mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-11-03 10:30:57 +08:00
06546649db
* **Provider/Blackbox.py**
* Raise `RateLimitError` when `"You have reached your request limit for the hour"` substring is detected
* **Provider/Copilot.py**
* Convert class to `AsyncGeneratorProvider`; rename `create_completion` → `create_async_generator`
* Swap `curl_cffi.requests.Session` for `AsyncSession`; reduce default timeout to **30 s**
* Fully async websocket flow (`await session.ws_connect`, `await wss.send/recv/close`)
* Emit new response types: `TitleGeneration`, `SourceLink`, aggregated `Sources`
* Track request completion with `done` flag; collect citations in `sources` dict
* **Provider/DuckDuckGo.py**
* Replace `duckduckgo_search.DDGS` with `duckai.DuckAI`
* Change base class to `AbstractProvider`; drop nodriver‑based auth
* **Provider/PollinationsAI.py**
* Re‑build text/audio model lists ensuring uniqueness; remove unused `extra_text_models`
* Fix image seed logic (`i==1` for first retry); propagate streaming `error` field via `ResponseError`
* **Provider/hf_space**
* **New file** `LMArenaProvider.py` implementing async queue/stream client
* Register `LMArenaProvider` in `hf_space/__init__.py`; delete `G4F` import
* **Provider/needs_auth/CopilotAccount.py**
* Inherit order changed to `Copilot, AsyncAuthedProvider`
* Refactor token & cookie propagation; add `cookies_to_dict` helper
* **Provider/needs_auth/OpenaiChat.py**
* Parse reasoning thoughts/summary; yield `Reasoning` responses
* Tighten access‑token validation and nodriver JS evaluations (`return_by_value`)
* Extend `Conversation` with `p` and `thoughts_summary`
* **providers/response.py**
* Add `SourceLink` response class returning single formatted citation link
* **providers/base_provider.py**
* Serialize `AuthResult` with custom `json.dump` to handle non‑serializable fields
* Gracefully skip empty cache files when loading auth data
* **image/copy_images.py**
* Ignore file extensions longer than 4 chars when inferring type
* **requests/__init__.py**
* Use `return_by_value=True` for `navigator.userAgent` extraction
* **models.py**
* Remove `G4F` from model provider lists; update `janus_pro_7b` best providers
* **GUI server/api.py**
* Stream `SuggestedFollowups` to client (`"suggestions"` event)
* **GUI static assets**
* **style.css**: bold chat title, add `.suggestions` styles, remove padding from `.chat-body`
* **chat.v1.js**
* Capture `suggestions` packets, render buttons, and send as quick replies
* Re‑order finish‑reason logic; adjust token count placement and system‑prompt toggling
* **chat-top-panel / footer** interactions updated accordingly
* **gui/client/static/js/chat.v1.js** & **css** further UI refinements (scroll handling, token counting, hide prompt toggle)
* Minor updates across multiple files to match new async interfaces and headers (`userAgent`, `raise_for_status`)
1012 lines
23 KiB
Python
1012 lines
23 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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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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AllenAI,
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ARTA,
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Blackbox,
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ChatGLM,
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ChatGptEs,
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Cloudflare,
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Copilot,
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DDG,
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DeepInfraChat,
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Dynaspark,
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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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Glider,
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Goabror,
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ImageLabs,
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Jmuz,
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LambdaChat,
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Liaobots,
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OIVSCode,
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PerplexityLabs,
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Pi,
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PollinationsAI,
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PollinationsImage,
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TypeGPT,
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TeachAnything,
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Websim,
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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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GigaChat,
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HailuoAI,
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HuggingChat,
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HuggingFace,
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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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Reka,
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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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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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DDG,
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Blackbox,
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Copilot,
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DeepInfraChat,
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AllenAI,
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PollinationsAI,
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TypeGPT,
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OIVSCode,
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ChatGptEs,
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Free2GPT,
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FreeGpt,
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Glider,
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Dynaspark,
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OpenaiChat,
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Jmuz,
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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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OIVSCode,
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TypeGPT,
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DeepInfraChat,
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PollinationsAI,
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Dynaspark,
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AllenAI,
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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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##########################
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### Text//Audio/Vision ###
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##########################
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### OpenAI ###
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# gpt-3.5
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gpt_3_5_turbo = Model(
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name = 'gpt-3.5-turbo',
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base_provider = 'OpenAI'
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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([DDG, Jmuz, ChatGptEs, PollinationsAI, Yqcloud, Goabror, Copilot, OpenaiChat, Liaobots])
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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, Jmuz, ChatGptEs, PollinationsAI, Liaobots, 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([DDG, Blackbox, ChatGptEs, TypeGPT, PollinationsAI, OIVSCode, Liaobots, Jmuz, OpenaiChat])
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)
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gpt_4o_audio = AudioModel(
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name = 'gpt-4o-audio',
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base_provider = 'OpenAI',
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best_provider = PollinationsAI
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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([Blackbox, 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 = IterListProvider([DDG, Blackbox, Liaobots])
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)
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### GigaChat ###
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gigachat = Model(
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name = 'GigaChat:latest',
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base_provider = 'gigachat',
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best_provider = GigaChat
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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 2-4 ###
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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 = Cloudflare
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)
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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([Jmuz, 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 = Jmuz
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)
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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, Glider, PollinationsAI, AllenAI, Jmuz, 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 = IterListProvider([Glider, AllenAI, Jmuz])
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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 = IterListProvider([AllenAI, Jmuz])
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)
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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 = Glider
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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([Jmuz, 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, Jmuz])
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)
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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([Blackbox, DDG, DeepInfraChat, LambdaChat, PollinationsAI, Jmuz, HuggingChat, HuggingFace])
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)
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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([Cloudflare, PollinationsAI])
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)
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### Mistral ###
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mixtral_8x7b = Model(
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name = "mixtral-8x7b",
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base_provider = "Mistral",
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best_provider = Jmuz
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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",
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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",
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best_provider = IterListProvider([PollinationsAI, HuggingChat, HuggingFace])
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)
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mixtral_small_24b = Model(
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name = "mixtral-small-24b",
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base_provider = "Mistral",
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best_provider = IterListProvider([Blackbox, DDG, DeepInfraChat])
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)
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### NousResearch ###
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hermes_3 = Model(
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name = "hermes-3",
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base_provider = "NousResearch",
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best_provider = LambdaChat
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)
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### Microsoft ###
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# phi
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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 = 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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# 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 = IterListProvider([DeepInfraChat, Jmuz])
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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-exp
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gemini_exp = Model(
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name = 'gemini-exp',
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base_provider = 'Google',
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best_provider = Jmuz
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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 DeepMind',
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best_provider = IterListProvider([Free2GPT, FreeGpt, TeachAnything, Websim, Dynaspark, Jmuz, 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 DeepMind',
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best_provider = IterListProvider([Free2GPT, FreeGpt, TeachAnything, Websim, Jmuz, 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 DeepMind',
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best_provider = IterListProvider([Dynaspark, GeminiPro, Gemini])
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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 DeepMind',
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best_provider = 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 DeepMind',
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best_provider = Gemini
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)
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### Anthropic ###
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# claude 3
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claude_3_haiku = Model(
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name = 'claude-3-haiku',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([DDG, Jmuz])
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)
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# claude 3.5
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claude_3_5_sonnet = Model(
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name = 'claude-3.5-sonnet',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([Blackbox, Jmuz, Liaobots])
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)
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# claude 3.7
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claude_3_7_sonnet = Model(
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name = 'claude-3.7-sonnet',
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base_provider = 'Anthropic',
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best_provider = IterListProvider([Blackbox, Liaobots])
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)
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### Reka AI ###
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reka_core = Model(
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name = 'reka-core',
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base_provider = 'Reka AI',
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best_provider = Reka
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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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)
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blackboxai_pro = Model(
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name = 'blackboxai-pro',
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base_provider = 'Blackbox AI',
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best_provider = Blackbox
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)
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### CohereForAI ###
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command_r = Model(
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name = 'command-r',
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base_provider = 'CohereForAI',
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best_provider = HuggingSpace
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)
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command_r_plus = Model(
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name = 'command-r-plus',
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base_provider = 'CohereForAI',
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best_provider = IterListProvider([HuggingSpace, HuggingChat])
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)
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command_r7b = Model(
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name = 'command-r7b',
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base_provider = 'CohereForAI',
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best_provider = HuggingSpace
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)
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command_a = Model(
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name = 'command-a',
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base_provider = 'CohereForAI',
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best_provider = HuggingSpace
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)
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### Qwen ###
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# qwen-1.5
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qwen_1_5_7b = Model(
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name = 'qwen-1.5-7b',
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base_provider = 'Qwen',
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best_provider = Cloudflare
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)
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# qwen-2
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qwen_2_72b = Model(
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name = 'qwen-2-72b',
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base_provider = 'Qwen',
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best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
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)
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qwen_2_vl_7b = VisionModel(
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name = "qwen-2-vl-7b",
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base_provider = 'Qwen',
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best_provider = HuggingFaceAPI
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)
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# qwen-2.5
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qwen_2_5 = Model(
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name = 'qwen-2.5',
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base_provider = 'Qwen',
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best_provider = HuggingSpace
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)
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qwen_2_5_72b = Model(
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name = 'qwen-2.5-72b',
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base_provider = 'Qwen',
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best_provider = Jmuz
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)
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qwen_2_5_coder_32b = Model(
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name = 'qwen-2.5-coder-32b',
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base_provider = 'Qwen',
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best_provider = IterListProvider([PollinationsAI, Jmuz, HuggingChat])
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)
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qwen_2_5_1m = Model(
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name = 'qwen-2.5-1m',
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base_provider = 'Qwen',
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best_provider = HuggingSpace
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)
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qwen_2_5_max = Model(
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name = 'qwen-2-5-max',
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base_provider = 'Qwen',
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best_provider = HuggingSpace
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)
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### qwq/qvq ###
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qwq_32b = Model(
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name = 'qwq-32b',
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base_provider = 'Qwen',
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best_provider = IterListProvider([Blackbox, Jmuz, HuggingChat])
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)
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qvq_72b = VisionModel(
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name = 'qvq-72b',
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base_provider = 'Qwen',
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best_provider = HuggingSpace
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)
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### Inflection ###
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pi = Model(
|
|
name = 'pi',
|
|
base_provider = 'Inflection',
|
|
best_provider = Pi
|
|
)
|
|
|
|
### DeepSeek ###
|
|
deepseek_chat = Model(
|
|
name = 'deepseek-chat',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([Blackbox, Jmuz])
|
|
)
|
|
|
|
deepseek_v3 = Model(
|
|
name = 'deepseek-v3',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([Blackbox, DeepInfraChat, LambdaChat, PollinationsAI, OIVSCode, TypeGPT, Liaobots])
|
|
)
|
|
|
|
deepseek_r1 = Model(
|
|
name = 'deepseek-r1',
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([Blackbox, DeepInfraChat, Glider, LambdaChat, PollinationsAI, TypeGPT, Liaobots, Jmuz, HuggingChat, HuggingFace])
|
|
)
|
|
|
|
janus_pro_7b = VisionModel(
|
|
name = DeepseekAI_JanusPro7b.default_model,
|
|
base_provider = 'DeepSeek',
|
|
best_provider = IterListProvider([DeepseekAI_JanusPro7b])
|
|
)
|
|
|
|
### x.ai ###
|
|
grok_3 = Model(
|
|
name = 'grok-3',
|
|
base_provider = 'x.ai',
|
|
best_provider = Grok
|
|
)
|
|
|
|
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 = PerplexityLabs
|
|
)
|
|
|
|
### Nvidia ###
|
|
nemotron_70b = Model(
|
|
name = 'nemotron-70b',
|
|
base_provider = 'Nvidia',
|
|
best_provider = IterListProvider([LambdaChat, HuggingChat, HuggingFace])
|
|
)
|
|
|
|
### Databricks ###
|
|
dbrx_instruct = Model(
|
|
name = 'dbrx-instruct',
|
|
base_provider = 'Databricks',
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
### THUDM ###
|
|
glm_4 = Model(
|
|
name = 'glm-4',
|
|
base_provider = 'THUDM',
|
|
best_provider = ChatGLM
|
|
)
|
|
|
|
### MiniMax ###
|
|
mini_max = Model(
|
|
name = "MiniMax",
|
|
base_provider = "MiniMax",
|
|
best_provider = HailuoAI
|
|
)
|
|
|
|
### 01-ai ###
|
|
yi_34b = Model(
|
|
name = "yi-34b",
|
|
base_provider = "01-ai",
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
### Cognitive Computations ###
|
|
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
|
|
)
|
|
|
|
### OpenBMB ###
|
|
minicpm_2_5 = Model(
|
|
name = "minicpm-2.5",
|
|
base_provider = "OpenBMB",
|
|
best_provider = DeepInfraChat
|
|
)
|
|
|
|
### Ai2 ###
|
|
olmo_1_7b = Model(
|
|
name = "olmo-1-7b",
|
|
base_provider = "Ai2",
|
|
best_provider = AllenAI
|
|
)
|
|
|
|
olmo_2_13b = Model(
|
|
name = "olmo-2-13b",
|
|
base_provider = "Ai2",
|
|
best_provider = AllenAI
|
|
)
|
|
|
|
olmo_2_32b = Model(
|
|
name = "olmo-2-32b",
|
|
base_provider = "Ai2",
|
|
best_provider = AllenAI
|
|
)
|
|
|
|
olmo_4_synthetic = VisionModel(
|
|
name = "olmo-4-synthetic",
|
|
base_provider = "Ai2",
|
|
best_provider = AllenAI
|
|
)
|
|
|
|
tulu_3_1_8b = Model(
|
|
name = "tulu-3-1-8b",
|
|
base_provider = "Ai2",
|
|
best_provider = AllenAI
|
|
)
|
|
|
|
tulu_3_70b = Model(
|
|
name = "tulu-3-70b",
|
|
base_provider = "Ai2",
|
|
best_provider = AllenAI
|
|
)
|
|
|
|
tulu_3_405b = Model(
|
|
name = "tulu-3-405b",
|
|
base_provider = "Ai2",
|
|
best_provider = AllenAI
|
|
)
|
|
|
|
### 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 = IterListProvider([PollinationsAI, TypeGPT])
|
|
)
|
|
|
|
|
|
#############
|
|
### Image ###
|
|
#############
|
|
|
|
### Stability AI ###
|
|
sdxl_turbo = ImageModel(
|
|
name = 'sdxl-turbo',
|
|
base_provider = 'Stability AI',
|
|
best_provider = IterListProvider([PollinationsImage, ImageLabs])
|
|
)
|
|
|
|
sd_3_5 = ImageModel(
|
|
name = 'sd-3.5',
|
|
base_provider = 'Stability AI',
|
|
best_provider = HuggingSpace
|
|
)
|
|
|
|
### Black Forest Labs ###
|
|
flux = ImageModel(
|
|
name = 'flux',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = IterListProvider([Blackbox, PollinationsImage, Websim, HuggingSpace, ARTA])
|
|
)
|
|
|
|
flux_pro = ImageModel(
|
|
name = 'flux-pro',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = PollinationsImage
|
|
)
|
|
|
|
flux_dev = ImageModel(
|
|
name = 'flux-dev',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = IterListProvider([PollinationsImage, HuggingSpace, HuggingChat, HuggingFace])
|
|
)
|
|
|
|
flux_schnell = ImageModel(
|
|
name = 'flux-schnell',
|
|
base_provider = 'Black Forest Labs',
|
|
best_provider = IterListProvider([PollinationsImage, HuggingSpace, HuggingChat, HuggingFace])
|
|
)
|
|
|
|
|
|
### OpenAI ###
|
|
dall_e_3 = ImageModel(
|
|
name = 'dall-e-3',
|
|
base_provider = 'OpenAI',
|
|
best_provider = IterListProvider([PollinationsImage, CopilotAccount, OpenaiAccount, MicrosoftDesigner, BingCreateImages])
|
|
)
|
|
|
|
### Midjourney ###
|
|
midjourney = ImageModel(
|
|
name = 'midjourney',
|
|
base_provider = 'Midjourney',
|
|
best_provider = PollinationsImage
|
|
)
|
|
|
|
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-3.5
|
|
gpt_3_5_turbo.name: gpt_3_5_turbo,
|
|
|
|
# gpt-4
|
|
gpt_4.name: gpt_4,
|
|
|
|
# gpt-4o
|
|
gpt_4o.name: gpt_4o,
|
|
gpt_4o_mini.name: gpt_4o_mini,
|
|
gpt_4o_audio.name: gpt_4o_audio,
|
|
|
|
# o1
|
|
o1.name: o1,
|
|
o1_mini.name: o1_mini,
|
|
|
|
# o3
|
|
o3_mini.name: o3_mini,
|
|
|
|
### Meta ###
|
|
meta.name: meta,
|
|
|
|
# llama-2
|
|
llama_2_7b.name: llama_2_7b,
|
|
|
|
# llama-3
|
|
llama_3_8b.name: llama_3_8b,
|
|
llama_3_70b.name: llama_3_70b,
|
|
|
|
# llama-3.1
|
|
llama_3_1_8b.name: llama_3_1_8b,
|
|
llama_3_1_70b.name: llama_3_1_70b,
|
|
llama_3_1_405b.name: llama_3_1_405b,
|
|
|
|
# llama-3.2
|
|
llama_3_2_1b.name: llama_3_2_1b,
|
|
llama_3_2_3b.name: llama_3_2_3b,
|
|
llama_3_2_11b.name: llama_3_2_11b,
|
|
llama_3_2_90b.name: llama_3_2_90b,
|
|
|
|
# llama-3.3
|
|
llama_3_3_70b.name: llama_3_3_70b,
|
|
|
|
# llama-4
|
|
llama_4_scout.name: llama_4_scout,
|
|
|
|
### Mistral ###
|
|
mixtral_8x7b.name: mixtral_8x7b,
|
|
mixtral_8x22b.name: mixtral_8x22b,
|
|
mistral_nemo.name: mistral_nemo,
|
|
mixtral_small_24b.name: mixtral_small_24b,
|
|
|
|
### NousResearch ###
|
|
hermes_3.name: hermes_3,
|
|
|
|
### Microsoft ###
|
|
# phi
|
|
phi_3_5_mini.name: phi_3_5_mini,
|
|
phi_4.name: phi_4,
|
|
|
|
# wizardlm
|
|
wizardlm_2_7b.name: wizardlm_2_7b,
|
|
wizardlm_2_8x22b.name: wizardlm_2_8x22b,
|
|
|
|
### Google ###
|
|
### Gemini
|
|
"gemini": gemini,
|
|
gemini.name: gemini,
|
|
gemini_exp.name: gemini_exp,
|
|
gemini_1_5_pro.name: gemini_1_5_pro,
|
|
gemini_1_5_flash.name: gemini_1_5_flash,
|
|
gemini_2_0_flash.name: gemini_2_0_flash,
|
|
gemini_2_0_flash_thinking.name: gemini_2_0_flash_thinking,
|
|
gemini_2_0_flash_thinking_with_apps.name: gemini_2_0_flash_thinking_with_apps,
|
|
|
|
### Anthropic ###
|
|
# claude 3
|
|
claude_3_haiku.name: claude_3_haiku,
|
|
|
|
# claude 3.5
|
|
claude_3_5_sonnet.name: claude_3_5_sonnet,
|
|
|
|
# claude 3.7
|
|
claude_3_7_sonnet.name: claude_3_7_sonnet,
|
|
|
|
### Reka AI ###
|
|
reka_core.name: reka_core,
|
|
|
|
### Blackbox AI ###
|
|
blackboxai.name: blackboxai,
|
|
blackboxai_pro.name: blackboxai_pro,
|
|
|
|
### CohereForAI ###
|
|
command_r.name: command_r,
|
|
command_r_plus.name: command_r_plus,
|
|
command_r7b.name: command_r7b,
|
|
command_a.name: command_a,
|
|
|
|
### GigaChat ###
|
|
gigachat.name: gigachat,
|
|
|
|
### Qwen ###
|
|
# qwen-1.5
|
|
qwen_1_5_7b.name: qwen_1_5_7b,
|
|
|
|
# qwen-2
|
|
qwen_2_72b.name: qwen_2_72b,
|
|
qwen_2_vl_7b.name: qwen_2_vl_7b,
|
|
|
|
# qwen-2.5
|
|
qwen_2_5.name: qwen_2_5,
|
|
qwen_2_5_72b.name: qwen_2_5_72b,
|
|
qwen_2_5_coder_32b.name: qwen_2_5_coder_32b,
|
|
qwen_2_5_1m.name: qwen_2_5_1m,
|
|
qwen_2_5_max.name: qwen_2_5_max,
|
|
|
|
# qwq/qvq
|
|
qwq_32b.name: qwq_32b,
|
|
qvq_72b.name: qvq_72b,
|
|
|
|
### Inflection ###
|
|
pi.name: pi,
|
|
|
|
### x.ai ###
|
|
grok_3.name: grok_3,
|
|
|
|
### Perplexity AI ###
|
|
sonar.name: sonar,
|
|
sonar_pro.name: sonar_pro,
|
|
sonar_reasoning.name: sonar_reasoning,
|
|
sonar_reasoning_pro.name: sonar_reasoning_pro,
|
|
r1_1776.name: r1_1776,
|
|
|
|
### DeepSeek ###
|
|
deepseek_chat.name: deepseek_chat,
|
|
deepseek_v3.name: deepseek_v3,
|
|
deepseek_r1.name: deepseek_r1,
|
|
|
|
### Nvidia ###
|
|
nemotron_70b.name: nemotron_70b,
|
|
|
|
### Databricks ###
|
|
dbrx_instruct.name: dbrx_instruct,
|
|
|
|
### THUDM ###
|
|
glm_4.name: glm_4,
|
|
|
|
## MiniMax ###
|
|
mini_max.name: mini_max,
|
|
|
|
## 01-ai ###
|
|
yi_34b.name: yi_34b,
|
|
|
|
### Cognitive Computations ###
|
|
dolphin_2_6.name: dolphin_2_6,
|
|
dolphin_2_9.name: dolphin_2_9,
|
|
|
|
### DeepInfra ###
|
|
airoboros_70b.name: airoboros_70b,
|
|
|
|
### Lizpreciatior ###
|
|
lzlv_70b.name: lzlv_70b,
|
|
|
|
### OpenBMB ###
|
|
minicpm_2_5.name: minicpm_2_5,
|
|
|
|
### Ai2 ###
|
|
olmo_1_7b.name: olmo_1_7b,
|
|
olmo_2_13b.name: olmo_2_13b,
|
|
olmo_2_32b.name: olmo_2_32b,
|
|
olmo_4_synthetic.name: olmo_4_synthetic,
|
|
tulu_3_1_8b.name: tulu_3_1_8b,
|
|
tulu_3_70b.name: tulu_3_70b,
|
|
tulu_3_405b.name: tulu_3_405b,
|
|
|
|
### Liquid AI ###
|
|
lfm_40b.name: lfm_40b,
|
|
|
|
### Uncensored AI ###
|
|
evil.name: evil,
|
|
|
|
#############
|
|
### Image ###
|
|
#############
|
|
|
|
### Stability AI ###
|
|
sdxl_turbo.name: sdxl_turbo,
|
|
sd_3_5.name: sd_3_5,
|
|
|
|
### Flux AI ###
|
|
flux.name: flux,
|
|
flux_pro.name: flux_pro,
|
|
flux_dev.name: flux_dev,
|
|
flux_schnell.name: flux_schnell,
|
|
|
|
### OpenAI ###
|
|
dall_e_3.name: dall_e_3,
|
|
|
|
### Midjourney ###
|
|
midjourney.name: midjourney,
|
|
}
|
|
|
|
|
|
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.name: [sd_3_5, [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 his providers
|
|
__models__ = {
|
|
model.name: (model, providers)
|
|
for model, providers in [
|
|
(model, [provider for provider in model.best_provider.providers if provider.working]
|
|
if isinstance(model.best_provider, IterListProvider)
|
|
else [model.best_provider]
|
|
if model.best_provider is not None and model.best_provider.working
|
|
else [])
|
|
for model in ModelUtils.convert.values()]
|
|
if providers
|
|
}
|
|
_all_models = list(__models__.keys())
|