The model has ~354M parameters and a vocabulary of 50.335 tokens. It is a foundation model, pre-trained for causal language modeling, so it is mainly suitable for basic natural language generation, and you will have to fine-tune it in order to use it on more specific downstream tasks.
In order to use the model for inference on GPU, the following pipeline is needed:
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3from transformers import pipeline
4
5tokenizer = AutoTokenizer.from_pretrained("osiria/diablo-italian-base-354m")
6model = AutoModelForCausalLM.from_pretrained("osiria/diablo-italian-base-354m", torch_dtype=torch.float16)
7
8device = torch.device("cuda")
9model = model.to(device)
10
11pipeline_nlg = pipeline("text-generation", model = model, tokenizer = tokenizer, device = 0)
12pipeline_nlg("Ciao, mi chiamo Marco Rossi e")
13
14# [{'generated_text': 'Ciao, mi chiamo Marco Rossi e sono un ragazzo di 23 anni.'}]
The model might behave erratically when presented with prompts which are too far away from its pre-training and, because of the probabilistic nature of its generation, it might occasionally produce biased or offensive content with respect to gender, race, ideologies, and political or religious beliefs.
These limitations imply that the model and its outputs should be used with caution, and should not be involved in situations that require the generated text to be fair or true.
