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0a070bdf10ac3385b04492a8d68173f61266e818
gpt4free/g4f/models.py
H Lohaus 0a070bdf10 feat: introduce AnyProvider & LM Arena, overhaul model/provider logic (#2925) 
* feat: introduce AnyProvider & LM Arena, overhaul model/provider logic

- **Provider additions & removals**
  - Added `Provider/LMArenaProvider.py` with full async stream implementation and vision model support
  - Registered `LMArenaProvider` in `Provider/__init__.py`; removed old `hf_space/LMArenaProvider.py`
  - Created `providers/any_provider.py`; registers `AnyProvider` dynamically in `Provider`
- **Provider framework enhancements**
  - `providers/base_provider.py`
    - Added `video_models` and `audio_models` attributes
  - `providers/retry_provider.py`
    - Introduced `is_content()` helper; now treats `AudioResponse` as stream content
- **Cloudflare provider refactor**
  - `Provider/Cloudflare.py`
    - Re‑implemented `get_models()` with `read_models()` helper, `fallback_models`, robust nodriver/curl handling and model‑name cleaning
- **Other provider tweaks**
  - `Provider/Copilot.py` – removed `"reasoning"` alias and initial `setOptions` WS message
  - `Provider/PollinationsAI.py` & `PollinationsImage.py`
    - Converted `audio_models` from list to dict, adjusted usage checks and labels
  - `Provider/hf/__init__.py` – applies `model_aliases` remap before dispatch
  - `Provider/hf_space/DeepseekAI_JanusPro7b.py` – now merges media before upload
  - `needs_auth/Gemini.py` – dropped obsolete Gemini model entries
  - `needs_auth/GigaChat.py` – added lowercase `"gigachat"` alias
- **API & client updates**
  - Replaced `ProviderUtils` with new `Provider` map usage throughout API and GUI server
  - Integrated `AnyProvider` as default fallback in `g4f/client` sync & async flows
  - API endpoints now return counts of providers per model and filter by `x_ignored` header
- **GUI improvements**
  - Updated JS labels with emoji icons, provider ignore logic, model count display
- **Model registry**
  - Renamed base model `"GigaChat:latest"` ➜ `"gigachat"` in `models.py`
- **Miscellaneous**
  - Added audio/video flags to GUI provider list
  - Tightened error propagation in `retry_provider.raise_exceptions`

* Fix unittests

* fix: handle None conversation when accessing provider-specific data

- Modified `AnyProvider` class in `g4f/providers/any_provider.py`
- Updated logic to check if `conversation` is not None before accessing `provider.__name__` attribute
- Wrapped `getattr(conversation, provider.__name__, None)` block in an additional `if conversation is not None` condition
- Changed `setattr(conversation, provider.__name__, chunk)` to use `chunk.get_dict()` instead of the object directly
- Ensured consistent use of `JsonConversation` when modifying or assigning `conversation` data

* ```
feat: add provider string conversion & update IterListProvider call

- In g4f/client/__init__.py, within both Completions and AsyncCompletions, added a check to convert the provider from a string using convert_to_provider(provider) when applicable.
- In g4f/providers/any_provider.py, removed the second argument (False) from the IterListProvider constructor call in the async for loop.
```

---------

Co-authored-by: hlohaus <983577+hlohaus@users.noreply.github.com>
2025-04-18 14:10:51 +02:00

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from __future__ import annotations
from dataclasses import dataclass
from .Provider import IterListProvider, ProviderType
from .Provider import (
### No Auth Required ###
AllenAI,
ARTA,
Blackbox,
ChatGLM,
ChatGptEs,
Cloudflare,
Copilot,
DDG,
DeepInfraChat,
Dynaspark,
Free2GPT,
FreeGpt,
HuggingSpace,
Grok,
DeepseekAI_JanusPro7b,
Glider,
Goabror,
ImageLabs,
Jmuz,
LambdaChat,
Liaobots,
OIVSCode,
PerplexityLabs,
Pi,
PollinationsAI,
PollinationsImage,
TypeGPT,
TeachAnything,
Websim,
Yqcloud,
### Needs Auth ###
BingCreateImages,
CopilotAccount,
Gemini,
GeminiPro,
GigaChat,
HailuoAI,
HuggingChat,
HuggingFace,
HuggingFaceAPI,
MetaAI,
MicrosoftDesigner,
OpenaiAccount,
OpenaiChat,
Reka,
)
@dataclass(unsafe_hash=True)
class Model:
"""
Represents a machine learning model configuration.
Attributes:
name (str): Name of the model.
base_provider (str): Default provider for the model.
best_provider (ProviderType): The preferred provider for the model, typically with retry logic.
"""
name: str
base_provider: str
best_provider: ProviderType = None
@staticmethod
def __all__() -> list[str]:
"""Returns a list of all model names."""
return _all_models
class ImageModel(Model):
pass
class AudioModel(Model):
pass
class VideoModel(Model):
pass
class VisionModel(Model):
pass
### Default ###
default = Model(
name = "",
base_provider = "",
best_provider = IterListProvider([
DDG,
Blackbox,
Copilot,
DeepInfraChat,
AllenAI,
PollinationsAI,
TypeGPT,
OIVSCode,
ChatGptEs,
Free2GPT,
FreeGpt,
Glider,
Dynaspark,
OpenaiChat,
Jmuz,
Cloudflare,
])
)
default_vision = VisionModel(
name = "",
base_provider = "",
best_provider = IterListProvider([
Blackbox,
OIVSCode,
TypeGPT,
DeepInfraChat,
PollinationsAI,
Dynaspark,
AllenAI,
HuggingSpace,
GeminiPro,
HuggingFaceAPI,
CopilotAccount,
OpenaiAccount,
Gemini,
], shuffle=False)
)
##########################
### Text//Audio/Vision ###
##########################
### OpenAI ###
# gpt-3.5
gpt_3_5_turbo = Model(
name = 'gpt-3.5-turbo',
base_provider = 'OpenAI'
)
# gpt-4
gpt_4 = Model(
name = 'gpt-4',
base_provider = 'OpenAI',
best_provider = IterListProvider([DDG, Jmuz, ChatGptEs, PollinationsAI, Yqcloud, Goabror, Copilot, OpenaiChat, Liaobots])
)
# gpt-4o
gpt_4o = VisionModel(
name = 'gpt-4o',
base_provider = 'OpenAI',
best_provider = IterListProvider([Blackbox, Jmuz, ChatGptEs, PollinationsAI, Liaobots, OpenaiChat])
)
gpt_4o_mini = Model(
name = 'gpt-4o-mini',
base_provider = 'OpenAI',
best_provider = IterListProvider([DDG, Blackbox, ChatGptEs, TypeGPT, PollinationsAI, OIVSCode, Liaobots, Jmuz, OpenaiChat])
)
gpt_4o_audio = AudioModel(
name = 'gpt-4o-audio',
base_provider = 'OpenAI',
best_provider = PollinationsAI
)
# o1
o1 = Model(
name = 'o1',
base_provider = 'OpenAI',
best_provider = IterListProvider([Blackbox, Copilot, OpenaiAccount])
)
o1_mini = Model(
name = 'o1-mini',
base_provider = 'OpenAI',
best_provider = OpenaiAccount
)
# o3
o3_mini = Model(
name = 'o3-mini',
base_provider = 'OpenAI',
best_provider = IterListProvider([DDG, Blackbox, Liaobots])
)
### GigaChat ###
gigachat = Model(
name = 'gigachat',
base_provider = 'gigachat',
best_provider = GigaChat
)
### Meta ###
meta = Model(
name = "meta-ai",
base_provider = "Meta",
best_provider = MetaAI
)
### llama 2-4 ###
llama_2_7b = Model(
name = "llama-2-7b",
base_provider = "Meta Llama",
best_provider = Cloudflare
)
llama_3_8b = Model(
name = "llama-3-8b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Jmuz, Cloudflare])
)
llama_3_70b = Model(
name = "llama-3-70b",
base_provider = "Meta Llama",
best_provider = Jmuz
)
llama_3_1_8b = Model(
name = "llama-3.1-8b",
base_provider = "Meta Llama",
best_provider = IterListProvider([DeepInfraChat, Glider, PollinationsAI, AllenAI, Jmuz, Cloudflare])
)
llama_3_1_70b = Model(
name = "llama-3.1-70b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Glider, AllenAI, Jmuz])
)
llama_3_1_405b = Model(
name = "llama-3.1-405b",
base_provider = "Meta Llama",
best_provider = IterListProvider([AllenAI, Jmuz])
)
llama_3_2_1b = Model(
name = "llama-3.2-1b",
base_provider = "Meta Llama",
best_provider = Cloudflare
)
llama_3_2_3b = Model(
name = "llama-3.2-3b",
base_provider = "Meta Llama",
best_provider = Glider
)
llama_3_2_11b = VisionModel(
name = "llama-3.2-11b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Jmuz, HuggingChat, HuggingFace])
)
llama_3_2_90b = Model(
name = "llama-3.2-90b",
base_provider = "Meta Llama",
best_provider = IterListProvider([DeepInfraChat, Jmuz])
)
llama_3_3_70b = Model(
name = "llama-3.3-70b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, DDG, DeepInfraChat, LambdaChat, PollinationsAI, Jmuz, HuggingChat, HuggingFace])
)
llama_4_scout = Model(
name = "llama-4-scout",
base_provider = "Meta Llama",
best_provider = IterListProvider([Cloudflare, PollinationsAI])
)
### Mistral ###
mixtral_8x7b = Model(
name = "mixtral-8x7b",
base_provider = "Mistral",
best_provider = Jmuz
)
mixtral_8x22b = Model(
name = "mixtral-8x22b",
base_provider = "Mistral",
best_provider = DeepInfraChat
)
mistral_nemo = Model(
name = "mistral-nemo",
base_provider = "Mistral",
best_provider = IterListProvider([PollinationsAI, HuggingChat, HuggingFace])
)
mixtral_small_24b = Model(
name = "mixtral-small-24b",
base_provider = "Mistral",
best_provider = IterListProvider([Blackbox, DDG, DeepInfraChat])
)
### NousResearch ###
hermes_3 = Model(
name = "hermes-3",
base_provider = "NousResearch",
best_provider = LambdaChat
)
### Microsoft ###
# phi
phi_3_5_mini = Model(
name = "phi-3.5-mini",
base_provider = "Microsoft",
best_provider = HuggingChat
)
phi_4 = Model(
name = "phi-4",
base_provider = "Microsoft",
best_provider = IterListProvider([DeepInfraChat, PollinationsAI, HuggingSpace])
)
# wizardlm
wizardlm_2_7b = Model(
name = 'wizardlm-2-7b',
base_provider = 'Microsoft',
best_provider = DeepInfraChat
)
wizardlm_2_8x22b = Model(
name = 'wizardlm-2-8x22b',
base_provider = 'Microsoft',
best_provider = IterListProvider([DeepInfraChat, Jmuz])
)
### Google DeepMind ###
# gemini
gemini = Model(
name = 'gemini-2.0',
base_provider = 'Google',
best_provider = Gemini
)
# gemini-exp
gemini_exp = Model(
name = 'gemini-exp',
base_provider = 'Google',
best_provider = Jmuz
)
# gemini-1.5
gemini_1_5_flash = Model(
name = 'gemini-1.5-flash',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Free2GPT, FreeGpt, TeachAnything, Websim, Dynaspark, Jmuz, GeminiPro])
)
gemini_1_5_pro = Model(
name = 'gemini-1.5-pro',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Free2GPT, FreeGpt, TeachAnything, Websim, Jmuz, GeminiPro])
)
# gemini-2.0
gemini_2_0_flash = Model(
name = 'gemini-2.0-flash',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Dynaspark, GeminiPro, Gemini])
)
gemini_2_0_flash_thinking = Model(
name = 'gemini-2.0-flash-thinking',
base_provider = 'Google DeepMind',
best_provider = Gemini
)
gemini_2_0_flash_thinking_with_apps = Model(
name = 'gemini-2.0-flash-thinking-with-apps',
base_provider = 'Google DeepMind',
best_provider = Gemini
)
### Anthropic ###
# claude 3
claude_3_haiku = Model(
name = 'claude-3-haiku',
base_provider = 'Anthropic',
best_provider = IterListProvider([DDG, Jmuz])
)
# claude 3.5
claude_3_5_sonnet = Model(
name = 'claude-3.5-sonnet',
base_provider = 'Anthropic',
best_provider = IterListProvider([Blackbox, Jmuz, Liaobots])
)
# claude 3.7
claude_3_7_sonnet = Model(
name = 'claude-3.7-sonnet',
base_provider = 'Anthropic',
best_provider = IterListProvider([Blackbox, Liaobots])
)
### Reka AI ###
reka_core = Model(
name = 'reka-core',
base_provider = 'Reka AI',
best_provider = Reka
)
### Blackbox AI ###
blackboxai = Model(
name = 'blackboxai',
base_provider = 'Blackbox AI',
best_provider = Blackbox
)
blackboxai_pro = Model(
name = 'blackboxai-pro',
base_provider = 'Blackbox AI',
best_provider = Blackbox
)
### 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([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])
)
qwen_2_vl_7b = VisionModel(
name = "qwen-2-vl-7b",
base_provider = 'Qwen',
best_provider = HuggingFaceAPI
)
# qwen-2.5
qwen_2_5 = Model(
name = 'qwen-2.5',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
qwen_2_5_72b = Model(
name = 'qwen-2.5-72b',
base_provider = 'Qwen',
best_provider = Jmuz
)
qwen_2_5_coder_32b = Model(
name = 'qwen-2.5-coder-32b',
base_provider = 'Qwen',
best_provider = IterListProvider([PollinationsAI, Jmuz, 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
)
### qwq/qvq ###
qwq_32b = Model(
name = 'qwq-32b',
base_provider = 'Qwen',
best_provider = IterListProvider([Blackbox, Jmuz, HuggingChat])
)
qvq_72b = VisionModel(
name = 'qvq-72b',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
### Inflection ###
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 [p for p in providers if p.working]
}
_all_models = list(__models__.keys())
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