Mungert/LFM2-1.2B-Extract-GGUF

LFM2-1.2B-Extract GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit c8dedc99.

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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.

You can find more information about other task-specific models in this blog post.

📄 Model details

Generation parameters: We strongly recommend using greedy decoding with a temperature=0.

System prompt: If no system prompt is provided, the model will default to JSON outputs. We recommend providing a system prompt with a specific format (JSON, XML, or YAML) and a given schema to improve accuracy (see the following example).

Supported languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.

Chat template: LFM2 uses a ChatML-like chat template as follows:

<|startoftext|><|im_start|>system
Return data as a JSON object with the following schema:\n[...]<|im_end|>
<|im_start|>user
Caenorhabditis elegans is a free-living transparent nematode about 1 mm in length that lives in temperate soil environments.<|im_end|>
<|im_start|>assistant
{
"species": "C. elegans",
"genus": "Caenorhabditis",
"description": "A free-living transparent nematode about 1 mm in length that lives in temperate soil environments.",
[...]{<|im_end|>

You can automatically apply it using the dedicated .apply_chat_template() function from Hugging Face transformers.

⚠️ The model is intended for single-turn conversations.

The data used for training these models was primarily synthetic, which allowed us to ensure a diverse data mix. We used a range of document types, domains, styles, lengths, and languages. We also varied the density and distribution of relevant text in the documents. In some cases, the extracted information was clustered in one part of the document; in others, it’s spread throughout. We applied the same approach of ensuring diversity when creating synthetic user requests and designing the structure of the model outputs. The data generation process underwent many iterations, incorporating ideas and feedback from across the Liquid AI team.

📈 Performance

We evaluated LFM2-Extract on a dataset of 5,000 documents, covering over 100 topics with a mix of writing styles, ambiguities, and formats. We used a combination of five metrics to capture a balanced view on syntax, accuracy, and faithfulness:

• Syntax score: Checks whether outputs parse cleanly as valid JSON, XML, or YAML.
• Format accuracy: Verifies that outputs match the requested format (e.g., JSON when JSON is requested).
• Keyword faithfulness: Measures whether values in the structured output actually appear in the input text.
• Absolute scoring: A judge LLM scores quality on a 1-5 scale, assessing completeness and correctness of extractions.
• Relative scoring: We ask a judge LLM to choose the best answer between the extraction model’s output and the ground-truth answer.

LFM2-1.2B-Extract can output complex objects in different languages on a level higher than Gemma 3 27B, a model 22.5 times its size.

🏃 How to run

• Hugging Face: LFM2-1.2B
• llama.cpp: LFM2-1.2B-Extract-GGUF
• LEAP: LEAP model library

📬 Contact

If you are interested in custom solutions with edge deployment, please contact our sales team.

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊