# Clone repositry
git clone https://huggingface.co/IntelLabs/LlavaOLMoBitnet1B

# Move to folder & intall requirements
cd LlavaOLMoBitnet1B
pip install -r requirements.txt

# Run inference
python llava_olmo.py

text = "Be concise. Describe what is in the image"

url = "https://URL_TO_IMAGE.jpg"

# NOTE! Install the `transformers` & `torch` libraries first
from transformers import OlmoeForCausalLM, AutoTokenizer
import torch

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Load different ckpts via passing e.g. `revision=step10000-tokens41B`
# also check allenai/OLMoE-1B-7B-0924-SFT & allenai/OLMoE-1B-7B-0924-Instruct
model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924").to(DEVICE)
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
inputs = tokenizer("Bitcoin is", return_tensors="pt")
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
out = model.generate(**inputs, max_length=64)
print(tokenizer.decode(out[0]))
# > # Bitcoin is a digital currency that is created and held electronically. 
#No one controls it. Bitcoins aren’t printed, like dollars or euros – they’re produced by people and businesses running computers all around the world, using software that solves mathematical

# NOTE! Install the `transformers` & `torch` libraries first
from transformers import OlmoeForCausalLM, AutoTokenizer
import torch

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Load different ckpts via passing e.g. `revision=step10000-tokens41B`
# also check allenai/OLMoE-1B-7B-0924-SFT & allenai/OLMoE-1B-7B-0924-Instruct
model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924").to(DEVICE)
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
inputs = tokenizer("Bitcoin is", return_tensors="pt")
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
out = model.generate(**inputs, max_length=64)
print(tokenizer.decode(out[0]))
# > # Bitcoin is a digital currency that is created and held electronically. 
#No one controls it. Bitcoins aren’t printed, like dollars or euros – they’re produced by people and businesses running computers all around the world, using software that solves mathematical

# Clone repo
git clone https://github.com/Vchitect/VEnhancer.git
cd VEnhancer

# Create environment
conda create -n venhancer python=3.10
conda activate venhancer

# Install requirments:
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
pip install -r requirements.txt

sudo apt-get update && apt-get install ffmpeg libsm6 libxext6  -y

bash run_VEnhancer.sh

python gradio_app.py

# Graph Reasoning installation 
pip install git+https://github.com/lamm-mit/GraphReasoning

# wkhtmltopdf installation
sudo apt-get install wkhtmltopdf

# Graph file
from huggingface_hub import hf_hub_download   
graph_name='large_graph_simple_giant.graphml'
filename = f"{graph_name}"
file_path = hf_hub_download(repo_id='lamm-mit/bio-graph-1K', filename=filename,  local_dir='./graph_giant_component')

# Embeddings
from huggingface_hub import hf_hub_download
embedding_name='embeddings_simple_giant_ge-large-en-v1.5.pkl'
filename = f"{embedding_name}"
file_path = hf_hub_download(repo_id='lamm-mit/bio-graph-1K', filename=filename,  local_dir='./graph_giant_component')

# Clone SciAgents 
git clone https://github.com/lamm-mit/SciAgentsDiscovery.git

# Insatll reqs
cd SciAgentsDiscovery
pip install -e .