Instructions to use Mungert/LFM2-1.2B-Extract-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mungert/LFM2-1.2B-Extract-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Mungert/LFM2-1.2B-Extract-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Mungert/LFM2-1.2B-Extract-GGUF", dtype="auto")

llama-cpp-python

How to use Mungert/LFM2-1.2B-Extract-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mungert/LFM2-1.2B-Extract-GGUF",
	filename="LFM2-1.2B-Extract-bf16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Mungert/LFM2-1.2B-Extract-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Mungert/LFM2-1.2B-Extract-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mungert/LFM2-1.2B-Extract-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/LFM2-1.2B-Extract-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

SGLang

How to use Mungert/LFM2-1.2B-Extract-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mungert/LFM2-1.2B-Extract-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/LFM2-1.2B-Extract-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mungert/LFM2-1.2B-Extract-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/LFM2-1.2B-Extract-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Mungert/LFM2-1.2B-Extract-GGUF with Ollama:
```
ollama run hf.co/Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Mungert/LFM2-1.2B-Extract-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mungert/LFM2-1.2B-Extract-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mungert/LFM2-1.2B-Extract-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mungert/LFM2-1.2B-Extract-GGUF to start chatting

Pi new

How to use Mungert/LFM2-1.2B-Extract-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Mungert/LFM2-1.2B-Extract-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Mungert/LFM2-1.2B-Extract-GGUF with Docker Model Runner:
```
docker model run hf.co/Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M
```

Lemonade

How to use Mungert/LFM2-1.2B-Extract-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mungert/LFM2-1.2B-Extract-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2-1.2B-Extract-GGUF-Q4_K_M

List all available models

lemonade list

LFM2-1.2B-Extract GGUF Models

Model Generation Details

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

Click here to get info on choosing the right GGUF model format

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.

🚀 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:

"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"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! 😊

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GGUF

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1B params

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lfm2

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