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Modified
docs/providers-and-models.md
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-2
Modified
g4f/Provider/DeepInfraChat.py
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Modified
g4f/models.py
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XFEstudio/gpt4free
Update g4f/Provider/DeepInfraChat.py
95821b5b
代码差异
3 个文件
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@@ -49,7 +49,7 @@ This document provides an overview of various AI providers and models, including
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|[playground.ai.cloudflare.com](https://playground.ai.cloudflare.com)|[Automatic cookies](https://playground.ai.cloudflare.com)|`g4f.Provider.Cloudflare`|`llama-2-7b, llama-3-8b, llama-3.1-8b, llama-3.2-1b, qwen-1.5-7b`|❌|❌|✔||❌|
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|[copilot.microsoft.com](https://copilot.microsoft.com)|Optional API key|`g4f.Provider.Copilot`|`gpt-4, gpt-4o`|❌|❌|✔||
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|[duckduckgo.com/aichat](https://duckduckgo.com/aichat)|No auth required|`g4f.Provider.DDG`|`gpt-4, gpt-4o-mini, claude-3-haiku, llama-3.1-70b, mixtral-8x7b`|❌|❌|✔||
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|[deepinfra.com/chat](https://deepinfra.com/chat)|No auth required|`g4f.Provider.DeepInfraChat`|`llama-3.1-8b, llama-3.1-70b, deepseek-chat, qwq-32b, wizardlm-2-8x22b, wizardlm-2-7b, qwen-2.5-72b, qwen-2.5-coder-32b, nemotron-70b`|❌|❌|✔||
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|[deepinfra.com/chat](https://deepinfra.com/chat)|No auth required|`g4f.Provider.DeepInfraChat`|`llama-3.1-8b, llama-3.2-90b, llama-3.3-70b, deepseek-v3, mixtral-small-28b, deepseek-r1, phi-4, wizardlm-2-8x22b, qwen-2.5-72b`|❌|❌|✔||
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|[chat10.free2gpt.xyz](https://chat10.free2gpt.xyz)|No auth required|`g4f.Provider.Free2GPT`|`mistral-7b`|❌|❌|✔||
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|[freegptsnav.aifree.site](https://freegptsnav.aifree.site)|No auth required|`g4f.Provider.FreeGpt`|`gemini-1.5-pro`|❌|❌|✔||
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|[app.giz.ai/assistant](https://app.giz.ai/assistant)|No auth required|`g4f.Provider.GizAI`|`gemini-1.5-flash`|❌|❌|✔||
@@ -149,7 +149,7 @@ This document provides an overview of various AI providers and models, including
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|mixtral-small-28b|Mistral|2+ Providers|[mistral.ai](https://mistral.ai/news/mixtral-small-28b/)|
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|hermes-2-dpo|NousResearch|2+ Providers|[huggingface.co](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO)|
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|phi-3.5-mini|Microsoft|1+ Providers|[huggingface.co](https://huggingface.co/microsoft/Phi-3.5-mini-instruct)|
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|wizardlm-2-7b|Microsoft|1+ Providers|[wizardlm.github.io](https://wizardlm.github.io/WizardLM2/)|
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|phi-4|Microsoft|1+ Providers|[techcommunity.microsoft.com](https://techcommunity.microsoft.com/blog/aiplatformblog/introducing-phi-4-microsoft%E2%80%99s-newest-small-language-model-specializing-in-comple/4357090)|
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|wizardlm-2-8x22b|Microsoft|2+ Providers|[wizardlm.github.io](https://wizardlm.github.io/WizardLM2/)|
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|gemini|Google DeepMind|1+|[deepmind.google](http://deepmind.google/technologies/gemini/)|
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|gemini-exp|Google DeepMind|1+ Providers|[blog.google](https://blog.google/feed/gemini-exp-1206/)|
@@ -10,30 +10,30 @@ class DeepInfraChat(OpenaiTemplate):
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default_model = 'meta-llama/Llama-3.3-70B-Instruct-Turbo'
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models = [
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'meta-llama/Llama-3.3-70B-Instruct',
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'meta-llama/Meta-Llama-3.1-8B-Instruct',
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'meta-llama/Llama-3.2-90B-Vision-Instruct',
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default_model,
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'meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo',
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'deepseek-ai/DeepSeek-V3',
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'Qwen/QwQ-32B-Preview',
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'mistralai/Mistral-Small-24B-Instruct-2501',
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'deepseek-ai/DeepSeek-R1',
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'deepseek-ai/DeepSeek-R1-Distill-Llama-70B',
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'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B',
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'microsoft/phi-4',
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'microsoft/WizardLM-2-8x22B',
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'microsoft/WizardLM-2-7B',
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'Qwen/Qwen2.5-72B-Instruct',
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'Qwen/Qwen2.5-Coder-32B-Instruct',
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'nvidia/Llama-3.1-Nemotron-70B-Instruct',
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]
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model_aliases = {
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"llama-3.3-70b": "meta-llama/Llama-3.3-70B-Instruct",
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"llama-3.1-8b": "meta-llama/Meta-Llama-3.1-8B-Instruct",
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"llama-3.2-90b": "meta-llama/Llama-3.2-90B-Vision-Instruct",
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"llama-3.3-70b": "meta-llama/Llama-3.3-70B-Instruct-Turbo",
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"llama-3.1-70b": "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
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"deepseek-v3": "deepseek-ai/DeepSeek-V3",
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"qwq-32b": "Qwen/QwQ-32B-Preview",
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"mixtral-small-28b": "mistralai/Mistral-Small-24B-Instruct-2501",
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"deepseek-r1": "deepseek-ai/DeepSeek-R1",
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"deepseek-r1": "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
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"deepseek-r1": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
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"phi-4": "microsoft/phi-4",
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"wizardlm-2-8x22b": "microsoft/WizardLM-2-8x22B",
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"wizardlm-2-7b": "microsoft/WizardLM-2-7B",
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"qwen-2.5-72b": "Qwen/Qwen2.5-72B-Instruct",
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"qwen-2.5-coder-32b": "Qwen/Qwen2.5-Coder-32B-Instruct",
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"nemotron-70b": "nvidia/Llama-3.1-Nemotron-70B-Instruct",
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}
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@classmethod
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cls,
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model: str,
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messages: Messages,
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stream: bool = True,
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top_p: float = 0.9,
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temperature: float = 0.7,
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max_tokens: int = None,
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headers: dict = {},
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**kwargs
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) -> AsyncResult:
@@ -230,7 +230,7 @@ llama_3_2_11b = VisionModel(
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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 = Jmuz
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best_provider = IterListProvider([DeepInfraChat, Jmuz])
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)
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# llama 3.3
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mixtral_small_28b = Model(
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name = "mixtral-small-28b",
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base_provider = "Mistral",
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best_provider = IterListProvider([Blackbox, BlackboxAPI])
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best_provider = IterListProvider([Blackbox, BlackboxAPI, DeepInfraChat])
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)
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### NousResearch ###
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best_provider = HuggingChat
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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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phi_4 = Model(
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name = "phi-4",
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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_8x22b = Model(
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name = 'wizardlm-2-8x22b',
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base_provider = 'Microsoft',
@@ -420,7 +420,7 @@ qwen_2_5_72b = Model(
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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([DeepInfraChat, PollinationsAI, Jmuz, HuggingChat])
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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-demo',
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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, BlackboxAPI, DeepInfraChat, Jmuz, HuggingChat])
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best_provider = IterListProvider([Blackbox, BlackboxAPI, Jmuz, HuggingChat])
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)
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qvq_72b = VisionModel(
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name = 'qvq-72b',
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deepseek_r1 = Model(
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name = 'deepseek-r1',
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base_provider = 'DeepSeek',
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best_provider = IterListProvider([Blackbox, BlackboxAPI, Glider, PollinationsAI, Jmuz, CablyAI, Liaobots, HuggingChat, HuggingFace])
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best_provider = IterListProvider([Blackbox, BlackboxAPI, DeepInfraChat, Glider, PollinationsAI, Jmuz, CablyAI, Liaobots, HuggingChat, HuggingFace])
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)
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### x.ai ###
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nemotron_70b = Model(
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name = 'nemotron-70b',
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base_provider = 'Nvidia',
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best_provider = IterListProvider([DeepInfraChat, HuggingChat, HuggingFace])
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best_provider = IterListProvider([HuggingChat, HuggingFace])
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)
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### Databricks ###
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### Microsoft ###
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# phi
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phi_3_5_mini.name: phi_3_5_mini,
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phi_4.name: phi_4,
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# wizardlm
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wizardlm_2_7b.name: wizardlm_2_7b,
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wizardlm_2_8x22b.name: wizardlm_2_8x22b,
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### Google ###