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语言模型知识库
语言模型Upstage稳定版

Embedding Query

Upstage 当前 query embedding alias，面向查询向量化与检索召回。

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语义检索查询向量化RAG
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语言模型Upstage稳定版

Embedding Passage

Upstage 当前 passage embedding alias，面向文档向量化与语义检索。

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语义检索文档召回RAG
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语言模型Liquid AI稳定版

LFM2-1.2B-RAG

1.2B parameter model optimized for Retrieval-Augmented Generation

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RAG检索增强知识问答
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语言模型Liquid AI稳定版

LFM2-ColBERT-350M

350M parameter ColBERT model for multi-language document retrieval and reranking

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检索召回向量检索RAG
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语言模型Arcee AI稳定版

Trinity Large Thinking

Pricing Integration List chevron-right Language Models chevron-right Trinity-Nano (6B) Trinity-Mini (26B) Trinity-Large-Preview Trinity-Large-Thinking API Reference chevron-right Your First API Call Chat Completion Usage Models Capabilities chevron-right Streaming Messages Multi-Turn Conversations Function Calling Structured Outputs Reasoning Traces Quick Deploys chevron-right Download Models arrow-up-right Hardware Prerequisites Consumer Hardware chevron-right Inference Engines chevron-right Policies chevron-right Deprecation Policy chevron-up chevron-down gitbook Powered by GitBook gitbook xmark block-quote On this page xmark xmark copy Copy chevron-down block-quote On this page block-quote Language Models Trinity-Large-Thinking Overview Trinity-Large-Thinking is a reasoning-optimized variant of Arcee AI's Trinity-Large family — a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token. Built on Trinity-Large-Base and post-trained with extended chain-of-thought reasoning and agentic RL, Trinity-Large-Thinking delivers state-of-the-art performance on agentic benchmarks while maintaining strong general capabilities. Trinity-Large-Thinking generates explicit reasoning traces wrapped in <think>...</think> blocks before producing its final response. This thinking process is critical to the model's performance — thinking tokens must be kept in context for multi-turn conversations and agentic loops to function correctly.

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多步推理Reasoning TraceAgent 工作流
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