Create model card with pipeline tag, license, and usage instructions

README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# JetSpec: Parallel Tree Drafting
+
+JetSpec is an implementation of **parallel tree drafting** for fast LLM speculative decoding inference with up to 10x acceptance length, and 1000+ TPS on coding and math tasks using B200 GPUs. This repository contains the draft head model presented in [JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting](https://huggingface.co/papers/2606.18394).
+
+A causal-parallel draft head proposes a token tree, and the frozen target model verifies the whole tree in one forward pass under a tree-causal attention mask. The accepted path is selected in accordance with the target's own logits, so decoding is lossless by construction.
+
+For more details, please refer to the [Project Webpage](https://hao-ai-lab.github.io/JetSpec) and the [GitHub Repository](https://github.com/hao-ai-lab/JetSpec).
+
+## Installation
+
+Create an environment and install the package:
+
+```bash
+pip install -e '.[bench,kernel]'
+```
+
+## Usage
+
+You can run speculative decoding using the lightweight Hugging Face-based reference implementation:
+
+```python
+from jetspec import LLM, SamplingParams
+
+llm = LLM("Qwen/Qwen3-8B", attn_implementation="flash_attention_2")
+out = llm.generate(
+    "The three primary colors are",
+    SamplingParams(temperature=0.0, max_new_tokens=64),
+)
+print(out["text"])
+```
+
+## Citation
+
+```bibtex
+@inproceedings{jetspec2026,
+  title = {JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting},
+  author = {Hu, Lanxiang and Feng, Zhaoxiang and Wu, Yulun and Yuan, Haoran and Zhao, Yujie and Qian, Yu-Yang and Wang, Bojun and Zhao, Peng and Jiang, Daxin and Zhu, Yibo and Rosing, Tajana and Zhang, Hao},
+  year = {2026},
+  url = {https://arxiv.org/abs/2606.18394},
+  eprint = {2606.18394},
+  note = {Preprint}
+}
+```