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lfm2.5-2.6b
LFM2.5-2.6B is LiquidAI's compact, text-only reasoning model for on-device agentic workloads. It has 2.69B parameters, a 128K-token context window, multilingual support, and post-training for tool use, instruction following, data extraction, RAG, and multi-step agents. This entry uses the recommended Q4_K_M GGUF quantization from LiquidAI's official repository.

Repository: localaiLicense: other

lfm2.5-2.6b-q8
LFM2.5-2.6B is LiquidAI's compact, text-only reasoning model for on-device agentic workloads. It has 2.69B parameters, a 128K-token context window, multilingual support, and post-training for tool use, instruction following, data extraction, RAG, and multi-step agents. This entry uses the higher-quality Q8_0 GGUF quantization from LiquidAI's official repository.

Repository: localaiLicense: other

hunyuan-ocr-q8
HunyuanOCR is Tencent's lightweight vision-language model for document parsing, text spotting, information extraction, and text-image translation. This Q8_0 GGUF build runs on llama.cpp with its bundled vision projector.

Repository: localaiLicense: tencent-hunyuan-community

hunyuan-ocr-bf16
HunyuanOCR in BF16 GGUF format for maximum model and vision-projector fidelity. It runs on llama.cpp and supports document parsing, text spotting, information extraction, and text-image translation.

Repository: localaiLicense: tencent-hunyuan-community

lfm2-1.2b
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.

Repository: localaiLicense: lfm1.0

liquidai_lfm2-350m-extract
Based on LFM2-350M, LFM2-350M-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. You can find more information about other task-specific models in this blog post.

Repository: localaiLicense: lfm1.0

liquidai_lfm2-1.2b-extract
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.

Repository: localaiLicense: lfm1.0

gliner2.5-vllm-cpp
GLiNER2.5 is a zero-shot named entity recognition and structured extraction model. Given a text and a set of label names, it extracts entity spans in a single forward pass — no fine-tuning required. The boundary architecture handles arbitrary span lengths up to the encoded window. In LocalAI, serve via the token classification endpoint with this model. The vllm.cpp engine runs the GLiNER2.5 extraction pipeline through the vllm_gliner_ner C ABI (ABI v27). mDeBERTa-v3-base backbone, 287M params, multilingual, ~594 MB.

Repository: localaiLicense: apache-2.0