The llm-detect-ai from starrylun

This repo contains our code and configurations for the LLM - Detect AI Generated Text competition. The summary of the solution is posted here. Please refer to the following sections for details on training and dependencies.

Section 1: Setup

1.1 Hardware

Jarvislabs.ai was our primary source of compute. Specifically, models were trained on the following instance:

Ubuntu 20.04.5 LTS (128 GB boot disk) Intel(R) Xeon(R) Silver 4216 CPU @ 2.10GHz (7 vCPUs) 4 x NVIDIA A100 40GB GPU OR 4 x NVIDIA A6000 48GB GPU

1.2 Software

I used PyTorch-2.1 image from Jarvislabs.ai, which comes with:

Python 3.10.11
CUDA 12.3

1.3 Dependencies

Please clone the repository and install the required packages using the following commands:

git clone https://github.com/rbiswasfc/llm-detect-ai.git
cd llm-detect-ai
pip install -r requirements.txt

1.4 Datasets

Please make sure Kaggle API is installed. Then run the following script to download the required datasets:

chmod +x ./setup.sh
./setup.sh

Please note that the above script will create datasets and models folder in the directory located one level above the current directory. The external datasets will be downloaded in the datasets folder. Instruction-tuned LLMs, which can be used to generate adversarial essays, will be downloaded in the models folder. Total size of downloaded data and model files is ~8GB.

Section 2: Training

Training scripts and configurations are located in the code and conf folders respectively. We leveraged HF accelerate library to execute training runs with DDP on multiple GPUs (4x A100). Specifically, we used the following configurations for training:

compute_environment: LOCAL_MACHINE                                            
debug: false                                                                           
distributed_type: MULTI_GPU
downcast_bf16: 'no'
gpu_ids: all
machine_rank: 0
main_training_function: main
mixed_precision: 'no'
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

2.1 LLM Models

For (Q)LoRA fine-tuning of the LLM models, please run the following commands:

accelerate launch ./code/train_r_detect.py \
--config-name conf_r_detect_mix_v16 \
use_wandb=false

accelerate launch ./code/train_r_detect.py \
--config-name conf_r_detect_mix_v26 \
use_wandb=false

Please note that training takes ~3 hours for mix_v16 and ~4 hours for mix_v26.

2.2 DeBERTa Ranking Models

To training the deberta-v3-large model with ranking loss, please run the following command:

accelerate launch ./code/train_r_ranking.py \
--config-name conf_r_ranking_large \
use_wandb=false

2.3 Embedding model

We trained an embedding model with supervised contrastive loss to find similar essays (KNN neighbors) for a given essay in the test set.

accelerate launch ./code/train_r_embed.py \
--config-name conf_r_embed \
use_wandb=false

Section 3: Text Generation

We fine-tuned a wide variety of LLMs using the CLM objective on PERSUADE corpus to produce student like essays. The fine-tuned checkpoints were uploaded as a Kaggle Dataset conjuring92/detect-ai-persuade-clm-ckpts. These checkpoints can be used to generate essays using the following commands:

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate.yaml

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate_tiny_llama.yaml

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate_pythia.yaml

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate_bloom.yaml

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate_gpt2.yaml

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate_opt.yaml

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate_falcon.yaml

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate_mpt.yaml

accelerate launch ./code/generate_r_clm.py \
--config_path ./conf/r_clm/conf_r_clm_generate_llama13b.yaml

accelerate launch ./code/generate_r_clm_from_scratch.py \
--config_path ./conf/r_clm/conf_r_clm_generate_mistral_persuade.yaml

Optionally, the fine-tuning of LLMs for text generation can be done using the following commands:

accelerate launch ./code/train_r_clm.py \
--config-name conf_r_clm_tiny_llama \
use_wandb=false

accelerate launch ./code/train_r_clm.py \
--config-name conf_r_clm_pythia \
use_wandb=false

accelerate launch ./code/train_r_clm.py \
--config-name conf_r_clm_bloom \
use_wandb=false

accelerate launch ./code/train_r_clm.py \
--config-name conf_r_clm_gpt2 \
use_wandb=false

accelerate launch ./code/train_r_clm.py \
--config-name conf_r_clm_opt \
use_wandb=false

accelerate launch ./code/train_r_clm.py \
--config-name conf_r_clm_falcon \
use_wandb=false

accelerate launch ./code/train_r_clm.py \
--config-name conf_r_clm_mpt \
use_wandb=false

accelerate launch ./code/train_r_clm.py \
--config-name conf_r_clm_llama13b \
use_wandb=false

accelerate launch ./code/train_r_clm_from_scratch.py \
--config-name conf_r_clm_mistral_persuade \
use_wandb=false

starrylun / llm-detect-ai Goto Github PK

llm-detect-ai's Introduction

Section 1: Setup

1.1 Hardware

1.2 Software

1.3 Dependencies

1.4 Datasets

Section 2: Training

2.1 LLM Models

2.2 DeBERTa Ranking Models

2.3 Embedding model

Section 3: Text Generation

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