Spaces:
Running
Running
File size: 2,869 Bytes
94e735e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import streamlit as st
from streamlit_extras.switch_page_button import switch_page
st.title("LLaVA-NeXT")
st.success("""[Original tweet](https://twitter.com/mervenoyann/status/1770832875551682563) (March 21, 2024)""", icon="βΉοΈ")
st.markdown(""" """)
st.markdown("""LLaVA-NeXT is recently merged to π€ Transformers and it outperforms many of the proprietary models like Gemini on various benchmarks!π€©
For those who don't know LLaVA, it's a language model that can take image π¬
Let's take a look, demo and more in this.
""")
st.markdown(""" """)
st.image("pages/LLaVA-NeXT/image_1.jpeg", use_column_width=True)
st.markdown(""" """)
st.markdown("""
LLaVA is essentially a vision-language model that consists of ViT-based CLIP encoder, a MLP projection and Vicuna as decoder β¨
LLaVA 1.5 was released with Vicuna, but LLaVA NeXT (1.6) is released with four different LLMs:
- Nous-Hermes-Yi-34B
- Mistral-7B
- Vicuna 7B & 13B
""")
st.markdown(""" """)
st.image("pages/LLaVA-NeXT/image_2.jpeg", use_column_width=True)
st.markdown(""" """)
st.markdown("""
Thanks to Transformers integration, it is very easy to use LLaVA NeXT, not only standalone but also with 4-bit loading and Flash Attention 2 π
See below on standalone usage π
""")
st.markdown(""" """)
st.image("pages/LLaVA-NeXT/image_3.jpeg", use_column_width=True)
st.markdown(""" """)
st.markdown("""To fit large models and make it even faster and memory efficient, you can enable Flash Attention 2 and load model into 4-bit using bitsandbytes β‘οΈ transformers makes it very easy to do this! See below π
""")
st.markdown(""" """)
st.image("pages/LLaVA-NeXT/image_4.jpeg", use_column_width=True)
st.markdown(""" """)
st.markdown("""If you want to try the code right away, here's the [notebook](https://t.co/NvoxvY9z1u).
Lastly, you can directly play with the LLaVA-NeXT based on Mistral-7B through the demo [here](https://t.co/JTDlqMUwEh) π€
""")
st.markdown(""" """)
st.video("pages/LLaVA-NeXT/video_1.mp4", format="video/mp4")
st.markdown(""" """)
st.info("""
Ressources:
[LLaVA-NeXT: Improved reasoning, OCR, and world knowledge](https://llava-vl.github.io/blog/2024-01-30-llava-next/)
by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee (2024)
[GitHub](https://github.com/haotian-liu/LLaVA/tree/main)
[Hugging Face documentation](https://huggingface.co/docs/transformers/model_doc/llava_next)""", icon="π")
st.markdown(""" """)
st.markdown(""" """)
st.markdown(""" """)
col1, col2, col3 = st.columns(3)
with col1:
if st.button('Previous paper', use_container_width=True):
switch_page("Depth Anything")
with col2:
if st.button('Home', use_container_width=True):
switch_page("Home")
with col3:
if st.button('Next paper', use_container_width=True):
switch_page("Painter") |