Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: image-to-text
+inference: false
+arxiv: 2304.08485
+---
+# LLaVA Model Card
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/FPshq08TKYD0e-qwPLDVO.png)
+
+Below is the model card of Llava model 13b, which is copied from the original Llava model card that you can find [here](https://huggingface.co/liuhaotian/llava-v1.5-13b).
+
+## 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-v1.5-13B was trained in September 2023.
+
+**Paper or resources for more information:**
+https://llava-vl.github.io/
+
+## How to use the model
+
+First, make sure to have `transformers >= 4.35.3`. 
+The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` to the location where you want to query images:
+
+### Using `pipeline`:
+
+Below we used [`"llava-hf/bakLlava-v1-hf"`](https://huggingface.co/llava-hf/bakLlava-v1-hf) checkpoint.
+
+```python
+from transformers import pipeline
+from PIL import Image    
+import request
+
+model_id = "llava-hf/bakLlava-v1-hf"
+pipe = pipeline("image-to-text", model=model_id)
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
+
+image = Image.open(requests.get(url, stream=True).raw)
+prompt = "<image>\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:"
+
+outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+print(outputs)
+>>> {"generated_text": "\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: Lava"}
+```
+
+### Using pure `transformers`:
+
+Below is an example script to run generation in `float16` precision on a GPU device:
+
+```python
+import requests
+from PIL import Image
+
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+model_id = "llava-hf/llava-1.5-7b-hf"
+
+prompt = "<image> \nUSER: What are these?\nASSISTANT:"
+image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True, 
+).to(0)
+
+
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
+
+output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+print(processor.decode(output[0][2:], skip_special_tokens=True))
+```
+
+### Model optimization
+
+#### 4-bit quantization through `bitsandbytes` library
+
+First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with: 
+
+```diff
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True,
++   load_in_4bit=True
+)
+```
+
+#### Use Flash-Attention 2 to further speed-up generation
+
+First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with: 
+
+```diff
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True,
++   use_flash_attention_2=True
+).to(0)
+```
+
+## License
+Llama 2 is licensed under the LLAMA 2 Community License, 
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.