Repository: localaiLicense: apache-2.0
The **LFM2.5-1.2B-Nova-Function-Calling-GGUF** is a quantized version of the original model, optimized for efficiency with **Unsloth**. It supports text and multimodal tasks, using different quantization levels (e.g., Q2_K, Q3_K, Q4_K, etc.) to balance performance and memory usage. The model is designed for function calling and is faster than the original version, making it suitable for tasks like code generation, reasoning, and multi-modal input processing.
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Repository: localaiLicense: lfm1.0
LFM2-1.2B is a hybrid liquid model designed for edge AI and on-device deployment, offering fast inference and multilingual support across 8 languages. It's optimized for agentic tasks, data extraction, and multi-turn conversations with efficient CPU/GPU/NPU compatibility.
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Repository: localaiLicense: lfm1.0
Based on LFM2-1.2B, LFM2-1.2B-Extract is designed to extract important information from a wide variety of unstructured documents (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML. Use cases: Extracting invoice details from emails into structured JSON. Converting regulatory filings into XML for compliance systems. Transforming customer support tickets into YAML for analytics pipelines. Populating knowledge graphs with entities and attributes from unstructured reports.
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Repository: localaiLicense: lfm1.0
Based on LFM2-1.2B, LFM2-1.2B-RAG is specialized in answering questions based on provided contextual documents, for use in RAG (Retrieval-Augmented Generation) systems. Use cases: Chatbot to ask questions about the documentation of a particular product. Custom support with an internal knowledge base to provide grounded answers. Academic research assistant with multi-turn conversations about research papers and course materials.
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Repository: localaiLicense: lfm1.0
Based on LFM2-1.2B, LFM2-1.2B-Tool is designed for concise and precise tool calling. The key challenge was designing a non-thinking model that outperforms similarly sized thinking models for tool use. Use cases: Mobile and edge devices requiring instant API calls, database queries, or system integrations without cloud dependency. Real-time assistants in cars, IoT devices, or customer support, where response latency is critical. Resource-constrained environments like embedded systems or battery-powered devices needing efficient tool execution.
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