Improve model card: Add pipeline tag, library name, paper, and code links

README.md

@@ -1,13 +1,22 @@
 ---
-license: llama3.1
+base_model:
+- meta-llama/Llama-3.1-8B
 datasets:
 - allenai/winogrande
 - allenai/ai2_arc
 - google/boolq
 - wentingzhao/obqa
-base_model:
-- meta-llama/Llama-3.1-8B
+license: llama3.1
 tags:
 - peft
 - bayesian
----
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+This repository contains a low-rank adapter model, based on [Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B), which was presented in the paper [Training-Free Bayesianization for Low-Rank Adapters of Large Language Models](https://huggingface.co/papers/2412.05723).
+
+**Training-Free Bayesianization (TFB)** is a simple yet theoretically grounded framework that efficiently transforms trained low-rank adapters into Bayesian ones without additional training. TFB systematically searches for the maximally acceptable level of variance in the weight posterior, constrained within a family of low-rank isotropic Gaussian distributions. This approach aims to achieve superior uncertainty estimation and generalization compared to existing methods, while eliminating the need for complex Bayesianization training procedures.
+
+For the code, installation instructions, and further details on how to use the TFB framework, please refer to the official GitHub repository:
+[https://github.com/Wang-ML-Lab/bayesian-peft](https://github.com/Wang-ML-Lab/bayesian-peft)