Igea-1B-v0.0.1 ⚕️🩺

Igea is a biomedical Small Language Model (SLM) for Italian, continually pretrained from Minerva with NMT translated Pubmed Abstracts

🔓: Access to the model is only granted after explicitly acknowledging that you have read the 'Bias, Risk, and Limitation' section of this model card.

This is ongoing research. Do not use it for any medical-related tasks.

Preprint: Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian.

How to use Igea with Hugging Face transformers

import transformers
import torch

model_id = "bmi-labmedinfo/Igea-1B-v0.1"

# 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 = "Il fegato è "

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

# Output:
# [{'generated_text': "Il fegato è una ghiandola fondamentale per il metabolismo umano, la più [...]"}]

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

This section identifies foreseeable harms and misunderstandings.

This is a continued pretraining of 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.

The biomedical setting poses additional threats, including:

• Disparities in research focus, demographic representation, and reporting standards
• Reinforcement of existing medical paradigms and overlook emerging or alternative viewpoints, hindering innovation and comprehensive care
• Generation of incorrect information and false claims, potentially leading to incorrect medical decisions

This model is therefore not intended to be used as it is for any medical-related task.

Training and evaluation data

It achieves the following results on the evaluation set:

• Loss: 1.6976
• Accuracy: 0.6011

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.02
• num_epochs: 1

Training results

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Evaluation

Evaluation results in terms of normalized accuracy for the Igea models on biomedical and general datasets, translated in Italian. The best performing checkpoint of Minerva has been included for comparison.

Credits

Developed by Tommaso M. Buonocore and Simone Rancati.

Thanks to Michele Montebovi for his precious advices.