Instructions to use mz516/llama-3.1-lora-MechQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mz516/llama-3.1-lora-MechQA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "mz516/llama-3.1-lora-MechQA") - Notebooks
- Google Colab
- Kaggle
This model adapts LLaMA-3.1-8B-Instruct to the MechQA task using LoRA fine-tuning. The model is trained on the MechQA dataset, which contains automatically generated SQuAD-style question–answer pairs related to engineering materials and their mechanical properties. Achieves 80.48 EM / 86.25 F1 on the MechQA validation set.
This model was developed as part of the study: “Automatic Generation of a Mechanical Properties Question-Answering Dataset for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models”
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Model tree for mz516/llama-3.1-lora-MechQA
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct