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  license: apache-2.0
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  license: apache-2.0
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+
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+ <p align="center">
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+ <img src="https://s11.ax1x.com/2023/12/28/piqvDMV.png" width="250" style="margin-bottom: 0.2;"/>
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+ <p>
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+ <h2 align="center"> <a href="https://arxiv.org/abs/2401.15947">MoE-LLaVA: Mixture of Experts for Large Vision-Language Models</a></h2>
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+ <h5 align="center"> If you like our project, please give us a star โญ on GitHub for latest update. </h2>
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+
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+ <h5 align="center">
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+
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+
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+
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+ </h5>
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+
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+
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+ ## ๐Ÿ“ฐ News
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+ * **[2024.01.30]** The [paper](https://arxiv.org/abs/2401.15947) is released.
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+ * **[2024.01.27]** ๐Ÿค—[Hugging Face demo](https://huggingface.co/spaces/LanguageBind/MoE-LLaVA) and **all codes & datasets** are available now! Welcome to **watch** ๐Ÿ‘€ this repository for the latest updates.
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+
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+ ## ๐Ÿ˜ฎ Highlights
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+
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+ MoE-LLaVA shows excellent performance in multi-modal learning.
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+
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+ ### ๐Ÿ”ฅ High performance, but with fewer parameters
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+ - with just **3B sparsely activated parameters**, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmarks.
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+
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+
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+ ### ๐Ÿš€ Simple baseline, learning multi-modal interactions with sparse pathways.
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+ - With the addition of **a simple MoE tuning stage**, we can complete the training of MoE-LLaVA on **8 V100 GPUs** within 2 days.
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+
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+
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+
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+ ## ๐Ÿค— Demo
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+
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+ ### Gradio Web UI
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+
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+ Highly recommend trying out our web demo by the following command, which incorporates all features currently supported by MoE-LLaVA. We also provide [online demo](https://huggingface.co/spaces/LanguageBind/MoE-LLaVA) in Huggingface Spaces.
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+ ```bash
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+ # use phi2
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+ deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-Phi2-2.7B-4e"
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+ # use qwen
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+ deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-Qwen-1.8B-4e"
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+ # use stablelm
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+ deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-StableLM-1.6B-4e"
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+ ```
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+
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+
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+
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+ ### CLI Inference
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+
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+ ```bash
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+ # use phi2
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+ deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-Phi2-2.7B-4e" --image-file "image.jpg"
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+ # use qwen
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+ deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-Qwen-1.8B-4e" --image-file "image.jpg"
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+ # use stablelm
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+ deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-StableLM-1.6B-4e" --image-file "image.jpg"
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+ ```
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+
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+
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+ ## ๐Ÿณ Model Zoo
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+
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+ | Model | LLM | Checkpoint | Avg | VQAv2 | GQA | VizWiz | SQA | T-VQA | POPE | MM-Bench| LLaVA-Bench-Wild | MM-Vet |
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+ |----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---|
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+ | MoE-LLaVA-1.6Bร—4-Top2 | 1.6B | [LanguageBind/MoE-LLaVA-StableLM-1.6B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-1.6B-4e) | 60.0 | 76.0 | 60.4 | 37.2 | 62.6 | 47.8 | 84.3 | 59.4 | 85.9 | 26.1 |
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+ | MoE-LLaVA-1.8Bร—4-Top2 | 1.8B | [LanguageBind/MoE-LLaVA-Qwen-1.8B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-Qwen-1.8B-4e) | 60.2 | 76.2 | 61.5 | 32.6 | 63.1 | 48.0 | 87.0 | 59.6 | 88.7 | 25.3 |
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+ | MoE-LLaVA-2.7Bร—4-Top2 | 2.7B | [LanguageBind/MoE-LLaVA-Phi2-2.7B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-2.7B-4e) | 63.9 | 77.1 | 61.1 | 43.4 | 68.7 | 50.2 | 85.0 | 65.5 | 93.2 | 31.1 |
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+
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+ <!--
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+ | LLaVA-1.5 | 7B | [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 62.0 | 78.5 | 62.0 | 50.0 | 66.8 | 58.2 | 85.9 | 64.3 | 31.1 |
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+ | LLaVA-1.5 | 13B | [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) | 64.9 | 80.0 | 63.3 | 53.6 | 71.6 | 61.3 | 85.9 | 67.7 | 36.1 |
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+ -->
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+
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+ ## โš™๏ธ Requirements and Installation
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+ * Python >= 3.10
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+ * Pytorch == 2.0.1
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+ * CUDA Version >= 11.7
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+ * **Transformers == 4.36.2**
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+ * **Tokenizers==0.15.1**
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+ * Install required packages:
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+ ```bash
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+ git clone https://github.com/PKU-YuanGroup/MoE-LLaVA
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+ cd MoE-LLaVA
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+ conda create -n moellava python=3.10 -y
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+ conda activate moellava
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+ pip install --upgrade pip # enable PEP 660 support
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+ pip install -e .
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+ pip install -e ".[train]"
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+ pip install flash-attn --no-build-isolation
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+
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+ # Below are optional. For Qwen model.
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+ git clone https://github.com/Dao-AILab/flash-attention
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+ cd flash-attention && pip install .
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+ # Below are optional. Installing them might be slow.
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+ # pip install csrc/layer_norm
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+ # If the version of flash-attn is higher than 2.1.1, the following is not needed.
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+ # pip install csrc/rotary
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+ ```
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+
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+ ## ๐Ÿ—๏ธ Training & Validating
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+ The training & validating instruction is in [TRAIN.md](docs/TRAIN.md) & [EVAL.md](docs/EVAL.md).
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+
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+ ## ๐Ÿ’ก Customizing your MoE-LLaVA
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+ The instruction is in [CUSTOM.md](docs/CUSTOM.md).
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+
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+ ## ๐Ÿ˜ Visualization
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+ The instruction is in [VISUALIZATION.md](docs/VISUALIZATION.md).
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+
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+ ## ๐Ÿค– API
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+ **We open source all codes.** If you want to load the model (e.g. ```LanguageBind/MoE-LLaVA```) on local, you can use the following code snippets.
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+
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+ **Using the following command to run the code.**
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+
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+ ```bash
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+ deepspeed predict.py
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+ ```
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+
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+ ```python
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+ import torch
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+ from moellava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
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+ from moellava.conversation import conv_templates, SeparatorStyle
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+ from moellava.model.builder import load_pretrained_model
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+ from moellava.utils import disable_torch_init
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+ from moellava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
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+
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+ def main():
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+ disable_torch_init()
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+ image = 'moellava/serve/examples/extreme_ironing.jpg'
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+ inp = 'What is unusual about this image?'
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+ model_path = 'LanguageBind/MoE-LLaVA-Phi2-2.7B-4e' # LanguageBind/MoE-LLaVA-Qwen-1.8B-4e or LanguageBind/MoE-LLaVA-StableLM-1.6B-4e
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+ device = 'cuda'
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+ load_4bit, load_8bit = False, False # FIXME: Deepspeed support 4bit or 8bit?
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+ model_name = get_model_name_from_path(model_path)
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+ tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device)
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+ image_processor = processor['image']
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+ conv_mode = "phi" # qwen or stablelm
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+ conv = conv_templates[conv_mode].copy()
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+ roles = conv.roles
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+ image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].to(model.device, dtype=torch.float16)
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+
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+ print(f"{roles[1]}: {inp}")
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+ inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
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+ conv.append_message(conv.roles[0], inp)
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+ conv.append_message(conv.roles[1], None)
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+ prompt = conv.get_prompt()
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+ input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
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+ stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
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+ keywords = [stop_str]
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+ stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
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+
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+ with torch.inference_mode():
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+ output_ids = model.generate(
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+ input_ids,
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+ images=image_tensor,
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+ do_sample=True,
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+ temperature=0.2,
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+ max_new_tokens=1024,
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+ use_cache=True,
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+ stopping_criteria=[stopping_criteria])
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+
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+ outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=True).strip()
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+ print(outputs)
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+
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+ if __name__ == '__main__':
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+ main()
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+ ```
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+
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+ ## ๐Ÿ™Œ Related Projects
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+ * [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) This framework empowers the model to efficiently utilize the united visual tokens.
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+ * [LanguageBind](https://github.com/PKU-YuanGroup/LanguageBind) An open source five modalities language-based retrieval framework.
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+
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+ ## ๐Ÿ‘ Acknowledgement
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+ * [LLaVA](https://github.com/haotian-liu/LLaVA) The codebase we built upon and it is an efficient large language and vision assistant.
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+
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+ ## ๐Ÿ”’ License
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+ * The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/LICENSE) file.
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+ * The service is a research preview intended for non-commercial use only, subject to the 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, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
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+
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+
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+
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+ ## โœ๏ธ Citation
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+ If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
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+
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+ ```BibTeX
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+ @misc{lin2024moellava,
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+ title={MoE-LLaVA: Mixture of Experts for Large Vision-Language Models},
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+ author={Bin Lin and Zhenyu Tang and Yang Ye and Jiaxi Cui and Bin Zhu and Peng Jin and Junwu Zhang and Munan Ning and Li Yuan},
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+ year={2024},
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+ eprint={2401.15947},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV}
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+ }
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+ ```
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+
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+ ```BibTeX
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+ @article{lin2023video,
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+ title={Video-LLaVA: Learning United Visual Representation by Alignment Before Projection},
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+ author={Lin, Bin and Zhu, Bin and Ye, Yang and Ning, Munan and Jin, Peng and Yuan, Li},
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+ journal={arXiv preprint arXiv:2311.10122},
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+ year={2023}
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+ }
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+ ```
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+
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+
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+
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+ ## โœจ Star History
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+ [![Star History](https://api.star-history.com/svg?repos=PKU-YuanGroup/MoE-LLaVA&type=Date)](https://star-history.com/#PKU-YuanGroup/MoE-LLaVA&Date)
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+
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+
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+ ## ๐Ÿค Contributors
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+
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+ <a href="https://github.com/PKU-YuanGroup/MoE-LLaVA/graphs/contributors">
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+ <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/MoE-LLaVA" />
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+ </a>