Text Generation
Transformers
Safetensors
English
Italian
ita
italian
anita
magistral
24b
uniba
bari
italy
italia
Conversational
LLaMantino
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
- Transformers
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
- Xet hash:
- 70e2c040b58512200815beed505464eacbf77b551ee23826fa1678e919fba744
- Size of remote file:
- 23.2 MB
- SHA256:
- 465c6c28a44edb18302e5aa0214046b3477e30f59e2f9d56fa4d2e3cf49d36dc
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