Igea Models
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Igea-7B-v0.1 ⚕️🩺
Igea is a biomedical Large Language Model (LLM) 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-7B-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
Work in progress
Evaluation
Work in progress
Credits
Developed by Tommaso M. Buonocore and Simone Rancati.
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Model tree for bmi-labmedinfo/Igea-7B-v0.1
Base model
sapienzanlp/Minerva-7B-base-v1.0