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README.md

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+---
+license: cc-by-nc-4.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- VILA
+- VLM
+---
+
+# VILA Model Card
+
+## Model details
+
+**Model type:**
+VILA 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:**
+VILA-2.7b was trained in Feb 2024.
+
+**Paper or resources for more information:**
+https://github.com/Efficient-Large-Model/VILA
+
+```
+@misc{lin2023vila,
+      title={VILA: On Pre-training for Visual Language Models},
+      author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
+      year={2023},
+      eprint={2312.07533},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+## 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:
+    - [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
+    - [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/Efficient-Large-Model/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.
+
+## Training dataset
+See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
+
+## Evaluation dataset
+A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.