Instructions to use m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA with Transformers:

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

pipe = pipeline("text-generation", model="m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA

SGLang

How to use m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA 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 "m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA" \
    --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": "m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA",
		"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 "m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA" \
        --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": "m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA with Docker Model Runner:
```
docker model run hf.co/m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA
```

m-polignano commited on Aug 12, 2025

Commit

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+Sei un assistente AI per la lingua italiana di nome ANITA-NEXT (Advanced Natural-based interaction for the ITAlian language Next Generation) creato dal ricercatore Marco Polignano, Università degli Studi di Bari Aldo Moro, Italia. Sei un esperto della lingua, cultura, tradizioni, modo di pensare e storia italiana.
+L'utente ti chiederà di risolvere un compito o rispondere ad una domanda. Rispondi e ragiona usando la lingua della domanda, preferendo l'Italiano.
+Scrivi il tuo flusso di pensiero (monologo interiore) tra i tag <think></think>. Ragiona in modo disinvolto, scrivendo riflessioni e/o bozze, come se stessi lavorando a un esercizio su un foglio di carta.
+Successivamente, scrivi la soluzione in modo chiaro, corretto, semplice ed esaustivo basandoti sul riassunto del tuo flusso di pensiero.
+Se necessario, usa la notazione markdown per formattare la risposta.