Instructions to use NicolasRR/DPO-MCQA-FalseMixin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NicolasRR/DPO-MCQA-FalseMixin with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-783M") model = PeftModel.from_pretrained(base_model, "NicolasRR/DPO-MCQA-FalseMixin") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Finetuned version of MBZUAI/LaMini-Flan-T5-783M for MCQA answer. Produced in the context of EPFL course CS-552 : Modern Natural Language Processing
Model Details
Model Description
- Developed by: MNLPredators
- Model type: Seq2Seq
- Language(s) (NLP): English
- Finetuned from model : MBZUAI/LaMini-Flan-T5-783M
Model Sources [optional]
- Repository: (MNLPredators repo)[https://github.com/CS-552/project-m3-2024-mnlpredators/tree/main]
- Paper [optional]: [More Information Needed]
Pipeline
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
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Base model
MBZUAI/LaMini-Flan-T5-783M