LFM2.5-2.6B is a Liquid AI text model built for tasks involving instruction following, document processing and the sequential completion of several steps. Within the LFM2.5 family, this version received dedicated training for agentic workflows: the developer trained it on interactions with tools and typical agent working environments. That training concerns the model’s behavior; the integration determines the specific actions available in an application.
LFM2.5-2.6B uses the hybrid LFM2 architecture with 2.69 billion parameters. It combines short-convolution blocks and grouped attention. Liquid AI is developing the family with on-device operation in mind, but in Ailoot API the model is accessed through a text interface: the user submits messages and receives a text response.
The model suits information extraction from text, questions about supplied material and processing long instructions. For example, provide customer correspondence, ask it to consolidate the requirements into one list, then identify contradictions and draft a clarification email. For knowledge-base answers, include the retrieved passages and specify that it should rely on them, preserve names and indicate when a necessary fact is absent.
Liquid AI’s documentation lists data extraction, RAG and long-context workflows among its use cases. The developer explicitly limits the recommended scope: agentic programming and tasks requiring substantial factual knowledge without supplied sources are not recommended for this version. When working with industry terminology, include definitions and source documents in the request.
This model has a stated context of 128,000 tokens in Ailoot API. It includes the task material and conversation history; plan the response size alongside them. The developer’s model card lists 16 languages, including Russian, English, Chinese, German and Spanish. This supports multilingual document and correspondence processing, with the desired output language and format specified in the instructions.
", "aboutText": "### A compact model with agentic training\n\nLFM2.5-2.6B is a Liquid AI text model built for tasks involving instruction following, document processing and the sequential completion of several steps. Within the LFM2.5 family, this version received dedicated training for agentic workflows: the developer trained it on interactions with tools and typical agent working environments. That training concerns the model’s behavior; the integration determines the specific actions available in an application.\n\nLFM2.5-2.6B uses the hybrid LFM2 architecture with 2.69 billion parameters. It combines short-convolution blocks and grouped attention. Liquid AI is developing the family with on-device operation in mind, but in Ailoot API the model is accessed through a text interface: the user submits messages and receives a text response.\n\n### Where it is useful\n\nThe model suits information extraction from text, questions about supplied material and processing long instructions. For example, provide customer correspondence, ask it to consolidate the requirements into one list, then identify contradictions and draft a clarification email. For knowledge-base answers, include the retrieved passages and specify that it should rely on them, preserve names and indicate when a necessary fact is absent.\n\nLiquid AI’s documentation lists data extraction, RAG and long-context workflows among its use cases. The developer explicitly limits the recommended scope: agentic programming and tasks requiring substantial factual knowledge without supplied sources are not recommended for this version. When working with industry terminology, include definitions and source documents in the request.\n\n### Context and languages\n\nThis model has a stated context of 128,000 tokens in Ailoot API. It includes the task material and conversation history; plan the response size alongside them. The developer’s model card lists 16 languages, including Russian, English, Chinese, German and Spanish. This supports multilingual document and correspondence processing, with the desired output language and format specified in the instructions.", "locale": "en", "methods": [ { "html": "POST /v1/messagestexttextPOST /v1/responsestexttextPOST /v1/chat/completionstexttextPOST /v1beta/models/liquid/lfm-2.5-2.6b:generateContenttexttext