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README.md

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-<h1 align='center'>WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Driving</h1>
-<div align='center'>
-    <a href='https://github.com/YoucanBaby' target='_blank'>Yifang Xu</a><sup>1*</sup>&emsp;
-    <a href='https://cuijh26.github.io/' target='_blank'>Jiahao Cui</a><sup>1*</sup>&emsp;
-    <a href='https://github.com/fudan-generative-vision/WAM-Flow' target='_blank'>Feipeng Cai</a><sup>2*</sup>&emsp;
-    <a href='https://github.com/SSSSSSuger' target='_blank'>Zhihao Zhu</a><sup>1</sup>&emsp;
-    <a href='https://github.com/NinoNeumann' target='_blank'>Hanlin Shang</a><sup>1</sup>&emsp;
-    <a href='https://github.com/isan089' target='_blank'>Shan Luan</a><sup>1</sup>&emsp;
-</div>
-<div align='center'>
-    <a href='https://github.com/xumingw' target='_blank'>Mingwang Xu</a><sup>1</sup>&emsp;
-    <a href='https://github.com/fudan-generative-vision/WAM-Flow' target='_blank'>Neng Zhang</a><sup>2</sup>&emsp;
-    <a href='https://github.com/fudan-generative-vision/WAM-Flow' target='_blank'>Yaoyi Li</a><sup>2</sup>&emsp;
-    <a href='https://github.com/fudan-generative-vision/WAM-Flow‘ target='_blank'>Jia Cai</a><sup>2</sup>&emsp;
-    <a href='https://sites.google.com/site/zhusiyucs/home' target='_blank'>Siyu Zhu</a><sup>1</sup>&emsp;
-</div>
-
-<div align='center'>
-    <sup>1</sup>Fudan University&emsp; <sup>2</sup>Yinwang Intelligent Technology Co., Ltd&emsp;
-</div>
-
-<br>
-<div align='center'>
-    <a href='https://github.com/fudan-generative-vision/WAM-Flow'><img src='https://img.shields.io/github/stars/fudan-generative-vision/WAM-Flow?style=social'></a>
-    <a href='https://arxiv.org/abs/2512.06112'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
-    <a href='https://huggingface.co/fudan-generative-ai/WAM-Flow'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Model-yellow'></a>
-</div>
-<br>
-
-
-
-## 📰 News
-- **`2026/02/01`**: 🎉🎉🎉 Release the pretrained models on [Huggingface](https://huggingface.co/fudan-generative-ai/WAM-Flow).
-- **`2025/12/06`**: 🎉🎉🎉 Paper submitted on [Arxiv](https://arxiv.org/pdf/2512.06112).
-
-
-
-## 📅️ Roadmap
-
-| Status | Milestone                                                                                             |    ETA     |
-| :----: | :----------------------------------------------------------------------------------------------------: | :--------: |
-|   ✅   | **[Release the SFT and inference code](https://github.com/fudan-generative-vision/WAM-Flow)**   | 2025.12.19 |
-|   ✅   | **[Pretrained models on Huggingface](https://huggingface.co/fudan-generative-ai/WAM-Flow)**    | 2026.02.01        |
-|   🚀   | **[Release the evaluation code](https://huggingface.co/fudan-generative-ai/WAM-Flow)**    | TBD |
-|   🚀   | **[Release the RL code](https://github.com/fudan-generative-vision/WAM-Flow)**   | TBD |
-|   🚀   | **[Release the pre-processed training data](#training)**                                       | TBD        |
-
-
-## 📸 Showcase
-![teaser](assets/Figure_1.png)
-
-## 🏆 Qualitative Results on NAVSIM
-### NAVSIM-v1 benchmark results
-<div style="text-align: center;">
-  <img src="assets/navsim-v1.png" alt="navsim-v1" width="70%" />
-</div>
-
-### NAVSIM-v2 benchmark results
-<div style="text-align: center;">
-<img src="assets/navsim-v2.png" alt="navsim-v2" width="70%" />
-</div>
-
-
-
-## 🔧️ Framework
-![framework](assets/Figure_2.png)
-Our method takes as input a front-view image, a natural-language navigation command with a system prompt, and the ego-vehicle states, and outputs an 8-waypoint future trajectory spanning 4 seconds through parallel denoising. The model is first trained via supervised fine-tuning to learn accurate trajectory prediction. We then apply simulatorguided GRPO to further optimize closed-loop behavior. The GRPO reward function integrates safety constraints (collision avoidance, drivable-area compliance) with performance objectives (ego-progress, time-to-collision, comfort).
-
-
-
-## Quick Start
-
-### Installation
-
-Clone the repo:
-
-```sh
-git clone https://github.com/fudan-generative-vision/WAM-Flow.git
-cd WAM-Flow
-```
-
-Install dependencies:
-
-```sh
-conda create --name wam-flow python=3.10
-conda activate wam-flow
-pip install -r requirements.txt
-```
-
-
-### Model Download
-
-Download models using huggingface-cli:
-
-```sh
-pip install "huggingface_hub[cli]"
-huggingface-cli download fudan-generative-ai/WAM-Flow --local-dir ./pretrained_model/wam-flow
-huggingface-cli download LucasJinWang/FUDOKI --local-dir ./pretrained_model/fudoki
-```
-
-
-
-### Inference
-
-```sh
-sh script/infer.sh
-```
-
-
-### Training
-
-```bash
-sh script/sft_debug.sh
-```
-
-
-
-## 📝 Citation
-
-If you find our work useful for your research, please consider citing the paper:
-
-```
-@article{xu2025wam,
-  title={WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Driving},
-  author={Xu, Yifang and Cui, Jiahao and Cai, Feipeng and Zhu, Zhihao and Shang, Hanlin and Luan, Shan and Xu, Mingwang and Zhang, Neng and Li, Yaoyi and Cai, Jia and others},
-  journal={arXiv preprint arXiv:2512.06112},
-  year={2025}
-}
-```
-
-
-
-## ⚠️ Social Risks and Mitigations
-
-The integration of Vision-Language-Action models into autonomous driving introduces ethical challenges, particularly regarding the opacity of neural decision-making and its impact on road safety. To mitigate these risks, it is imperative to implement explainable AI frameworks and robust safe protocols that ensure predictable vehicle behavior in long-tailed scenarios. Furthermore, addressing concerns over data privacy and public surveillance requires transparent data governance and rigorous de-identification practices. By prioritizing safety-critical alignment and ethical compliance, this research promotes the responsible development and deployment of VLA-based autonomous systems.
-
-
-
-## 🤗 Acknowledgements
-We gratefully acknowledge the contributors to the [Recogdrive](https://github.com/xiaomi-research/recogdrive), [Janus](https://github.com/deepseek-ai/Janus), [FUDOKI](https://github.com/fudoki-hku/FUDOKI) and [flow_matching](https://github.com/facebookresearch/flow_matching) repositories, whose commitment to open source has provided us with their excellent codebases and pretrained models.
+---
+license: apache-2.0
+---

best_model_epoch95.pt

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config.json

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-{
-  "_name_or_path": "/cache/models/LucasJinWang-FUDOKI",
-  "aligner_config": {
-    "cls": "MlpProjector",
-    "model_type": "aligner",
-    "params": {
-      "depth": 2,
-      "input_dim": 1024,
-      "n_embed": 2048,
-      "projector_type": "mlp_gelu"
-    }
-  },
-  "architectures": [
-    "MultiModalityCausalLM"
-  ],
-  "gen_aligner_config": {
-    "cls": "MlpProjector",
-    "model_type": "gen_aligner",
-    "params": {
-      "depth": 2,
-      "input_dim": 8,
-      "n_embed": 2048,
-      "projector_type": "mlp_gelu"
-    }
-  },
-  "gen_head_config": {
-    "cls": "vision_head",
-    "model_type": "gen_head",
-    "params": {
-      "image_token_embed": 2048,
-      "image_token_size": 16384,
-      "n_embed": 2048
-    }
-  },
-  "gen_vision_config": {
-    "cls": "VQ-16",
-    "model_type": "gen_vision",
-    "params": {
-      "image_token_size": 16384,
-      "n_embed": 8
-    }
-  },
-  "language_config": {
-    "hidden_size": 2048,
-    "intermediate_size": 5632,
-    "max_position_embeddings": 16384,
-    "model_type": "llama",
-    "num_attention_heads": 16,
-    "num_hidden_layers": 24,
-    "num_key_value_heads": 16,
-    "torch_dtype": "bfloat16",
-    "vocab_size": 122401
-  },
-  "model_type": "multi_modality",
-  "torch_dtype": "float32",
-  "transformers_version": "4.42.4",
-  "vision_config": {
-    "cls": "CLIPVisionTower",
-    "model_type": "vision",
-    "params": {
-      "image_size": 384,
-      "model_name": "siglip_large_patch16_384",
-      "select_feature": "same",
-      "select_layer": -1
-    }
-  }
-}

model.safetensors

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