|
--- |
|
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""" |
|
``` |
