Instructions to use andreagemelli/LFM2.5-350M-IT-Extract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andreagemelli/LFM2.5-350M-IT-Extract with Transformers:

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

pipe = pipeline("text-generation", model="andreagemelli/LFM2.5-350M-IT-Extract")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andreagemelli/LFM2.5-350M-IT-Extract")
model = AutoModelForCausalLM.from_pretrained("andreagemelli/LFM2.5-350M-IT-Extract", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use andreagemelli/LFM2.5-350M-IT-Extract with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andreagemelli/LFM2.5-350M-IT-Extract"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andreagemelli/LFM2.5-350M-IT-Extract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/andreagemelli/LFM2.5-350M-IT-Extract

SGLang

How to use andreagemelli/LFM2.5-350M-IT-Extract 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 "andreagemelli/LFM2.5-350M-IT-Extract" \
    --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": "andreagemelli/LFM2.5-350M-IT-Extract",
		"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 "andreagemelli/LFM2.5-350M-IT-Extract" \
        --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": "andreagemelli/LFM2.5-350M-IT-Extract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use andreagemelli/LFM2.5-350M-IT-Extract with Docker Model Runner:
```
docker model run hf.co/andreagemelli/LFM2.5-350M-IT-Extract
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LFM2.5-350M-IT-Extract: a fine-tuned version of liquid ai model for document key information extraction

Model details

Base model: LiquidAI/LFM2.5-350M
Task: Key Information Extraction (KIE) from Italian form/document images (structured JSON extraction)
Dataset: xfund-kie (derived from XFUND Italian split, see xfund-kie/README.md)
Language: Italian (it)
Fine-tuning framework: TRL.TRL.
Adapted original colab: https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing

Performance (on `xfund-kie` validation)

Model	Avg. F1	Errors (JSON parse / total val docs)
`LiquidAI/LFM2.5-350M` (base)	0.2877	6 / 50
`andreagemelli/LFM2.5-350M-IT-Extract` (fine-tuned)	0.6639	10 / 50

How to use for inference

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch, json
from datasets import load_dataset

model_id = "andreagemelli/LFM2.5-350M-IT-Extract"
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load dataset from Hugging Face Hub (no local repo needed)
dataset = load_dataset("andreagemelli/xfund-kie-it", split="validation")
doc = dataset.filter(lambda x: x["source"] == "it_val_0")[0]

annotation = doc["annotation"]  # from dataset messages or annotation field
schema_text = "".join([f"{k}: {v}.\n" for k, v in REF_SCHEMA.items() if k in annotation]) # cognome: surname of the person.\nnome: ...

user_text = doc["text"] if "text" in doc else doc["messages"][1]["content"]

messages = [
    {"role": "system", "content": SYTEM_PROMPT_DEFAULT + schema_text},
    {"role": "user", "content": user_text},
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
output = model.generate(inputs, max_new_tokens=1024, do_sample=False, streamer=streamer)

Expected snippet output (ref: it_val_0 from xfund-kie/it.val.json):

{
  "cognome": "VALLE",
  "nome": "LUISA",
  ...
}

Defaults I used in my experiments:

REF_SCHEMA = json.load('/path/to/schema/json') # https://huggingface.co/datasets/andreagemelli/xfund-kie-it/blob/main/schema.json
SYTEM_PROMPT_DEFAULT = f"""Identify and extract information matching the following schema.
Return data as a JSON object. Missing data should be omitted.
"""

Cite this project

@misc{gemelli2026LFM2.5-350M-IT-Extract 
  title        = {LFM2.5-350M-IT-Extract: A tiny model for italian document key information extraction},
  author       = {Gemelli, Andrea},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/andreagemelli/LFM2.5-350M-IT-Extract}}
}