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---
inference: false
language:
- en
tags:
- 'LLaMA '
- MultiModal
---
*This is a Hugging Face friendly Model, the original can be found at https://huggingface.co/liuhaotian/llava-llama-2-13b-chat-preview*
<br>
# LLaVA 13B 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, LlavaForCausalLM
from PIL import Image
import requests
import torch
PATH_TO_CONVERTED_WEIGHTS = "shauray/Llava-Llama-2-13B-hf"
model = LlavaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS,
device_map="cuda",torch_dtype=torch.float16).to("cuda")
processor = LlavaProcessor.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
url = "https://llava-vl.github.io/static/images/view.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
prompt = "How can you best describe this image?"
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda",
torch.float16)
# Generate
generate_ids = model.generate(**inputs,
do_sample=True,
max_length=1024,
temperature=0.1,
top_p=0.9,
)
out = processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()
print(out)
"""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"""
``` |