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--- |
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tags: |
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- vision |
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- image-text-to-text |
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--- |
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# LLaVa-Next, leveraging [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) as LLM |
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The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Improved reasoning, OCR, and world knowledge](https://llava-vl.github.io/blog/2024-01-30-llava-next/) by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon [LLaVa](llava) by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning. |
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Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA 1.6 improves on LLaVA 1.5 BY: |
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- Using [Mistral-7B](https://mistral.ai/news/announcing-mistral-7b/) and [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) (for this checkpoint) which has better commercial licenses, |
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and bilingual support |
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- More diverse and high quality data mixture |
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- Dynamic high resolution |
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## Intended uses & limitations |
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You can use the raw model for tasks like image captioning, visual question answering, multimodal chatbot use cases. See the [model hub](https://huggingface.co/models?search=llava-hf) to look for |
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other versions on a task that interests you. |
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### How to use |
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Here's the prompt template for this model: |
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``` |
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"[INST] <image>\nWhat is shown in this image? [/INST]" |
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``` |
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You can load and use the model like following: |
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```python |
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from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration |
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import torch |
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from PIL import Image |
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import requests |
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processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-34b-hf") |
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model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-34b-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True) |
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model.to("cuda:0") |
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# prepare image and text prompt, using the appropriate prompt template |
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url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true" |
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image = Image.open(requests.get(url, stream=True).raw) |
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prompt = ""<|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|><|im_start|>assistant\n"" |
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inputs = processor(prompt, image, return_tensors="pt").to("cuda:0") |
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# autoregressively complete prompt |
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output = model.generate(**inputs, max_new_tokens=100) |
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print(processor.decode(output[0], skip_special_tokens=True)) |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{liu2023improved, |
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title={Improved Baselines with Visual Instruction Tuning}, |
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author={Haotian Liu and Chunyuan Li and Yuheng Li and Yong Jae Lee}, |
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year={2023}, |
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eprint={2310.03744}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
