# Fine-tuning Optimus on a VAE language modeling task

_Note: The latent vector size has a great impact on the model performance: small latent size provides a tight information bottleneck, often yielding low reconstruction quality; In contrast, large latent size shows good reconstruction quality, but would be hard to control the latent manipulation with vector operators if the latent size is too large. To have a fair comparison with existing works, we use a 32-dimensional latent vector for the experiments on language modeling._ 

##### Download a pre-trained model (pre-trained from Wikipedia). 


| Beta    | Latent size | Checkpoint |
| -------- | ------- | ------- |
| 0.0  | 32 | [Checkpoint](https://chunylcus.blob.core.windows.net/machines/msrdl/optimus/output/pretrain/philly_rr3_vc4_g8_base_vae_wikipedia_pretraining_beta_schedule_beta0.0_d1.0_ro0.5_ra0.25_32_v2/checkpoint-508523.zip?sp=r&st=2023-08-28T00:40:43Z&se=3023-08-28T08:40:43Z&sv=2022-11-02&sr=c&sig=kUkSFqeHFfTeqxxpvqVdICCJupwODFwJprCAW2o4irE%3D)    |
| 0.5  | 32 | [Checkpoint](https://chunylcus.blob.core.windows.net/machines/msrdl/optimus/output/pretrain/philly_rr3_vc4_g8_base_vae_wikipedia_pretraining_beta_schedule_beta0.5_d1.0_ro0.5_ra0.25_32_v2/checkpoint-508523.zip?sp=r&st=2023-08-28T00:40:43Z&se=3023-08-28T08:40:43Z&sv=2022-11-02&sr=c&sig=kUkSFqeHFfTeqxxpvqVdICCJupwODFwJprCAW2o4irE%3D)      |
| 0.0  | 768 | [Checkpoint](https://chunylcus.blob.core.windows.net/machines/msrdl/optimus/output/pretrain/philly_rr3_vc4_g8_base_vae_wikipedia_pretraining_beta_schedule_beta0.0_d1.0_ro0.5_ra0.25_768_v2/checkpoint-508523.zip?sp=r&st=2023-08-28T00:40:43Z&se=3023-08-28T08:40:43Z&sv=2022-11-02&sr=c&sig=kUkSFqeHFfTeqxxpvqVdICCJupwODFwJprCAW2o4irE%3D)     |
| 0.5  | 768 | [Checkpoint](https://chunylcus.blob.core.windows.net/machines/msrdl/optimus/output/pretrain/philly_rr3_vc4_g8_base_vae_wikipedia_pretraining_beta_schedule_beta0.5_d1.0_ro0.5_ra0.25_768_v2/checkpoint-508523.zip?sp=r&st=2023-08-28T00:40:43Z&se=3023-08-28T08:40:43Z&sv=2022-11-02&sr=c&sig=kUkSFqeHFfTeqxxpvqVdICCJupwODFwJprCAW2o4irE%3D)  |
| 1.0  | 768 | [Checkpoint](https://chunylcus.blob.core.windows.net/machines/msrdl/optimus/output/pretrain/philly_rr3_vc4_g8_base_vae_wikipedia_pretraining_beta_schedule_beta1.0_d1.0_ro0.5_ra0.25_768_v2/checkpoint-508523.zip?sp=r&st=2023-08-28T00:40:43Z&se=3023-08-28T08:40:43Z&sv=2022-11-02&sr=c&sig=kUkSFqeHFfTeqxxpvqVdICCJupwODFwJprCAW2o4irE%3D)  |



```
export PYTHONPATH="${PYTHONPATH}:/workspace/code"
export GPU_ID=0,1

export TRAIN_FILE=../data/datasets/snli_data/train.txt
export TEST_FILE=../data/datasets/snli_data/test.txt

CUDA_VISIBLE_DEVICES=$GPU_ID python examples/big_ae/run_lm_vae_training.py \
    --output_dir=../output/LM/Snli/local_lm_vae_snli_optimus \
    --dataset Snli \
    --encoder_model_type=bert \
    --encoder_model_name_or_path=bert-base-cased \
    --decoder_model_type=gpt2 \
    --decoder_model_name_or_path=gpt2 \
    --beta 1.0 \
    --ratio_zero 0.5 \
    --ratio_increase 0.25 \
    --do_train \
    --do_eval \
    --fb_mode 1 \
    --dim_target_kl 0.5\
    --train_data_file=$TRAIN_FILE \
    --eval_data_file=$TEST_FILE \
    --num_train_epochs 1.0 \
    --save_steps 1000 \
    --logging_steps 1000 \
    --overwrite_output_dir \
    --per_gpu_train_batch_size=5 \
    --block_size 100 \
    --length_weighted_loss \
    --use_pretrained_model \
    --use_pretrained_vae \
    --checkpoint_dir ../output/pretrain/philly_rr3_vc4_g8_base_vae_wikipedia_pretraining_beta_schedule_beta0.0_d1.0_ro0.5_ra0.25_32_v2/checkpoint-508523 \
    --gloabl_step_eval 508523
```