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--- |
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tags: |
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- Mantis |
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- VLM |
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- LMM |
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- Multimodal LLM |
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- llava |
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base_model: llava-hf/llava-1.5-7b-hf |
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model-index: |
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- name: Mantis-llava-7b |
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results: [] |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# Mantis: Interleaved Multi-Image Instruction Tuning (Deprecated) |
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**Mantis** is a multimodal conversational AI model that can chat with users about images and text. It's optimized for multi-image reasoning, where interleaved text and images can be used to generate responses. |
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**Note that this is an older version of Mantis**, please refer to our newest version at [mantis-Siglip-llama3](https://huggingface.co/TIGER-Lab/Mantis-8B-siglip-llama3). The newer version improves significantly over both multi-image and single-image tasks. |
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Mantis is trained on the newly curated dataset **Mantis-Instruct**, a large-scale multi-image QA dataset that covers various multi-image reasoning tasks. |
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|[Demo](https://huggingface.co/spaces/TIGER-Lab/Mantis) | [Github](https://github.com/TIGER-AI-Lab/Mantis) | [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) | |
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## Inference |
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You can install Mantis's GitHub codes as a Python package |
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```bash |
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pip install git+https://github.com/TIGER-AI-Lab/Mantis.git |
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``` |
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then run inference with codes here: [examples/run_mantis.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py) |
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```python |
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from mantis.models.mllava import chat_mllava |
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from PIL import Image |
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import torch |
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image1 = "image1.jpg" |
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image2 = "image2.jpg" |
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images = [Image.open(image1), Image.open(image2)] |
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# load processor and model |
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from mantis.models.mllava import MLlavaProcessor, LlavaForConditionalGeneration |
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processor = MLlavaProcessor.from_pretrained("TIGER-Lab/Mantis-bakllava-7b") |
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model = LlavaForConditionalGeneration.from_pretrained("TIGER-Lab/Mantis-bakllava-7b", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2") |
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# chat |
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text = "<image> <image> What's the difference between these two images? Please describe as much as you can." |
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response, history = chat_mllava(text, images, model, processor) |
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print("USER: ", text) |
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print("ASSISTANT: ", response) |
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# The image on the right has a larger number of wallets displayed compared to the image on the left. The wallets in the right image are arranged in a grid pattern, while the wallets in the left image are displayed in a more scattered manner. The wallets in the right image have various colors, including red, purple, and brown, while the wallets in the left image are primarily brown. |
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text = "How many items are there in image 1 and image 2 respectively?" |
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response, history = chat_mllava(text, images, model, processor, history=history) |
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print("USER: ", text) |
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print("ASSISTANT: ", response) |
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# There are two items in image 1 and four items in image 2. |
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``` |
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Or, you can run the model without relying on the mantis codes, using pure hugging face transformers. See [examples/run_mantis_hf.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py) for details. |
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## Training |
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Training codes will be released soon. |
