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add modelscope api support
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yrk
parent
97894f1603
commit
f6ce4e16f8
4 changed files with 78 additions and 52 deletions
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@ -873,9 +873,9 @@ def add_known_models(model_cost_map: Optional[Dict] = None):
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elif value.get("litellm_provider") == "heroku":
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heroku_models.add(key)
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elif value.get("litellm_provider") == "dashscope":
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dashscope_models.append(key)
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dashscope_models.add(key)
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elif value.get("litellm_provider") == "modelscope":
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modelscope_models.append(key)
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modelscope_models.add(key)
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elif value.get("litellm_provider") == "moonshot":
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moonshot_models.add(key)
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elif value.get("litellm_provider") == "publicai":
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@ -1239,30 +1239,14 @@ from .llms.topaz.common_utils import TopazModelInfo
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# OpenAIGPTConfig, OpenAIGPT5Config, etc. are lazy loaded - instances will be created on first access
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from .llms.xai.common_utils import XAIModelInfo
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from .llms.azure.chat.gpt_transformation import AzureOpenAIConfig
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from .llms.azure.completion.transformation import AzureOpenAITextConfig
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from .llms.hosted_vllm.chat.transformation import HostedVLLMChatConfig
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from .llms.llamafile.chat.transformation import LlamafileChatConfig
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from .llms.litellm_proxy.chat.transformation import LiteLLMProxyChatConfig
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from .llms.vllm.completion.transformation import VLLMConfig
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from .llms.deepseek.chat.transformation import DeepSeekChatConfig
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from .llms.lm_studio.chat.transformation import LMStudioChatConfig
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from .llms.lm_studio.embed.transformation import LmStudioEmbeddingConfig
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from .llms.nscale.chat.transformation import NscaleConfig
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from .llms.perplexity.chat.transformation import PerplexityChatConfig
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from .llms.azure.chat.o_series_transformation import AzureOpenAIO1Config
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from .llms.watsonx.completion.transformation import IBMWatsonXAIConfig
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from .llms.watsonx.chat.transformation import IBMWatsonXChatConfig
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from .llms.watsonx.embed.transformation import IBMWatsonXEmbeddingConfig
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from .llms.github_copilot.chat.transformation import GithubCopilotConfig
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from .llms.nebius.chat.transformation import NebiusConfig
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from .llms.dashscope.chat.transformation import DashScopeChatConfig
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from .llms.modelscope.chat.transformation import ModelScopeChatConfig
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from .llms.moonshot.chat.transformation import MoonshotChatConfig
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from .llms.v0.chat.transformation import V0ChatConfig
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from .llms.morph.chat.transformation import MorphChatConfig
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from .llms.lambda_ai.chat.transformation import LambdaAIChatConfig
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from .llms.hyperbolic.chat.transformation import HyperbolicChatConfig
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# PublicAI now uses JSON-based configuration (see litellm/llms/openai_like/providers.json)
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# All remaining configs are now lazy loaded - see _lazy_imports_registry.py
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# Import LlmProviders here (before main import) because it's imported during import time
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# in multiple places including openai.py (via main import)
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from litellm.types.utils import LlmProviders
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## Lazy loading this is not straightforward, will leave it here for now.
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from .main import * # type: ignore
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from .compression import compress # type: ignore[no-redef]
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@ -1958,6 +1942,9 @@ if TYPE_CHECKING:
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from .llms.dashscope.rerank.transformation import (
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DashScopeRerankConfig as DashScopeRerankConfig,
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)
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from .llms.modelscope.chat.transformation import (
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ModelScopeChatConfig as ModelScopeChatConfig,
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)
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from .llms.moonshot.chat.transformation import (
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MoonshotChatConfig as MoonshotChatConfig,
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)
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@ -304,6 +304,7 @@ LLM_CONFIG_NAMES = (
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"GigaChatConfig",
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"GigaChatEmbeddingConfig",
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"DashScopeChatConfig",
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"ModelScopeChatConfig",
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"MoonshotChatConfig",
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"DockerModelRunnerChatConfig",
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"V0ChatConfig",
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@ -1150,6 +1151,10 @@ _LLM_CONFIGS_IMPORT_MAP = {
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".llms.dashscope.chat.transformation",
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"DashScopeChatConfig",
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),
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"ModelScopeChatConfig": (
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".llms.modelscope.chat.transformation",
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"ModelScopeChatConfig",
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),
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"MoonshotChatConfig": (".llms.moonshot.chat.transformation", "MoonshotChatConfig"),
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"DockerModelRunnerChatConfig": (
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".llms.docker_model_runner.chat.transformation",
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@ -1085,33 +1085,45 @@ dashscope_models: set = set(
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]
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)
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WANDB_MODELS: set = set(
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[
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# openai models
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"openai/gpt-oss-120b",
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"openai/gpt-oss-20b",
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# zai-org models
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"zai-org/GLM-4.5",
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# Qwen models
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"Qwen/Qwen3-235B-A22B-Instruct-2507",
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"Qwen/Qwen3-Coder-480B-A35B-Instruct",
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"Qwen/Qwen3-235B-A22B-Thinking-2507",
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# moonshotai
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"moonshotai/Kimi-K2-Instruct",
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"moonshotai/Kimi-K2.5",
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# MiniMaxAI
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"MiniMaxAI/MiniMax-M2.5",
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# meta models
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"meta-llama/Llama-3.1-8B-Instruct",
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"meta-llama/Llama-3.3-70B-Instruct",
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"meta-llama/Llama-4-Scout-17B-16E-Instruct",
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# deepseek-ai
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"deepseek-ai/DeepSeek-V3.1",
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"deepseek-ai/DeepSeek-R1-0528",
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"deepseek-ai/DeepSeek-V3-0324",
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# microsoft
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"microsoft/Phi-4-mini-instruct",
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]
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)
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modelscope_models: List = [
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# LLM-Research models
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"LLM-Research/c4ai-command-r-plus-08-2024",
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"LLM-Research/Llama-4-Scout-17B-16E-Instruct",
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"LLM-Research/Llama-4-Maverick-17B-128E-Instruct",
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# Mistral models
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"mistralai/Mistral-Small-Instruct-2409",
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"mistralai/Ministral-8B-Instruct-2410",
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"mistralai/Mistral-Large-Instruct-2407",
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"Qwen/Qwen2.5-Coder-32B-Instruct",
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"Qwen/Qwen2.5-Coder-14B-Instruct",
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"Qwen/Qwen2.5-Coder-7B-Instruct",
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"Qwen/Qwen2.5-72B-Instruct",
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"Qwen/Qwen2.5-32B-Instruct",
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"Qwen/Qwen2.5-14B-Instruct",
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"Qwen/Qwen2.5-7B-Instruct",
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"Qwen/QwQ-32B-Preview",
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"opencompass/CompassJudger-1-32B-Instruct",
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"Qwen/QVQ-72B-Preview",
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"Qwen/Qwen2-VL-7B-Instruct",
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"Qwen/Qwen2.5-14B-Instruct-1M",
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"Qwen/Qwen2.5-7B-Instruct-1M",
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"Qwen/Qwen2.5-VL-3B-Instruct",
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"Qwen/Qwen2.5-VL-7B-Instruct",
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"Qwen/Qwen2.5-VL-72B-Instruct",
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"deepseek-ai/DeepSeek-V3",
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"Qwen/QwQ-32B",
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"XGenerationLab/XiYanSQL-QwenCoder-32B-2412",
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"Qwen/Qwen2.5-VL-32B-Instruct",
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"LLM-Research/Llama-4-Scout-17B-16E-Instruct",
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"LLM-Research/Llama-4-Maverick-17B-128E-Instruct",
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# Qwen3 series
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"Qwen/Qwen3-0.6B",
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"Qwen/Qwen3-1.7B",
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"Qwen/Qwen3-4B",
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@ -1120,14 +1132,36 @@ modelscope_models: List = [
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"Qwen/Qwen3-30B-A3B",
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"Qwen/Qwen3-32B",
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"Qwen/Qwen3-235B-A22B",
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"deepseek-ai/DeepSeek-R1-0528",
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"MiniMax/MiniMax-M1-80k",
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"Qwen/Qwen3-235B-A22B-Instruct-2507",
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"Qwen/Qwen3-Coder-480B-A35B-Instruct",
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"Qwen/Qwen3-235B-A22B-Thinking-2507",
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"ZhipuAI/GLM-4.5",
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"Qwen/Qwen3-30B-A3B-Thinking-2507",
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"Qwen/Qwen3-Coder-30B-A3B-Instruct",
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# Qwen3.5 series (new)
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"Qwen/Qwen3.5-0.6B",
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"Qwen/Qwen3.5-1.7B",
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"Qwen/Qwen3.5-3B",
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"Qwen/Qwen3.5-7B",
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"Qwen/Qwen3.5-14B",
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"Qwen/Qwen3.5-30B-A3B",
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"Qwen/Qwen3.5-32B",
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"Qwen/Qwen3.5-235B-A22B",
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"Qwen/Qwen3.5-235B-A22B-Instruct-2507",
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"Qwen/Qwen3.5-Coder-480B-A35B-Instruct",
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"Qwen/Qwen3.5-235B-A22B-Thinking-2507",
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"Qwen/Qwen3.5-30B-A3B-Thinking-2507",
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"Qwen/Qwen3.5-Coder-30B-A3B-Instruct",
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# Other models
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"Qwen/QwQ-32B-Preview",
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"opencompass/CompassJudger-1-32B-Instruct",
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"Qwen/QVQ-72B-Preview",
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"Qwen/Qwen2-VL-7B-Instruct",
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"deepseek-ai/DeepSeek-V3",
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"Qwen/QwQ-32B",
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"XGenerationLab/XiYanSQL-QwenCoder-32B-2412",
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"deepseek-ai/DeepSeek-R1-0528",
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"MiniMax/MiniMax-M1-80k",
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"ZhipuAI/GLM-4.5",
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]
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nebius_embedding_models: List = [
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@ -61,7 +61,7 @@ class TestModelScopeConfig:
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# Set up environment variables for the test
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api_key = "fake-modelscope-key"
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api_base = "https://api-inference.modelscope.cn/v1"
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model = "Qwen/Qwen3-8B"
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model = "modelscope/Qwen/Qwen3-8B" # Use modelscope/ prefix to specify provider
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model_name = "Qwen3-8B" # The actual model name without provider prefix
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# Mock the HTTP request to the ModelScope API
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