> ## Documentation Index
> Fetch the complete documentation index at: https://docs.liquid.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# LFM2.5-VL-1.6B-Extract
> Vision extraction Nano for structured JSON extraction from images
← Back to Liquid Nanos
LFM2.5-VL-1.6B-Extract extracts user-defined fields from images and returns structured JSON. It extends the Extract family to vision workflows, using a YAML field list in the system prompt to define exactly what to extract.
## Specifications
| Property | Value |
| -------------- | -------------------------------------------- |
| Parameters | 1.6B total (1.2B LM + \~400M vision encoder) |
| Context Length | 128K tokens |
| Image Input | Single image, dynamic resolution |
| Task | Vision structured extraction |
| Output Format | JSON |
Extract visual attributes into JSON.
Auto-tag product images with structured fields.
Detect visual events for automated workflows.
## Prompting Recipe
Use greedy decoding for best results. This model is intended for single-image, single-turn extraction.
Describe the fields to extract as YAML in the system prompt, then provide the image as the user message.
```yaml theme={"theme":{"light":"github-light","dark":"github-dark"}}
wood_color: The overall coloration of the wood surface
wood_texture: The tactile quality of the wood surface
wood_pattern: The pattern types visible on the wood surface
```
## Quick Start
**Install:**
```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
pip install "transformers>=5.1.0" pillow torch accelerate
```
**Run:**
```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from transformers import AutoModelForImageTextToText, AutoProcessor
from transformers.image_utils import load_image
model_id = "LiquidAI/LFM2.5-VL-1.6B-Extract"
model = AutoModelForImageTextToText.from_pretrained(
model_id,
device_map="auto",
dtype="bfloat16",
trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
image = load_image("https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract/resolve/main/sample_image.png")
fields_yaml = """wood_color: The overall coloration of the wood surface
wood_texture: The tactile quality of the wood surface
wood_pattern: The pattern types visible on the wood surface"""
system_prompt = f"""Extract the following from the image:
{fields_yaml}
Respond with only a JSON object. Do not include any text outside the JSON."""
conversation = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": [{"type": "image", "image": image}]},
]
inputs = processor.apply_chat_template(
conversation,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
tokenize=True,
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
response = processor.batch_decode(
outputs[:, inputs["input_ids"].shape[1]:],
skip_special_tokens=True,
)[0]
print(response)
```
**Run GGUF:**
```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
llama-server -hf LiquidAI/LFM2.5-VL-1.6B-Extract-GGUF:Q4_K_M
llama-cli -hf LiquidAI/LFM2.5-VL-1.6B-Extract-GGUF:F16 \
-p "wood_color: The overall coloration of the wood surface" \
--image ./image.jpg
```