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--- |
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inference: false |
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language: |
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- en |
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tags: |
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- 'LLaMA ' |
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- MultiModal |
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--- |
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*This is a Hugging Face friendly Model, the original can be found at https://huggingface.co/liuhaotian/llava-llama-2-7b-chat-lightning-lora-preview* |
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<br> |
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# LLaVA Model Card |
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## Model details |
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**Model type:** |
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LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. |
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It is an auto-regressive language model, based on the transformer architecture. |
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**Model date:** |
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LLaVA-v1.5-7B was trained in September 2023. |
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**Paper or resources for more information:** |
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https://llava-vl.github.io/ |
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## License |
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Llama 2 is licensed under the LLAMA 2 Community License, |
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Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
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**Where to send questions or comments about the model:** |
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https://github.com/haotian-liu/LLaVA/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of LLaVA is research on large multimodal models and chatbots. |
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**Primary intended users:** |
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. |
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- 158K GPT-generated multimodal instruction-following data. |
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- 450K academic-task-oriented VQA data mixture. |
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- 40K ShareGPT data. |
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## Evaluation dataset |
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A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. |
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## Usage |
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usage is as follows |
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```python |
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from transformers import LlavaProcessor, LlavaForCausalLM |
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from PIL import Image |
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import requests |
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import torch |
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PATH_TO_CONVERTED_WEIGHTS = "shauray/Llava-1.5-7B-hf" |
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model = LlavaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS, |
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device_map="cuda",torch_dtype=torch.float16).to("cuda") |
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processor = LlavaProcessor.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) |
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url = "https://llava-vl.github.io/static/images/view.jpg" |
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB") |
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prompt = "How can you best describe this image?" |
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inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda", |
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torch.float16) |
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# Generate |
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generate_ids = model.generate(**inputs, |
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do_sample=True, |
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max_length=1024, |
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temperature=0.1, |
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top_p=0.9, |
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) |
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out = processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip() |
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print(out) |
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"""The photograph shows a wooden dock floating on the water, with mountains in the background. It is an idyllic scene that captures both |
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nature and human-made structures at their finest moments of beauty or tranquility depending upon one's perspective as they gaze into it""" |
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``` |
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