Add link to paper (#2)

- Add link to paper (c4fd121230b81373c16eba1cdd23be19e7acdf15)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,17 +1,18 @@
 ---
-pipeline_tag: text-generation
-library_name: transformers
-license: apache-2.0 # Assuming Apache 2.0 license, adjust if different.
 base_model:
 - Qwen/Qwen3-8B
 - Qwen/Qwen3-0.6B
 - Qwen/Qwen3-4B
 - Qwen/Qwen3-1.7B
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
 tags:
 - Light weight
 - Agentic
 - Conversational
 ---
+
 # Qwen3 Quantized Models – Lexicons Edition
 
 This repository provides quantized versions of the **Qwen3** language models, optimized for efficient deployment on edge devices and low-resource environments. The following models have been added to our **Lexicons** Model Zoo:
@@ -25,7 +26,7 @@ This repository provides quantized versions of the **Qwen3** language models, op
 
 ## Model Overview
 
-**Qwen3** is the latest open-source LLM series developed by Alibaba Group. Released on **April 28, 2025**, the models were trained on **36 trillion tokens** across **119 languages and dialects**. Qwen3 models are instruction-tuned and support long context windows and multilingual capabilities. This model is described in [An Empirical Study of Qwen3 Quantization](https://arxiv.org/abs/2505.02214).
+**Qwen3** is the latest open-source LLM series developed by Alibaba Group. Released on **April 28, 2025**, the models were trained on **36 trillion tokens** across **119 languages and dialects**. Qwen3 models are instruction-tuned and support long context windows and multilingual capabilities. This model is described in [An Empirical Study of Qwen3 Quantization](https://huggingface.co/papers/2505.02214).
 
 The quantized versions provided here use **4-bit Q4_K_M** precision ensuring high performance at a fraction of the memory and compute cost. These models are ideal for real-time inference, chatbots, and on-device applications.
 
@@ -38,7 +39,6 @@ The quantized versions provided here use **4-bit Q4_K_M** precision ensuring hig
 - **Instruction-Tuned**: Fine-tuned to follow user instructions effectively.
 - **Scalable Sizes**: Choose from 0.6B to 8B parameter models based on your use case.
 
-
 ---
 
 ## Available Quantized Versions