Add pipeline tag, license and paper link

README.md

@@ -1,13 +1,15 @@
 ---
-tags:
-  - sketchtune
-  - sketch to adapt
 library_name: transformers
+tags:
+- sketchtune
+- sketch to adapt
+pipeline_tag: text-generation
+license: apache-2.0
 ---
 
 # Base Models for Fine-tuning in *(ICML 2025) Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation*
 
-This repository hosts the compressed base models used in the fine-tuning experiments from our ICML 2025 paper: **Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation**. The available models and formats are as follows.
+This repository hosts the compressed base models used in the fine-tuning experiments from our ICML 2025 paper: **[Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation](https://huggingface.co/papers/2410.06364)**. The available models and formats are as follows.
 
 | Model         | Bits | GPR (Groups Per Row)     |
 |---------------|--------|--------------------|
@@ -34,7 +36,7 @@ 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 our full paper: [https://huggingface.co/papers/2410.06364](https://huggingface.co/papers/2410.06364).
 
 ### Citation