57 lines
1.8 KiB
Markdown
57 lines
1.8 KiB
Markdown
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---
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library_name: transformers
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license: other
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license_name: lfm1.0
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license_link: LICENSE
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language:
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- en
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- ar
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- zh
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- fr
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- de
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- ja
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- ko
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- es
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pipeline_tag: text-generation
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tags:
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- liquid
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- lfm2
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- edge
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base_model: LiquidAI/LFM2-350M-Extract
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---
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<center>
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<div style="text-align: center;">
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<img
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src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
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alt="Liquid AI"
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style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
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/>
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</div>
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<div style="display: flex; justify-content: center; gap: 0.5em;">
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<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
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</div>
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</center>
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<br>
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# LFM2-350M-Extract-GGUF
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Based on [LFM2-350M](https://huggingface.co/LiquidAI/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.
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**Use cases**:
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- Extracting invoice details from emails into structured JSON.
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- Converting regulatory filings into XML for compliance systems.
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- Transforming customer support tickets into YAML for analytics pipelines.
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- Populating knowledge graphs with entities and attributes from unstructured reports.
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You can find more information about other task-specific models in this [blog post](https://www.liquid.ai/blog/introducing-liquid-nanos-frontier-grade-performance-on-everyday-devices).
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## 🏃 How to run LFM2
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Example usage with [llama.cpp](https://github.com/ggml-org/llama.cpp):
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```
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llama-cli -hf LiquidAI/LFM2-350M-Extract-GGUF
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```
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