Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- NVILA
+- VLM
+---
+
+# VILA Model Card
+
+## Model details
+
+**Model type:**
+NVILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
+
+**Model date:**
+NVILA was trained in Nov 2024.
+
+**Paper or resources for more information:**
+https://github.com/NVLabs/VILA
+
+```
+@misc{liu2024nvila,
+      title={NVILA: Efficient Frontier Visual Language Models},
+      author={Zhijian Liu and Ligeng Zhu and Baifeng Shi and Zhuoyang Zhang and Yuming Lou and Shang Yang and Haocheng Xi and Shiyi Cao and Yuxian Gu and Dacheng Li and Xiuyu Li and Yunhao Fang and Yukang Chen and Cheng-Yu Hsieh and De-An Huang and An-Chieh Cheng and Vishwesh Nath and Jinyi Hu and Sifei Liu and Ranjay Krishna and Daguang Xu and Xiaolong Wang and Pavlo Molchanov and Jan Kautz and Hongxu Yin and Song Han and Yao Lu},
+      year={2024},
+      eprint={2412.04468},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2412.04468},
+}
+```
+
+## License
+- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
+- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
+- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
+    - [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
+    - [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
+
+**Where to send questions or comments about the model:**
+https://github.com/NVLabs/VILA/issues
+
+## Intended use
+**Primary intended uses:**
+The primary use of VILA is research on large multimodal models and chatbots.
+
+**Primary intended users:**
+The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
+
+## Input:
+**Input Type:** Image, Video, Text
+**Input Format:** Red, Green, Blue; MP4 ;String
+**Input Parameters:** 2D, 3D
+
+## Output: 
+**Output Type:** Text
+**Output Format:** String
+
+**Supported Hardware Microarchitecture Compatibility:**
+* Ampere
+* Jetson
+* Hopper
+* Lovelace
+
+**[Preferred/Supported] Operating System(s):** <br>
+Linux
+
+## Training dataset
+See [Dataset Preparation](https://arxiv.org/abs/2412.04468) for more details.
+
+** Data Collection Method by dataset
+* [Hybrid: Automated, Human]
+
+** Labeling Method by dataset
+* [Hybrid: Automated, Human]
+
+## Inference:
+**Engine:** [Tensor(RT), Triton, Or List Other Here]
+* PyTorch
+* TensorRT-LLM
+* TinyChat
+
+**Test Hardware:**
+* A100
+* Jetson Orin
+* RTX 4090
+
+## Ethical Considerations
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.