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Model Card for Minerva-1B-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.


This is the model card for Minerva-1B-base-v1.0, a 1 billion parameter model trained on 200 billion tokens (100 billion in Italian, 100 billion in English).

This model is part of the Minerva LLM family:

🚨⚠️🚨 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:
    • 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 as if it were factual
  • Generate irrelevant or repetitive outputs

We are aware of the biases 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:

How to use Minerva with Hugging Face transformers

import transformers
import torch

model_id = "sapienzanlp/Minerva-1B-base-v1.0"

# Initialize the pipeline.
pipeline = transformers.pipeline(
    model_kwargs={"torch_dtype": torch.bfloat16},

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

# Compute the outputs.
output = pipeline(

# Output:
# [{'generated_text': "La capitale dell'Italia Γ¨ la cittΓ  di Roma, che si trova a [...]"}]

Model Architecture

Minerva-1B-base-v1.0 is a Transformer model based on the Mistral architecture, where the number of layers, number of heads, and the hidden states dimension are modified to reach 1B parameters. Please, take a 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 Name Tokens Layers Hidden Size Attention Heads KV Heads Sliding Window Max Context Length
Minerva-350M-base-v1.0 70B (35B it + 35B en) 16 1152 16 4 2048 16384
Minerva-1B-base-v1.0 200B (100B it + 100B en) 16 2048 16 4 2048 16384
Minerva-3B-base-v1.0 660B (330B it + 330B en) 32 2560 32 8 2048 16384

Model Training

Minerva-1B-base-v1.0 was trained using llm-foundry 0.6.0 from MosaicML. The hyperparameters used are the following:

Model Name Optimizer lr betas eps weight decay Scheduler Warmup Steps Batch Size (Tokens) Total Steps
Minerva-350M-base-v1.0 Decoupled AdamW 2e-4 (0.9, 0.95) 1e-8 0.0 Cosine 2% 4M 16,690
Minerva-1B-base-v1.0 Decoupled AdamW 2e-4 (0.9, 0.95) 1e-8 0.0 Cosine 2% 4M 47,684
Minerva-3B-base-v1.0 Decoupled AdamW 2e-4 (0.9, 0.95) 1e-8 0.0 Cosine 2% 4M 157,357

Model Evaluation

We assessed our model using the LM-Evaluation-Harness library, which serves as a comprehensive framework for testing generative language models across a wide range of evaluation tasks.

All the reported benchmark data was already present in the LM-Evaluation-Harness suite.

Italian Data:

Task Accuracy
xcopa (0-shot) 0.602
Hellaswag (5-shot) 0.3847
Belebele (5-shot) 0.25
TruthfulQA MC 1 (0-shot) 0.2375
TruthfulQA MC 2 (0-shot) 0.3996
M MMLU (5-shot) 0.2546
arc challenge (5-shot) 0.2549

English Data:

Task Accuracy
Hellaswag (5-shot) 0.43
Belebele (5-shot) 0.26
TruthfulQA MC 1 (0-shot) 0.21
TruthfulQA MC 2 (0-shot) 0.38
M MMLU (5-shot) 0.26
arc challenge (5-shot) 0.26
arc easy (5-shot) 0.4966
piqa (5-shot) 0.6975
sciq (5-shot) 0.830

Training Data

Minerva-1B-base-v1.0 was trained on 100B Italian tokens and 100B English tokens sampled from CulturaX. The data was selected from the following sources:

  • OSCAR-2201
  • OSCAR-2301
  • mC4

We have extracted some statistics on Italian (115B tokens) and English (210B tokens) documents from CulturaX on the selected sources:

Proportion of number of tokens per domain (Italian) italian-tok-counts

Proportion of number of tokens per domain (English) english-tok-counts

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):

Model Voc. Size Fertility IT (CX) Fertility EN (CX) Fertility IT (Wp) Fertility EN (Wp)
Mistral-7B-v0.1 32000 1.87 1.32 2.05 1.57
gemma-7b 256000 1.42 1.18 1.56 1.34
Minerva-1B-base-v1.0 32768 1.39 1.32 1.66 1.59


Minerva-350M-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 coordinator

Special thanks for their support

  • Giuseppe Fiameni, Nvidia
  • Sergio Orlandini, CINECA


This work was funded by the PNRR MUR project PE0000013-FAIR. We acknowledge the CINECA award "IscB_medit" under the ISCRA initiative, for the availability of high performance computing resources and support.

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