m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITA

---

"Built on m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA"

ANITA-NEXT-24B-Magistral-2506-VISION-ITA is a Thinking Vision Language Model of the ANITA - Large Language Models family. The model is a merge of textual layers from of ANITA-NEXT-24B-Magistral-2506-VISION-ITA and vision layers and processor from mistralai/Mistral-Small-3.1-24B-Instruct-2503. This model version aims to be the a Multilingual Vision Language Model 🏁 (EN 🇺🇸 + ITA🇮🇹) to further fine-tuning on Specific Tasks in Italian.

❗❗❗Use at your own risk. The model may generate hallucinations, incorrect, invented, offensive, unethical or dangerous responses. We are not responsible for any dangerous/offensive/criminal use. The model is release for research only purposes.❗❗❗

The 🌟ANITA project🌟 *(Advanced Natural-based interaction for the ITAlian language)* wants to provide Italian NLP researchers with an improved model for the Italian Language 🇮🇹 use cases.

The NEXT family includes four models:

• m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA - General Purpose
• m-polignano/ANITA-NEXT-24B-Dolphin-Mistral-UNCENSORED-ITA - Uncensored
• m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITA - Vision-Language
• m-polignano/ANITA-NEXT-20B-gpt-oss-ITA - Agentic Ready

---

GGUF - OLLAMA: m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITA-GGUF

---

Colab Demo: A100 - 40GB - Colab Notebook
The Model runs on a single GPU, 19.56GB of VRAM by using a 4bit Quantization.

---

Specifications

• Model developers:
  Ph.D. Marco Polignano - University of Bari Aldo Moro, Italy
  SWAP Research Group
  
• Variations: The model release has been supervised fine-tuning (SFT) using QLoRA 4bit, on instruction-based datasets. DPO approach over the mlabonne/orpo-dpo-mix-40k dataset is used to align with human preferences for helpfulness and safety.
• Input: Models input text only.
• Language: Multilingual 🏁 + Italian 🇮🇹
• Output: Models generate text and code only.
• Model Architecture: Mistral architecture.
• Context length: 128k, but degradate after 40k.
• Library Used: [Transformers 4.56.0.dev0] (https://huggingface.co/docs/transformers/index)

---

Playground

To use the model directly, there are many ways to get started, choose one of the following ways to experience it.

Prompt Template

<s>[SYSTEM_PROMPT]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.[/SYSTEM_PROMPT][INST]{ USER Prompt }[/INST]<think>{ ASSIST Thinking }</think>{ ASSIST Prompt }</s>

Transformers

For direct use with transformers, you can easily get started with the following steps.

• Firstly, you need to install transformers via the command below with pip.

      pip install -U --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo
      pip install sentencepiece protobuf "datasets>=3.4.1,<4.0.0" "huggingface_hub>=0.34.0" hf_transfer
      pip install transformers==4.52.4 vllm==0.8.4
      wget https://fastly.picsum.photos/id/237/200/300.jpg?hmac=TmmQSbShHz9CdQm0NkEjx1Dyh_Y984R9LpNrpvH2D_U -O image.jpg
  

• Right now, you can start using the model directly.

      from PIL import Image
      from transformers.image_utils import load_image
      from transformers import AutoProcessor, AutoModelForVision2Seq
      import torch
      
      processor = AutoProcessor.from_pretrained("m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITA")
      model = AutoModelForVision2Seq.from_pretrained("m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITA",
                                                      torch_dtype=torch.bfloat16,
                                                      device_map="auto",
                                                      load_in_4bit=True,
                                                      bnb_4bit_quant_type="nf4",
                                                      bnb_4bit_use_double_quant=True,
                                                      bnb_4bit_compute_dtype=torch.bfloat16,
                                                     )
      # Load images
      image1 = load_image("image.jpg")
      prompt = "Descrivi il contenuto dell'immagine?"
      
      messages = [
          {
              "role": "user",
              "content": [
                  {"type": "text", "text": prompt},
                  {"type": "image"},
              ],
          },
      ]
      
      prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
      inputs = processor(text=prompt, images=[image1], return_tensors="pt")
      inputs = inputs.to("cuda").to(torch.bfloat16)
      
      # Generate outputs
      generated_ids = model.generate(**inputs, max_new_tokens=500)
      generated_texts = processor.batch_decode(
          generated_ids,
          skip_special_tokens=True,
          max_tokens=8192,
          temperature=0.7,
          top_p=0.90,
      )
      
      print(generated_texts[0])
  

---

Citation instructions

@misc{polignano2024advanced,
      title={Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA}, 
      author={Marco Polignano and Pierpaolo Basile and Giovanni Semeraro},
      year={2024},
      eprint={2405.07101},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{rastogi2025magistral,
  title={Magistral},
  author={Rastogi, Abhinav and Jiang, Albert Q and Lo, Andy and Berrada, Gabrielle and Lample, Guillaume and Rute, Jason and Barmentlo, Joep and Yadav, Karmesh and Khandelwal, Kartik and Chandu, Khyathi Raghavi and others},
  journal={arXiv preprint arXiv:2506.10910},
  year={2025}
}