blackboxai = Model(
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name = 'blackboxai',
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base_provider = 'Blackbox AI',
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best_provider = Blackbox
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|
)
|
|
|
|
### CohereForAI ###
|
|
command_r = Model(
|
|
name = 'command-r',
|
|
base_provider = 'CohereForAI',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
command_r_plus = Model(
|
|
name = 'command-r-plus',
|
|
base_provider = 'CohereForAI',
|
|
best_provider = IterListProvider([PollinationsAI, HuggingSpace, HuggingChat])
|
|
)
|
|
|
|
command_r7b = Model(
|
|
name = 'command-r7b',
|
|
base_provider = 'CohereForAI',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
command_a = Model(
|
|
name = 'command-a',
|
|
base_provider = 'CohereForAI',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
### Qwen ###
|
|
# qwen-1.5
|
|
qwen_1_5_7b = Model(
|
|
name = 'qwen-1.5-7b',
|
|
base_provider = 'Qwen',
|
|
best_provider = Cloudflare
|
|
)
|
|
|
|
# qwen-2
|
|
qwen_2_72b = Model(
|
|
name = 'qwen-2-72b',
|
|
base_provider = 'Qwen',
|
|
best_provider = IterListProvider([DeepInfraChat, HuggingSpace, Together])
|
|
)
|
|
|
|
qwen_2_vl_7b = VisionModel(
|
|
name = "qwen-2-vl-7b",
|
|
base_provider = 'Qwen',
|
|
best_provider = HuggingFaceAPI
|
|
)
|
|
|
|
qwen_2_vl_72b = VisionModel(
|
|
name = "qwen-2-vl-72b",
|
|
base_provider = 'Qwen',
|
|
best_provider = Together
|
|
)
|
|
|
|
# qwen-2.5
|
|
qwen_2_5 = Model(
|
|
name = 'qwen-2.5',
|
|
base_provider = 'Qwen',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
qwen_2_5_7b = Model(
|
|
name = 'qwen-2.5-7b',
|
|
base_provider = 'Qwen',
|
|
best_provider = Together
|
|
)
|
|
|
|
qwen_2_5_72b = Model(
|
|
name = 'qwen-2.5-72b',
|
|
base_provider = 'Qwen',
|
|
best_provider = Together
|
|
)
|
|
|
|
qwen_2_5_coder_32b = Model(
|
|
name = 'qwen-2.5-coder-32b',
|
|
base_provider = 'Qwen',
|
|
best_provider = IterListProvider([PollinationsAI, LambdaChat, Together, HuggingChat])
|
|
)
|
|
|
|
qwen_2_5_1m = Model(
|
|
name = 'qwen-2.5-1m',
|
|
base_provider = 'Qwen',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
qwen_2_5_max = Model(
|
|
name = 'qwen-2.5-max',
|
|
base_provider = 'Qwen',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
qwen_2_5_vl_72b = Model(
|
|
name = 'qwen-2.5-vl-72b',
|
|
base_provider = 'Qwen',
|
|
best_provider = Together
|
|
)
|
|
|
|
# qwen3
|
|
qwen_3_235b = Model(
|
|
name = 'qwen-3-235b',
|
|
base_provider = 'Qwen',
|
|
best_provider = IterListProvider([DeepInfraChat, Together, HuggingSpace])
|
|
)
|
|
|
|
qwen_3_32b = Model(
|
|
name = 'qwen-3-32b',
|
|
base_provider = 'Qwen',
|
|
best_provider = IterListProvider([DeepInfraChat, LambdaChat, HuggingSpace])
|
|
)
|
|
|
|
qwen_3_30b = Model(
|
|
name = 'qwen-3-30b',
|
|
base_provider = 'Qwen',
|
|
best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
|
|
)
|
|
|
|
qwen_3_14b = Model(
|
|
name = 'qwen-3-14b',
|
|
base_provider = 'Qwen',
|
|
best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
|
|
)
|
|
|
|
qwen_3_4b = Model(
|
|
name = 'qwen-3-4b',
|
|
base_provider = 'Qwen',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
qwen_3_1_7b = Model(
|
|
name = 'qwen-3-1.7b',
|
|
base_provider = 'Qwen',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
qwen_3_0_6b = Model(
|
|
name = 'qwen-3-0.6b',
|
|
base_provider = 'Qwen',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
### qwq/qvq ###
|
|
qwq_32b = Model(
|
|
name = 'qwq-32b',
|
|
base_provider = 'Qwen',
|
|
best_provider = IterListProvider([DeepInfraChat, PollinationsAI, Together, HuggingChat])
|
|
)
|
|
|
|
### DeepSeek ###
|
|
# deepseek-v3
|
|
deepseek_v3 = Model(
|
|
name = 'deepseek-v3',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([DeepInfraChat, PollinationsAI, Together])
|
|
)
|
|
|
|
# deepseek-r1
|
|
deepseek_r1 = Model(
|
|
name = 'deepseek-r1',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([DeepInfraChat, LambdaChat, PollinationsAI, Together, HuggingChat, HuggingFace])
|
|
)
|
|
|
|
deepseek_r1_turbo = Model(
|
|
name = 'deepseek-r1-turbo',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
deepseek_r1_distill_llama_70b = Model(
|
|
name = 'deepseek-r1-distill-llama-70b',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([DeepInfraChat, Together, PollinationsAI])
|
|
)
|
|
|
|
deepseek_r1_distill_qwen_1_5b = Model(
|
|
name = 'deepseek-r1-distill-qwen-1.5b',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = Together
|
|
)
|
|
|
|
deepseek_r1_distill_qwen_14b = Model(
|
|
name = 'deepseek-r1-distill-qwen-14b',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = Together
|
|
)
|
|
|
|
deepseek_r1_distill_qwen_32b = Model(
|
|
name = 'deepseek-r1-distill-qwen-32b',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([DeepInfraChat, PollinationsAI])
|
|
)
|
|
|
|
# deepseek-v2
|
|
deepseek_prover_v2 = Model(
|
|
name = 'deepseek-prover-v2',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
deepseek_prover_v2_671b = Model(
|
|
name = 'deepseek-prover-v2-671b',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
# deepseek-v3-0324
|
|
deepseek_v3_0324 = Model(
|
|
name = 'deepseek-v3-0324',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([DeepInfraChat, Together, LambdaChat, PollinationsAI])
|
|
)
|
|
|
|
# deepseek-r1-0528
|
|
deepseek_r1_0528 = Model(
|
|
name = 'deepseek-r1-0528',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([DeepInfraChat, LambdaChat, Together])
|
|
)
|
|
|
|
# janus
|
|
janus_pro_7b = VisionModel(
|
|
name = DeepseekAI_JanusPro7b.default_model,
|
|
base_provider = 'DeepSeek',
|
|
best_provider = DeepseekAI_JanusPro7b
|
|
)
|
|
|
|
### x.ai ###
|
|
grok_2 = Model(
|
|
name = 'grok-2',
|
|
base_provider = 'x.ai',
|
|
best_provider = Grok
|
|
)
|
|
|
|
grok_3 = Model(
|
|
name = 'grok-3',
|
|
base_provider = 'x.ai',
|
|
best_provider = Grok
|
|
)
|
|
|
|
grok_3_mini = Model(
|
|
name = 'grok-3-mini',
|
|
base_provider = 'x.ai',
|
|
best_provider = PollinationsAI
|
|
)
|
|
|
|
grok_3_r1 = Model(
|
|
name = 'grok-3-r1',
|
|
base_provider = 'x.ai',
|
|
best_provider = Grok
|
|
)
|
|
|
|
### Perplexity AI ###
|
|
sonar = Model(
|
|
name = 'sonar',
|
|
base_provider = 'Perplexity AI',
|
|
best_provider = PerplexityLabs
|
|
)
|
|
|
|
sonar_pro = Model(
|
|
name = 'sonar-pro',
|
|
base_provider = 'Perplexity AI',
|
|
best_provider = PerplexityLabs
|
|
)
|
|
|
|
sonar_reasoning = Model(
|
|
name = 'sonar-reasoning',
|
|
base_provider = 'Perplexity AI',
|
|
best_provider = PerplexityLabs
|
|
)
|
|
|
|
sonar_reasoning_pro = Model(
|
|
name = 'sonar-reasoning-pro',
|
|
base_provider = 'Perplexity AI',
|
|
best_provider = PerplexityLabs
|
|
)
|
|
|
|
r1_1776 = Model(
|
|
name = 'r1-1776',
|
|
base_provider = 'Perplexity AI',
|
|
best_provider = IterListProvider([Together, PerplexityLabs])
|
|
)
|
|
|
|
### Nvidia ###
|
|
nemotron_70b = Model(
|
|
name = 'nemotron-70b',
|
|
base_provider = 'Nvidia',
|
|
best_provider = IterListProvider([LambdaChat, Together, HuggingChat, HuggingFace])
|
|
)
|
|
|
|
### Cognitive Computations ###
|
|
# dolphin-2
|
|
dolphin_2_6 = Model(
|
|
name = "dolphin-2.6",
|
|
base_provider = "Cognitive Computations",
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
dolphin_2_9 = Model(
|
|
name = "dolphin-2.9",
|
|
base_provider = "Cognitive Computations",
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
### DeepInfra ###
|
|
airoboros_70b = Model(
|
|
name = "airoboros-70b",
|
|
base_provider = "DeepInfra",
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
### Lizpreciatior ###
|
|
lzlv_70b = Model(
|
|
name = "lzlv-70b",
|
|
base_provider = "Lizpreciatior",
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
### Liquid AI ###
|
|
lfm_40b = Model(
|
|
name = "lfm-40b",
|
|
base_provider = "Liquid AI",
|
|
best_provider = LambdaChat
|
|
)
|
|
|
|
### Uncensored AI ###
|
|
evil = Model(
|
|
name = 'evil',
|
|
base_provider = 'Evil Mode - Experimental',
|
|
best_provider = PollinationsAI
|
|
)
|
|
|
|
sdxl_turbo = ImageModel(
|
|
name = 'sdxl-turbo',
|
|
base_provider = 'Stability AI',
|
|
best_provider = IterListProvider([HuggingFaceMedia, PollinationsImage, ImageLabs])
|
|
)
|
|
|
|
sd_3_5_large = ImageModel(
|
|
name = 'sd-3.5-large',
|
|
base_provider = 'Stability AI',
|
|
best_provider = IterListProvider([HuggingFaceMedia, HuggingSpace])
|
|
)
|
|
|
|
### Black Forest Labs ###
|
|
flux = ImageModel(
|
|
name = 'flux',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = IterListProvider([HuggingFaceMedia, PollinationsImage, Websim, Together, HuggingSpace])
|
|
)
|
|
|
|
flux_pro = ImageModel(
|
|
name = 'flux-pro',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = IterListProvider([PollinationsImage, Together])
|
|
)
|
|
|
|
flux_dev = ImageModel(
|
|
name = 'flux-dev',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = IterListProvider([PollinationsImage, HuggingSpace, Together, HuggingChat, HuggingFace])
|
|
)
|
|
|
|
flux_schnell = ImageModel(
|
|
name = 'flux-schnell',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = IterListProvider([PollinationsImage, Together, HuggingChat, HuggingFace])
|
|
)
|
|
|
|
flux_redux = ImageModel(
|
|
name = 'flux-redux',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = Together
|
|
)
|
|
|
|
flux_depth = ImageModel(
|
|
name = 'flux-depth',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = Together
|
|
)
|
|
|
|
flux_canny = ImageModel(
|
|
name = 'flux-canny',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = Together
|
|
)
|
|
|
|
flux_kontext_max = ImageModel(
|
|
name = 'flux-kontext-max',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = Together
|
|
)
|
|
|
|
flux_dev_lora = ImageModel(
|
|
name = 'flux-dev-lora',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = Together
|
|
)
|
|
|
|
flux_kontext_pro = ImageModel(
|
|
name = 'flux-kontext-pro',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = Together
|
|
)
|
|
|
|
class ModelUtils:
|
|
"""
|
|
Utility class for mapping string identifiers to Model instances.
|
|
Now uses automatic discovery instead of manual mapping.
|
|
"""
|
|
|
|
convert: Dict[str, Model] = {}
|
|
|
|
@classmethod
|
|
def refresh(cls):
|
|
"""Refresh the model registry and update convert"""
|
|
cls.convert = ModelRegistry.all_models()
|
|
|
|
@classmethod
|
|
def get_model(cls, name: str) -> Optional[Model]:
|
|
"""Get model by name or alias"""
|
|
return ModelRegistry.get(name)
|
|
|
|
@classmethod
|
|
def register_alias(cls, alias: str, model_name: str):
|
|
"""Register an alias for a model"""
|
|
ModelRegistry._aliases[alias] = model_name
|
|
|
|
# Register special aliases after all models are created
|
|
ModelRegistry._aliases["gemini"] = "gemini-2.0"
|
|
|
|
# Fill the convert dictionary
|
|
ModelUtils.convert = ModelRegistry.all_models()
|
|
|
|
# Demo models configuration
|
|
demo_models = {
|
|
llama_3_2_11b.name: [llama_3_2_11b, [HuggingChat]],
|
|
qwen_2_vl_7b.name: [qwen_2_vl_7b, [HuggingFaceAPI]],
|
|
deepseek_r1.name: [deepseek_r1, [HuggingFace, PollinationsAI]],
|
|
janus_pro_7b.name: [janus_pro_7b, [HuggingSpace]],
|
|
command_r.name: [command_r, [HuggingSpace]],
|
|
command_r_plus.name: [command_r_plus, [HuggingSpace]],
|
|
command_r7b.name: [command_r7b, [HuggingSpace]],
|
|
qwen_2_5_coder_32b.name: [qwen_2_5_coder_32b, [HuggingFace]],
|
|
qwq_32b.name: [qwq_32b, [HuggingFace]],
|
|
llama_3_3_70b.name: [llama_3_3_70b, [HuggingFace]],
|
|
sd_3_5_large.name: [sd_3_5_large, [HuggingSpace, HuggingFace]],
|
|
flux_dev.name: [flux_dev, [PollinationsImage, HuggingFace, HuggingSpace]],
|
|
flux_schnell.name: [flux_schnell, [PollinationsImage, HuggingFace, HuggingSpace]],
|
|
}
|
|
|
|
# Create a list of all models and their providers
|
|
def _get_working_providers(model: Model) -> List:
|
|
"""Get list of working providers for a model"""
|
|
if model.best_provider is None:
|
|
return []
|
|
|
|
if isinstance(model.best_provider, IterListProvider):
|
|
return [p for p in model.best_provider.providers if p.working]
|
|
|
|
return [model.best_provider] if model.best_provider.working else []
|
|
|
|
# Generate __models__ using the auto-discovered models
|
|
__models__ = {
|
|
name: (model, _get_working_providers(model))
|
|
for name, model in ModelRegistry.all_models().items()
|
|
if name and _get_working_providers(model)
|
|
}
|
|
|
|
# Generate _all_models list
|
|
_all_models = list(__models__.keys())
|
|
|
|
# Backward compatibility - ensure Model.__all__() returns the correct list
|
|
Model.__all__ = staticmethod(lambda: _all_models)
|