Some usecase examples with local LLMs

Open the different notebooks and follow along. The intended order is:

1. huggingface.ipynb: How to use a LLM from the HuggingFace model hub.
2. prompt_engineering.ipynb: How to use prompt engineering to improve the quality of the LLM responses.
3. lora.ipynb: Finetuning a model using QLoRA for a specific task.
4. rag.ipynb: How to talk with a PDF using Retrieval Augmented Generation (RAG).

The directory llm_server contains a simple API that can be used to serve a LLM in a remote machine.

Usage

1. Go to a machine with a GPU (at least a 3090) and install the environment:

ssh remote_server
git clone https://github.com/RaulPPelaez/llm_handson
cd llm_handson
mamba create -n llm python==3.11 pip
mamba activate llm
pip install -r requirements.txt

2. Start a Jupyter Notebook server in a machine where you have a GPU:

Lets give it access to just one GPU.

CUDA_VISIBLE_DEVICES=0 jupyter notebook --no-browser --port 1234

3. Open the notebook in your laptop using a SSH tunnel:

	ssh -4 -NL 1234:localhost:1234 remote_server &
	firefox http://localhost:1234