Add pipeline tag and improve documentation

README.md

@@ -1,13 +1,16 @@
 ---
-tags:
-  - sketchtune
-  - sketch to adapt
 library_name: transformers
+pipeline_tag: text-generation
+tags:
+- sketchtune
+- sketch to adapt
 ---
 
 # Fine-Tuned Model Checkpoints for *(ICML 2025) Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation*
 
-This repository contains the fine-tuned model checkpoints used in our ICML 2025 paper: **Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation**.
+This repository contains the fine-tuned model checkpoints used in the paper: [Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation](https://huggingface.co/papers/2410.06364).
+
+**Authors**: Tianyi Zhang, Junda Su, Aditya Desai, Oscar Wu, Zhaozhuo Xu, Anshumali Shrivastava.
 
 The table below lists the available models along with their fine-tuning datasets, bit widths, groups per row, and training epochs.
 
@@ -40,7 +43,7 @@ SketchTune is a novel method for adapting large language models (LLMs) that focu
 * Even with base models that are **2.6–3.5× smaller**, SketchTune **outperforms LoRA, DoRA, and S2FT** on commonsense and math reasoning benchmarks.
 * On the GSM8K math dataset, SketchTune achieves a **14.48% higher accuracy than LoftQ**, while training **7.3× fewer parameters**.
 
-For a deep dive into how sketching works, including math details and extensive test results, check out our full paper: [https://arxiv.org/abs/2410.06364](https://arxiv.org/abs/2410.06364).
+For a deep dive into how sketching works, including math details and extensive test results, check out the full paper: [https://huggingface.co/papers/2410.06364](https://huggingface.co/papers/2410.06364).
 
 ### Citation