> ## Documentation Index
> Fetch the complete documentation index at: https://liquidai-example-leonie-demo.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# LFM2-1.2B-Extract
> 1.2B parameter extraction model (deprecated - use LFM2.5-1.2B-Instruct instead)
← Back to Liquid Nanos
This model is deprecated. Use [LFM2.5-1.2B-Instruct](/lfm/models/lfm25-1.2b-instruct) for structured extraction with current LFM2.5 prompting and tool-use behavior.
LFM2-1.2B-Extract is optimized for extracting structured data (JSON, XML, YAML) from unstructured documents. It handles complex nested schemas and multi-field extraction with high accuracy.
## Specifications
| Property | Value |
| -------------- | --------------------- |
| Parameters | 1.2B |
| Context Length | 32K tokens |
| Task | Structured Extraction |
| Output Formats | JSON, XML, YAML |
Extract fields from documents
Automate form filling
Complex nested structures
## Prompting Recipe
Use `temperature=0` (greedy decoding) for best results. This model is intended for single-turn conversations only.
**System Prompt Format:**
```
Identify and extract information matching the following schema.
Return data as a JSON object. Missing data should be omitted.
Schema:
- field_name: "Description of what to extract"
- nested_object:
- nested_field: "Description"
```
If no system prompt is provided, defaults to JSON. Specify format (JSON, XML, or YAML) and schema for better accuracy.
## Quick Start
**Install:**
```bash theme={null}
pip install "transformers>=5.2.0" torch accelerate
```
**Run:**
```python theme={null}
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "LiquidAI/LFM2-1.2B-Extract"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
system_prompt = """Identify and extract information matching the following schema.
Return data as a JSON object. Missing data should be omitted.
Schema:
- name: "Person's full name"
- email: "Email address"
- company: "Company name"
"""
user_input = "Contact John Smith at john.smith@acme.com. He works at Acme Corp."
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_input}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0, do_sample=False)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
**Download GGUF:**
```bash theme={null}
hf download LiquidAI/LFM2-1.2B-Extract-GGUF \
--local-dir ./LFM2-1.2B-Extract-GGUF
```
**Run:**
```bash theme={null}
llama-cli -m ./LFM2-1.2B-Extract-GGUF/LFM2-1.2B-Extract-Q4_K_M.gguf \
-p "Extract name and email from: Contact John at john@example.com" \
--temp 0
```