初始化项目，由ModelHub XC社区提供模型

Model: TIGER-Lab/Mantis-llava-7b
Source: Original Platform

README.md

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+---
+tags:
+- Mantis
+- VLM
+- LMM
+- Multimodal LLM
+- llava
+base_model: llava-hf/llava-1.5-7b-hf
+model-index:
+- name: Mantis-llava-7b
+  results: []
+license: apache-2.0
+language:
+- en
+---
+
+# Mantis: Interleaved Multi-Image Instruction Tuning (Deprecated)
+
+**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.
+
+**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.
+
+Mantis is trained on the newly curated dataset **Mantis-Instruct**, a large-scale multi-image QA dataset that covers various multi-image reasoning tasks.
+
+|[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) |  
+
+![Mantis](https://raw.githubusercontent.com/TIGER-AI-Lab/Mantis/main/docs/assets/images/overall_barchart.jpeg)
+
+## Inference
+
+You can install Mantis's GitHub codes as a Python package
+```bash
+pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+```
+then run inference with codes here: [examples/run_mantis.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py)
+
+```python
+from mantis.models.mllava import chat_mllava
+from PIL import Image
+import torch
+
+
+image1 = "image1.jpg"
+image2 = "image2.jpg"
+images = [Image.open(image1), Image.open(image2)]
+
+# load processor and model
+from mantis.models.mllava import MLlavaProcessor, LlavaForConditionalGeneration
+processor = MLlavaProcessor.from_pretrained("TIGER-Lab/Mantis-bakllava-7b")
+model = LlavaForConditionalGeneration.from_pretrained("TIGER-Lab/Mantis-bakllava-7b", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
+
+# chat
+text = "<image> <image> What's the difference between these two images? Please describe as much as you can."
+response, history = chat_mllava(text, images, model, processor)
+
+print("USER: ", text)
+print("ASSISTANT: ", response)
+# 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.
+
+text = "How many items are there in image 1 and image 2 respectively?"
+response, history = chat_mllava(text, images, model, processor, history=history)
+
+print("USER: ", text)
+print("ASSISTANT: ", response)
+# There are two items in image 1 and four items in image 2.
+```
+
+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.
+
+
+## Training
+Training codes will be released soon.