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runninglsy authored Nov 4, 2024
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Expand Up @@ -7,6 +7,7 @@ Ovis (Open VISion) is a novel Multimodal Large Language Model (MLLM) architectur
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## Release
- [11/04] 🔥 Announcing quantized Ovis1.6: [Ovis1.6-Gemma2-9B-GPTQ-Int4](https://huggingface.co/AIDC-AI/Ovis1.6-Gemma2-9B-GPTQ-Int4) and [Ovis1.6-Llama3.2-3B-GPTQ-Int4](https://huggingface.co/AIDC-AI/Ovis1.6-Llama3.2-3B-GPTQ-Int4)!
- [10/22] 🔥 Announcing Ovis1.6-Llama3.2-3B ([Model](https://huggingface.co/AIDC-AI/Ovis1.6-Llama3.2-3B), [Demo](https://huggingface.co/spaces/AIDC-AI/Ovis1.6-Llama3.2-3B))!
- [09/19] 🔥 Announcing Ovis1.6-Gemma2-9B ([Model](https://huggingface.co/AIDC-AI/Ovis1.6-Gemma2-9B), [Demo](https://huggingface.co/spaces/AIDC-AI/Ovis1.6-Gemma2-9B))! This latest release further enhances high-resolution image processing, is trained on a larger, more diverse, and higher-quality dataset, and refines the training process with DPO training following instruction-tuning.
- [07/24] 🔥 Introducing Ovis1.5, featuring improved high-resolution image processing and optimized training data for enhanced performance.
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- [Performance](#performance)
- [Finetune](#finetune)
- [Inference](#inference)
- [Quantization](#quantization)
- [Citation](#citation)
- [Team](#team)
- [License](#license)
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python ovis/serve/server.py --model_path MODEL_PATH --port PORT
```

## Quantization
We quantized Ovis1.6 using AutoGPTQ. For detailed information on running and creating your own quantized version, please refer to the respective Huggingface model cards: [Ovis1.6-Gemma2-9B-GPTQ-Int4](https://huggingface.co/AIDC-AI/Ovis1.6-Gemma2-9B-GPTQ-Int4) and [Ovis1.6-Llama3.2-3B-GPTQ-Int4](https://huggingface.co/AIDC-AI/Ovis1.6-Llama3.2-3B-GPTQ-Int4). Quantized Ovis1.6 maintains performance comparable to its non-quantized counterpart while requiring less GPU memory:

![performance-Ovis1_6-Gemma2-9B-GPTQ-Int4](docs/performance/Ovis1_6-Gemma2-9B-GPTQ-Int4.png)
![performance-Ovis1_6-Llama3_2-3B-GPTQ-Int4](docs/performance/Ovis1_6-Llama3_2-3B-GPTQ-Int4.png)
![performance-Ovis1_6-VRAM-Comparison](docs/performance/Ovis1_6-VRAM-Comparison.png)

## Citation
If you find Ovis useful, please cite the paper
```
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