Instructions to use LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct

SGLang

How to use LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct 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 "LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct
```

AU-extraction_Qwen2.5-7B-Instruct / README.md

LequeuISIR

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	---
	library_name: transformers
	datasets:
	- LequeuISIR/GDN-CC
	- LequeuISIR/GDN-CC-large
	language:
	- fr
	base_model:
	- Qwen/Qwen2.5-7B-Instruct
	---

	# Model Card for AU-clarification_gemma-2-9b-it

	Qwen2.5-7B-Instruct finetuned on the GDN-CC dataset for the task of Argumentative Unit Extraction. This is the best model for AU extraction and the one used to annotate GDN-CC-large.

	## Uses

	It is recommended to use it with the vLLM framework:

	```python
	from vllm import LLM, SamplingParams

	llm = LLM(model="LequeuISIR/AU-extraction_Qwen2.5-7B-Instruct",
	max_model_len=2048)

	tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

	sampling_params = SamplingParams(temperature=0.2, max_tokens=2000)

	messages = [
	{"role": "user", "content": f"{PROMPT}{item['text'].strip()}"}
	]
	prompt_string = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True
	)

	outputs = llm.generate([prompt_string], sampling_params)

	```

	with the prompt being:
	```
	Je vais te donner un texte d'opinion en français. Ton travail est de segmenter ce textes en unités argumentatives. \
	Nous définissions une unité argumentative comme un ou des segments du texte qui s'intérèssent à un sujet. Elle peut être composée de solution(s), \
	d'argument(s) ou de simple affirmation(s). Une unitée argumentative n'est pas forcément contigüe: elle peut joindre des segments qui ne se suivent pas. \n \
	Cette tâche est EXTRACTIVE. tu dois COPIER et seulement copier le texte de l'unité argumentative exactement comme elle est écrite, incluant les majuscules et la ponctuation. \
	Si l'unité argumentative est composée de plusieurs segments non contigues, tu peux les concaténer en les séparant simplement d'un espace. Il y a au moins \
	une unité argumentative dans le texte, mais pas de nombre maximum. Ressors les unité argumentative sous forme de liste comme montré dans l'exemple. \
	Il n'est pas obligatoire que tous les segments du texte fasse partie d'une unité argumentative. \n\n \

	Tu dois donner les unités argumentative sous la forme du liste: \n \
	- unité argumentative 1\n \
	- unité argumentative 2\n \
	...\n\n \

	Ne sort RIEN D'AUTRE que la liste d'unités argumentatives.
	voici le texte:\n \
	"""
	```





	BibTeX:

	```bibtex
	@article{lequeu2026gdn,
	title={The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations},
	author={Lequeu, Pierre-Antoine and Labat, L{\'e}o and Cave, Laur{\`e}ne and Lejeune, Ga{\"e}l and Yvon, Fran{\c{c}}ois and Piwowarski, Benjamin},
	journal={arXiv preprint arXiv:2601.14944},
	year={2026}
	}
	```