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README.md

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 license: apache-2.0
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+# LMDrive Model Card
+
+## Model details
+
+**Model type:**
+LMDrive is an end-to-end, closed-loop, language-based autonomous driving framework, which interacts with the dynamic environment via multi-modal multi-view sensor data and natural language instructions.
+
+**Model date:**
+LMDrive-1.0 (based on Vicuna-v1.5-7B) was trained in November 2023. The original Vicuna-v1.5 also needs to be downloaded.
+
+**Paper or resources for more information:**
+
+Github: https://github.com/opendilab/LMDrive/README.md
+
+Paper: https://arxiv.org/abs/2312.07488
+
+**Related weights for the vision encoder**
+
+https://huggingface.co/deepcs233/LMDrive-vision-encoder-r50-v1.0
+
+**Where to send questions or comments about the model:**
+
+https://github.com/opendilab/LMDrive/issues
+
+
+
+## Intended use
+**Primary intended uses:**
+
+The primary use of LMDrive is research on large multimodal models for autonomous driving.
+
+**Primary intended users:**
+
+The primary intended users of the model are researchers and hobbyists in computer vision, large multimodal model, autonomous driving, and artificial intelligence.
+
+## Training dataset
+
+- 64K instruction-sensor-control data clips collected in the CARLA simulator. [dataset_webpage](https://huggingface.co/datasets/deepcs233/LMDrive)
+  - where each clip includes one navigation instruction, several notice instructions, a sequence of multi-modal multi-view sensor data, and control signals. The duration of the clip spans from 2 to 20 seconds 
+
+## Evaluation benchmark
+LangAuto, LangAuto-short, LangAuto-tiny, LangAuto-notice