A Medical AI Chat

What this repo is about:

• Install and use a medical GPT chat with custom model files and model downloads from Hugging Face.
• Run a Mistral programming code generation GPT chat from Ollama's library provided by default.

Prerequisites

• Have a fast, modern PC (best with NVIDIA CUDA support and >= 12 GB graphics RAM), have patience, or use an M1 Mac or an AI web hosting with available GPU (e.g., Azure).
• Install Ollama: https://ollama.com - install for your system.
• Choose the models for your domain: https://ollama.com/library.
• Note: Mistral is no longer available in German by default.
  • Run ollama run mistral to download the model and run it. Example prompt: Schreibe mir einen Python Flask Server, der ein Hello World zurück gibt.
  • For German, use the custom model from: https://huggingface.co/TheBloke/em_german_leo_mistral-GGUF.

Based on this YouTube clip:

• https://www.youtube.com/watch?v=bANziaFj_sA

Chainlit Documentation

• https://docs.chainlit.io/get-started/overview

Official Mistral Website with AI GPT comparison

• https://mistral.ai/news/mistral-large

Example Ollama run Mistral with prompt:

Useful tips and commands with Ollama:

• Bookmark the Ollama terminal in MacOS Dock / Windows taskbar.
• Check in a terminal: ollama -v.
• ollama list lists the installed models.
• /? for help.
• ollama rm <Modelname> deletes an installed model.
• Exit command line chat by typing: /bye.

Additional browse for other medical GGUF M1 Mac models:

• For MacOS M1 SOC GPU (no CUDA for PC & Linux): https://huggingface.co/search/full-text?q=health+gguf&type=model.

Test command line chat:

• Download and install via model file first, then run: ollama run biomistral1 (7B - German). Example prompt: Was ist der Haupt-Wirkstoff in Aspirin?.

Use as a Chainlit chat:

• To use Ollama as a Python Chainlit chat, follow these steps. Result can be seen in the following screenshot:

Installation of Python Conda environment (optional):

• Install Miniconda first: https://docs.conda.io/en/latest/miniconda.html.
• Then run:

  conda create --name torch python=3.9
  conda activate torch
  pip install -r ./langchain-gemma-ollama-chainlit/requirements.txt
  python -m ipykernel install --user --name torch --display-name "Python 3.9 (torch)"
  pip install transformers==4.20.0
  pip install googletrans==4.0.0-rc1 (optional)
  conda install torch -c pytorch-nightly

GPU Acceleration M1 MacOS - use mps (optional)

• if using conda: conda install pytorch torchvision torchaudio -c pytorch-nightly
• or with pip: pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu

Detailed explanation, create a pytorch conda environment (optional)

• Create new env from Jeff Heaton Youtube / Github pages

• MacOS M1 https://www.youtube.com/watch?v=VEDy-c5Sk8Y&t=380s

• other: https://www.youtube.com/results?search_query=jeff+heaton+pytorch+installation

• activate env by typing in console:

• conda activate torch

Load custom Model - ggml-model-Q8_0.gguf - 7B Model - in german (optional)

• In case a model is not listed on https://ollama.com/library:
• First install the huggingface-cli:
• python -m pip install -U "huggingface_hub[cli]"
• pip3 install huggingface-hub
• Load the model file to local cache (danger, have at least 6 GB of free space):
• huggingface-cli download BioMistral/BioMistral-7B-GGUF ggml-model-Q8_0.gguf --local-dir . --local-dir-use-symlinks False
• ollama create biomistral1 -f Modelfile
• ollama run biomistral1

Install a model thats not listed in the ollama directory (/library)

• cd langchain-gemma-ollama-chainlit change directory
• replace in langchain-gemma-ollama-chainlit-de.py: model = Ollama(model="gemma:2b") - instead of gemma:2b with your model name e.g. mistral or custom installation biomistral1.
• edit Modefile with correct template
• ollama create biomistral1 -f Modelfile

On manifest missing error (optional)

• Create the ollama file from custom model:
• (you can initialize, in case of manifest missing error do a ollama run mistral first)

Run the chat in a browser:

• cd langchain-gemma-ollama-chainlit change directory
• chainlit run langchain-gemma-ollama-chainlit-de.py -> medical
• chainlit run langchain-mistral-ollama-chainlit.py -> code

Delete Models in Huggingface cache if not needed or HD is full

• show llms: cd ~/.cache/huggingface/hub
• ls -lia
• python -m pip install -U "huggingface_hub[cli]"
• start removing llms: huggingface-cli delete-cache
• select via Space - don't select no action then enter und choose Y(es)

Example prompts (english)

• What are the symptoms of the common cold?
• What causes the seasonal flu?
• What medication would be prescribed for a headache?

Example prompts (german) - with Biomistral trained and finetuned on medical domain:

• Was sind die Symptome einer Erkältung?
• Was verursacht die saisonale Grippe?
• Welche Medikamente würden gegen Kopfschmerzen verschrieben?

Mistral Model (Programminglanguages better use ollama run codestral, very slow): ollama run mistral

• Wie importiert man ein Package in Java?
• Wie definiert man einen Type mit einem String und einem boolean in Typescript?
• Schreibe mir einen Python Flask Server, der ein Hello World zurück gibt

Finetune gguf models (very optional, for experts only)

• https://github.com/ggerganov/llama.cpp/tree/master/examples/finetune
• https://rentry.org/cpu-lora

Screenshot of demo of another prompt: