Update README.md

README.md

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 ---
 license: mit
 library_name: transformers
----
+---
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/66a056d0229269a861ac1245/UmJOD5HnhCfvy3nAXgxgE.png" alt="PARD" width="100" align="left">
+<div align="center">
+<h1>PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding</h1>
+</div>
+
+
+<p align="center"> |
+<a href="https://arxiv.org/pdf/2605.08632"><b>Paper</b></a> |
+<a href="https://github.com/AMD-AIG-AIMA/PARD"><b>Github</b></a> |
+</p>
+
+<br clear="left"/>
+
+## Introduction
+
+PARD is a high-performance speculative decoding method that also enables low-cost adaptation of autoregressive draft models into parallel draft models. PARD-2 further advances PARD by introducing a Target-Aligned Parallel Draft Model for dual-mode speculative decoding. Instead of optimizing draft models only for token-level prediction accuracy, PARD-2 aligns draft-model training with the inference-time objective of maximizing consecutive token acceptance. PARD-2 offers the following advantages:
+
+- **Target-Aligned Optimization**: PARD-2 reformulates the draft-model objective from next-token prediction accuracy to acceptance-length optimization, better matching the draft-then-verify process used during speculative decoding.
+
+- **Confidence-Adaptive Token Optimization**: PARD-2 introduces Confidence-Adaptive Token (CAT) optimization, which adaptively reweights tokens according to their contribution to the verification process. This improves the alignment between draft generation and target-model acceptance.
+
+- **Dual-Mode Speculative Decoding**: A single PARD-2 draft model supports both target-independent and target-dependent modes, combining the deployment flexibility of PARD with the stronger alignment capability of target-aware methods.
+
+State-of-the-Art Performance: Across diverse models and tasks, PARD-2 achieves up to 6.94× lossless acceleration. On LLaMA3.1-8B, PARD-2 surpasses EAGLE-3 by 1.9× and PARD by 1.3×, setting a new performance frontier for speculative decoding.
+
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/AMD-AGI/PARD/master/datas/img/pard_2.png" width="100%">
+  <br>
+  <em>Throughput and Latency Trade-offs on vLLM. PARD-2 consistently achieves a superior Pareto frontier across various batch sizes from 1 to 64.</em>
+</p>
+
+
+## Model Weights
+
+| Model Series | Model Name                            | Download      |
+|--------------|---------------------------------------|---------------|
+| Llama3       | amd/PARD2-Llama-3.1-8B                   | [🤗 HuggingFace](https://huggingface.co/amd/PARD2-Llama-3.1-8B )  |
+| Qwen3        | amd/PARD2-Qwen3-8B                     | [🤗 HuggingFace](https://huggingface.co/amd/PARD2-Qwen3-8B) |
+| Qwen3        | amd/PARD2-Qwen3-14B                      | [🤗 HuggingFace](https://huggingface.co/amd/PARD2-Qwen3-14B) |
+
+## How To Use
+
+Please visit [PARD2](https://github.com/AMD-AIG-AIMA/PARD) repo for more information
+
+
+## Citation
+```
+@article{an2026pard,
+  title={PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding},
+  author={An, Zihao and Liu, Taichi and Liu, Ziqiong and Li, Dong and Liu, Ruofeng and Barsoum, Emad},
+  journal={arXiv preprint arXiv:2605.08632},
+  year={2026}
+}
+```