Improve model card: Add pipeline tag, library name, paper, and code links
Browse filesThis PR enhances the model card by:
* Adding the `pipeline_tag: text-generation` to ensure better discoverability for causal language modeling tasks on the Hub.
* Specifying `library_name: transformers`, as the model, being a PEFT adapter for a Causal Language Model, is compatible with the Hugging Face Transformers library.
* Including a link to the associated paper: [Training-Free Bayesianization for Low-Rank Adapters of Large Language Models](https://huggingface.co/papers/2412.05723).
* Adding a link to the official GitHub repository for code: https://github.com/Wang-ML-Lab/bayesian-peft.
* Providing a description of the model based on its abstract for better context.
Please review and merge this PR.
README.md
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datasets:
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- allenai/winogrande
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- allenai/ai2_arc
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- google/boolq
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- wentingzhao/obqa
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- meta-llama/Llama-3.1-8B
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tags:
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- peft
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- bayesian
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base_model:
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- meta-llama/Llama-3.1-8B
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datasets:
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- allenai/winogrande
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- allenai/ai2_arc
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- google/boolq
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- wentingzhao/obqa
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license: llama3.1
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tags:
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- peft
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- bayesian
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pipeline_tag: text-generation
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library_name: transformers
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---
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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).
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**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.
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For the code, installation instructions, and further details on how to use the TFB framework, please refer to the official GitHub repository:
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[https://github.com/Wang-ML-Lab/bayesian-peft](https://github.com/Wang-ML-Lab/bayesian-peft)
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