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Model Card for Model ID
VaxLLM (Vaccine Large Language Model) is a fine-tuned Llama-3 model to automatically perform the classification and annotation of vaccine-related articles, using Brucella vaccines as a case study.
Model Details
Model Description
VaxLLM can classify the articles to filter the relevant articles containing specific information about the Brucella vaccine.
VaxLLM can also systematically extract the key information about vaccines, including the vaccine antigen, vaccine formulation, vaccine platform, host species used as animal models, and experiments used to investigate the vaccine.
- Developed by: Xingxian Li at He Group, University of Michigan
- License: MIT
- Finetuned from model: Meta-Llama-3-8B-Instruct
- Task Supported: Text Generation
- Specialized Domain: Brucella vaccine-related content
- Model Type: Fine-tuned Language Model
Model Sources
- Repository: https://github.com/xingxianli/VaxLLM
- Paper: https://www.biorxiv.org/content/10.1101/2024.11.25.625209v1
Uses
- Use case: To assist researchers in identifying and analyzing Brucella vaccine-related articles for research and development. -Intended Users: Researchers, bioinformaticians, and professionals in the field of vaccine development.
Direct Use
Gated model: Login with an HF token with gated access permission, please request permission before usage
huggingface-cli login
Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="Xingxian123/VaxLLM")
Use directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Xingxian123/VaxLLM")
model = AutoModelForCausalLM.from_pretrained("Xingxian123/VaxLLM")
prompt
classification: Using the following data: '{Abstract information}', is this article about a brucella vaccine? To classify an article as being about a brucella vaccine, you must successfully extract at least some information about the vaccine formulation. This includes details such as the antigen, protein, gene, adjuvant, or vaccine platform mentioned in the abstract.
annotation: Extract the following details using the given data: '{Abstract information}': Vaccine Introduction,Vaccine Antigen, Vaccine Type, Vaccine Formulation, Host Species Used as Laboratory Animal Model, Experiment Used to investigate the vaccine Ensure each response is based solely on the provided data. Ensure the response is formatted as follows: Response: Vaccine Introduction: Vaccine Type: Vaccine Antigen: Vaccine Formulation: Host Species Used as Laboratory Animal Model: Experiment Used to investigate the vaccine:
Bias, Risks, and Limitations
While the model has been fine-tuned for Brucella vaccine-related tasks, users should carefully validate outputs, particularly for critical applications. Model might struggle with content outside the Brucella vaccine domain.
Recommendations
Users should be made aware of the risks, biases and limitations of the model.
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meta-llama/Meta-Llama-3-8B-Instruct