Create README.md

README.md

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+---
+inference: false
+language:
+- en
+tags:
+- 'LLaMA '
+- MultiModal
+---
+<br>
+<br>
+
+*Copied and modified from https://huggingface.co/liuhaotian/llava-llama-2-13b-chat-lightning-preview*
+
+# LLaVA Model Card
+
+## Model details
+
+**Model type:**
+LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
+It is an auto-regressive language model, based on the transformer architecture.
+
+**Model date:**
+LLaVA-LLaMA-2-13B-Chat-Preview was trained in July 2023.
+
+**Paper or resources for more information:**
+https://llava-vl.github.io/
+
+## License
+Llama 2 is licensed under the LLAMA 2 Community License, 
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+
+**Where to send questions or comments about the model:**
+https://github.com/haotian-liu/LLaVA/issues
+
+## Intended use
+**Primary intended uses:**
+The primary use of LLaVA is research on large multimodal models and chatbots.
+
+**Primary intended users:**
+The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
+
+## Training dataset
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 80K GPT-generated multimodal instruction-following data.
+
+## Evaluation dataset
+A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs.
+We also evaluate our model on the ScienceQA dataset.  Our synergy with GPT-4 sets a new state-of-the-art on the dataset.
+See https://llava-vl.github.io/ for more details.
+
+## Usage
+usage is as follows 
+
+```python
+from transformers import LlavaProcessor, LlavaLlamaForCausalLM
+
+PATH_TO_CONVERTED_WEIGHTS = "shauray/Llava-Llama-2-7B-hf"
+
+model = LlavaLlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+processor = LlavaProcessor.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+url = "https://llava-vl.github.io/static/images/view.jpg"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+prompt = "How would you best describe the image given?"
+
+inputs = processor(text=prompt, images=image, return_tensors="pt")
+# Generate
+generate_ids = model.generate(**inputs, max_length=30)
+tokenizer.batch_decode(generate_ids, skip_special_tokens=True)[0]
+"""The photograph shows a wooden dock floating on the water, with mountains in the background. It is an idyllic scene that captures both
+nature and human-made structures at their finest moments of beauty or tranquility depending upon one's perspective as they gaze into it"""
+```