HeorhiiS / llmrecsys

Experiments with LLMs for recommendation systems

Turning LLMs into recommendation systems 🐝

What this repository has:

• Experiments with LLMs for recommendation systems for MLOSS on movielens dataset
• Finetuning scripts for LLMs
• SLURM batch files for running finetuning on a cluster
• LoRA weights to run this yourself

How do I run this myself?

1. Install the requirements

Recommended: create a virtual environment (I prefer conda)

conda create -n llmrec python=3.9
conda activate llmrec
pip install -r requirements.txt

2. Download the official LLAMA weights from Meta

Paste the presign URL into the bash script slurm/download.sh and run it. This will download the weights and tokenizer to `llamadownloads/ or your preferred weight directory.

For GPT-J the weights will be loaded and cached automatically.

3. Apply converstion script to convert the weights to the right format. SLURM version in slurm/prepdata.sh

python ../convert_llama_weights_to_hf.py \
    --input_dir ../weights_dir/ --model_size 65B --output_dir ../converted_checkpoints/65B

4. Run the finetuning script

python lora_llm/customllamatrain.py

5. Run the evaluation script

python lora_llm/customllamaeval.py

For GPT-J, the procedure is the same, but you can skip step 2 and 3. Scripts use wandb for logging, so you need to modifty the scripts if you don't want to use it or provide your own API key and apply wandb.login().

You can also play around with prompt and the dataset in the notebook makemydata.ipynb. You will need to download the dataset from here. The notebook will prepare the data for you for LLAMA. For GPT-J the script will combine instruction and input fields into one field and add endoftext tokens.