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
```

ANITA-NEXT-24B-Magistral-2506-ITA / README.md

m-polignano

Update README.md

0350a12 verified 10 months ago

2.36 kB

license: apache-2.0
language:
  - en
  - it
base_model:
  - mistralai/Magistral-Small-2506
pipeline_tag: text-generation
library_name: transformers
tags:
  - ita
  - italian
  - anita
  - magistral
  - 24b
  - uniba
  - bari
  - italy
  - italia
  - Conversational
  - LLaMantino

"Built on mistral/Magistral-Small-2506"

NITA-NEXT-24B-Magistral-2506-ITA is a model of the ANITA - Large Language Models family. The model is a fine-tuned version of Magistral-Small-2506 (a fine-tuned Magistral model). This model version aims to be the a Multilingual Model 🏁 (EN 🇺🇸 + ITA🇮🇹) to further fine-tuning on Specific Tasks in Italian.

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.