Instructions to use VerbACxSS/sempl-it-v3-sentence-splitter-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VerbACxSS/sempl-it-v3-sentence-splitter-awq with Transformers:

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

pipe = pipeline("text-generation", model="VerbACxSS/sempl-it-v3-sentence-splitter-awq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("VerbACxSS/sempl-it-v3-sentence-splitter-awq", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VerbACxSS/sempl-it-v3-sentence-splitter-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VerbACxSS/sempl-it-v3-sentence-splitter-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VerbACxSS/sempl-it-v3-sentence-splitter-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VerbACxSS/sempl-it-v3-sentence-splitter-awq

SGLang

How to use VerbACxSS/sempl-it-v3-sentence-splitter-awq 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 "VerbACxSS/sempl-it-v3-sentence-splitter-awq" \
    --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": "VerbACxSS/sempl-it-v3-sentence-splitter-awq",
		"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 "VerbACxSS/sempl-it-v3-sentence-splitter-awq" \
        --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": "VerbACxSS/sempl-it-v3-sentence-splitter-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VerbACxSS/sempl-it-v3-sentence-splitter-awq with Docker Model Runner:
```
docker model run hf.co/VerbACxSS/sempl-it-v3-sentence-splitter-awq
```

sempl-it-v3-sentence-splitter-awq / README.md

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	---
	license: mit
	language:
	- it
	base_model:
	- abhishekchohan/Qwen3-4B-AWQ
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- legal
	---
	# sempl-it-v3-sentence-splitter-awq
	SEMPL-IT aims to simplify Italian administrative texts using a progressive approach based on multiple specialized models.
	Each model addresses a specific aspect of the text transformation process, ensuring a step-by-step refinement.

	## Simplification Pipeline
	The complete text simplification pipeline follows eight sequential steps:
	1. [proofreading](https://huggingface.co/VerbACxSS/sempl-it-v3-proofreading-awq)
	2. [lex](https://huggingface.co/VerbACxSS/sempl-it-v3-lex-awq)
	3. [connectives](https://huggingface.co/VerbACxSS/sempl-it-v3-connectives-awq)
	4. [expressions](https://huggingface.co/VerbACxSS/sempl-it-v3-expressions-awq)
	5. [sentence-splitter](https://huggingface.co/VerbACxSS/sempl-it-v3-sentence-splitter-awq) (this)
	6. [nominalizations](https://huggingface.co/VerbACxSS/sempl-it-v3-nominalizations-awq)
	7. [verbs](https://huggingface.co/VerbACxSS/sempl-it-v3-verbs-awq)
	8. [sentence-reorganizer](https://huggingface.co/VerbACxSS/sempl-it-v3-sentence-reorganizer-awq)
	9. [explain](https://huggingface.co/VerbACxSS/sempl-it-v3-explain-awq)

	## Web App
	To integrate this model into the full system, check out:
	- Frontend: [GitHub Repository](https://github.com/VerbACxSS/sempl-it-frontend)
	- Backend: [GitHub Repository](https://github.com/VerbACxSS/sempl-it-backend)
	- Inference: [GitHub Repository](https://github.com/VerbACxSS/sempl-it-inference)

	## Usage
	Install the following dependencies:
	```sh
	pip install transformers==4.49.0
	pip install autoawq==0.2.9
	pip install peft==0.15.0
	```

	Define the system prompt and the text to simplify:
	```py
	PROMPT = """Sei un esperto redattore di documenti istituzionali italiani.

	Dividi le frasi molto lunghe in frasi più brevi. Non alterare il contenuto e lo stile del testo originale.

	# Steps
	1. Leggi attentamente il testo istituzionale.
	2. Individua le frasi lunghe.
	3. Dividi, quando possibile, le frasi individuate in frasi più brevi.

	# Output Format
	Il testo modificato con l'originale formattazione e suddivisione in sezioni e paragrafi.

	# Notes
	- Il testo fornito può essere complesso e richiede attenzione ai dettagli.
	- Esegui solamente le operazioni descritte, non eliminare e non modificare altri contenuti.
	- Assicurati che le implicazioni giuridiche e legali del documento siano mantenute.
	- Non modificare il testo tra virgolette."""

	TEXT_TO_SIMPLIFY = """Il documento individua le esigenze di sviluppo necessarie per assicurare che i principi delineati dalla Legge Regionale 23 dicembre 2004, n. 29 e dai successivi atti normativi, sulla essenziale funzione della ricerca e innovazione nelle Aziende Sanitarie della Regione Emilia-Romagna, si traducano in azioni concrete nel Servizio Sanitario Regionale.

	Alla luce delle evidenze della letteratura internazionale, delle indicazioni della normativa nazionale e della valutazione di quanto già attuato a livello regionale negli anni passati, vengono individuati gli obiettivi di sviluppo e le linee per il raggiungimento dei suddetti obiettivi."""
	```

	Load SEMPL-IT model and tokenizer:
	```py
	from transformers import AutoModelForCausalLM, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained("VerbACxSS/sempl-it-v3-sentence-splitter-awq")
	model = AutoModelForCausalLM.from_pretrained("VerbACxSS/sempl-it-v3-sentence-splitter-awq").to("cuda")
	```

	Define and apply chat template:
	```py
	chat = [
	{"role": "system", "content": PROMPT},
	{"role": "assistant", "content": TEXT_TO_SIMPLIFY},
	]

	formatted_chat = tokenizer.apply_chat_template(
	chat,
	tokenize=False,
	add_generation_prompt=True
	)
	model_inputs = tokenizer([formatted_chat], return_tensors="pt").to("cuda")
	```

	Use SEMPL_IT model with following sampling parameters to generate `simplified_text`:
	```py
	generated_ids = model.generate(
	**model_inputs,
	max_new_tokens=4096,
	temperature=0.1,
	top_p=0.2
	)
	simplified_text = tokenizer.decode(generated_ids[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True)
	print(simplified_text)
	```

	## Acknowledgements
	This contribution is a result of the research conducted within the framework of the PRIN 2020 (Progetti di Rilevante Interesse Nazionale) "VerbACxSS: on analytic verbs, complexity, synthetic verbs, and simplification. For accessibility" (Prot. 2020BJKB9M), funded by the Italian Ministero dell'Università e della Ricerca.