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← Back to Liquid Nanos LFM2-350M-Extract is the fastest extraction model, optimized for edge deployment with strict memory and compute constraints. It delivers structured data extraction with minimal latency.

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Specifications

PropertyValue
Parameters350M
Context Length32K tokens
TaskStructured Extraction
Output FormatsJSON, XML, YAML

Edge Deployment

Runs on mobile devices

Low Latency

Fastest extraction model

Batch Processing

High-volume extraction

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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"

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Quick Start

Install:
pip install transformers torch
Run:
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "LiquidAI/LFM2-350M-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:
- product: "Product name"
- price: "Price in dollars"
- quantity: "Number of items"
"""

user_input = "Order: 5 units of Widget Pro at $29.99 each"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_input}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").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)