redhatai 🚀

🛠️ Installation:

1. Clone the repository:

git clone [<URL-of-your-repo>](https://github.com/avijra/redhatai.git)

3. In the root folder, create a new directory:

mkdir SOURCE_DOCUMENTS

🔧 Local Setup:

1. Install required libraries:

pip install -r requirements.txt

3. Start the app:

streamlit run redhat_ai.py

🐳 Docker Setup:

1. Build the Docker image:

docker build . -t name_of_image:version

3. Running with GPUs:

• Ensure you have NVIDIA Docker utilities:

  yum install nvidia-container-toolkit
  

• Start the container:

  docker run -p 8501:8501 -it --mount src="$HOME/.cache",target=/root/.cache,type=bind --gpus=all name_of_image:version
  

3. If not using GPUs:

   docker run -p 8501:8501 -it --mount src="$HOME/.cache",target=/root/.cache name_of_image:version
   

4. Alternatively, pull images directly:

   docker pull avijra/redhatai_vicuna-13b-gptq:1.0
   docker pull avijra/redhatai_vicuna-7b-gptq:1.0
   

📝 Things to Note:

1. Image building and downloading takes time due to size (~20GB). Relax and sip your coffee ☕.
2. First app run will be slower due to model download.
3. App response time varies based on GPU VRAM. Estimate: 48 GiB.
4. Code currently supports only 2 CUDA devices. To accommodate more, adjust in run_redhatai.py.

• For example:

  # Current Setup
  model = AutoGPTQForCausalLM.from_quantized(
      ...
      max_memory={ 0: "15GIB", 1: "15GIB" },
      ...
  )
  
  # Adjusted for 3 devices
  model = AutoGPTQForCausalLM.from_quantized(
      ...
      max_memory={ 0: "15GIB", 1: "15GIB", 2: "15GIB" },
      ...
  )