Model Card for Minerva-7B-base-v1.0

Minerva is the first family of LLMs pretrained from scratch on Italian developed by Sapienza NLP in collaboration with Future Artificial Intelligence Research (FAIR) and CINECA. Notably, the Minerva models are truly-open (data and model) Italian-English LLMs, with approximately half of the pretraining data including Italian text.

• Minerva LLMs - website

Description

This is the model card for Minerva-7B-base-v1.0, a 7 billion parameter model trained on almost 2.5 trillion tokens (1.14 trillion in Italian, 1.14 trillion in English, and 200 billion in code).

This model is part of the Minerva LLM family:

• Minerva-350M-base-v1.0
• Minerva-1B-base-v1.0
• Minerva-3B-base-v1.0
• Minerva-7B-base-v1.0
• Minerva-7B-instruct-v1.0

🚨⚠️🚨 Bias, Risks, and Limitations 🚨⚠️🚨

This section identifies foreseeable harms and misunderstandings.

This is a foundation model, not subject to alignment. Model may:

• Overrepresent some viewpoints and underrepresent others
• Contain stereotypes
• Contain personal information
• Generate:
  • Racist and sexist content
  • Hateful, abusive, or violent language
  • Discriminatory or prejudicial language
  • Content that may not be appropriate for all settings, including sexual content
• Make errors, including producing incorrect information or historical facts as if it were factual
• Generate irrelevant or repetitive outputs

We are aware of the biases and potential problematic/toxic content that current pretrained large language models exhibit: more specifically, as probabilistic models of (Italian and English) languages, they reflect and amplify the biases of their training data. For more information about this issue, please refer to our survey:

• Biases in Large Language Models: Origins, Inventory, and Discussion

How to use Minerva with Hugging Face transformers

import transformers
import torch

model_id = "sapienzanlp/Minerva-7B-base-v1.0"

# Initialize the pipeline.
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

# Input text for the model.
input_text = "La capitale dell'Italia è"

# Compute the outputs.
output = pipeline(
  input_text,
  max_new_tokens=128,
)

output

[{'generated_text': "La capitale dell'Italia è la città di Roma, che si trova a [...]"}]

Model Architecture

Minerva-7B-base-v1.0 is a Transformer model based on the Mistral architecture. Please look at the configuration file for a detailed breakdown of the hyperparameters we chose for this model.

The Minerva LLM family is composed of:

Model Training

Minerva-7B-base-v1.0 was trained using llm-foundry 0.8.0 from MosaicML. The hyperparameters used are the following:

Model Evaluation

For Minerva's evaluation process, we utilized ITA-Bench, a new evaluation suite to test the capabilities of Italian-speaking models. ITA-Bench is a collection of 18 benchmarks that assess the performance of language models on various tasks, including scientific knowledge, commonsense reasoning, and mathematical problem-solving.

Training Data

Minerva-7B-base-v1.0 is trained on 1.14T Italian tokens, 1.14T English tokens, and 200B code tokens.

The training data is a mixture of the following datasets:

Tokenizer Fertility

The tokenizer fertility measures the average amount of tokens produced per tokenized word. A tokenizer displaying high fertility values in a particular language typically indicates that it segments words in that language extensively. The tokenizer fertility is strictly correlated with the inference speed of the model with respect to a specific language, as higher values mean longer sequences of tokens to generate and thus lower inference speed.

Fertility computed over a sample of Cultura X (CX) data and Wikipedia (Wp):

Notice

Minerva-7B-base-v1.0 is a pretrained base model and, therefore, has no moderation mechanisms.

The Sapienza NLP Team

• Riccardo Orlando: data preprocessing, model training
• Pere-Lluis Huguet Cabot: data preprocessing, vocabulary, evaluation
• Luca Moroni: data curation, data analysis, downstream tasks, evaluation
• Simone Conia: data curation, evaluation, project supervision
• Edoardo Barba: data preprocessing, downstream tasks, project supervision
• Roberto Navigli: project lead and coordination

Special thanks for their support

• Giuseppe Fiameni, Nvidia
• Sergio Orlandini, CINECA

Acknowledgments

This work was funded by the PNRR MUR project PE0000013-FAIR and the CREATIVE PRIN project, which is funded by the MUR Progetti di Rilevante Interesse Nazionale programme (PRIN 2020). We acknowledge the CINECA award "IscB_medit" under the ISCRA initiative for the availability of high-performance computing resources and support.