`In this task, you will see a description of an uncanny situation. Then, you will see a joke that was written about the situation. Explain how the joke relates to the situation and why it is funny.

{few-shot examples separated by ###, newline after "explanation of the caption:"} This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller. caption: Can I interest you in opening an offshore account? explanation of the caption: `

In training, I used just the individual example:

This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller. caption: Can I interest you in opening an offshore account? explanation of the caption:\n

In inference, I had some better results with a more natural prompt (no newline or space at end)

This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller. caption: Can I interest you in opening an offshore account? the caption is funny because

Training script

Trained on a V100

git clone https://github.com/artidoro/qlora
cd qlora

pip3 install -r requirements.txt --quiet

! cd qlora && python qlora.py \
    --model_name_or_path ../llama-2-7b-hf \
    --output_dir ../thatsthejoke \
    --logging_steps 20 \
    --save_strategy steps \
    --data_seed 42 \
    --save_steps 80 \
    --save_total_limit 10 \
    --evaluation_strategy steps \
    --max_new_tokens 64 \
    --dataloader_num_workers 1 \
    --group_by_length \
    --logging_strategy steps \
    --remove_unused_columns False \
    --do_train \
    --lora_r 64 \
    --lora_alpha 16 \
    --lora_modules all \
    --double_quant \
    --quant_type nf4 \
    --bits 4 \
    --warmup_ratio 0.03 \
    --lr_scheduler_type constant \
    --gradient_checkpointing \
    --dataset /content/nycaptions.jsonl \
    --dataset_format 'self-instruct' \
    --source_max_len 16 \
    --target_max_len 512 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 16 \
    --max_steps 250 \
    --eval_steps 187 \
    --learning_rate 0.0002 \
    --adam_beta2 0.999 \
    --max_grad_norm 0.3 \
    --lora_dropout 0.1 \
    --weight_decay 0.0 \
    --seed 0

monsoon-nlp/nyrkr-joker-llama

nyrkr-joker-llama

Prompt options

Training script

monsoon-nlp/
nyrkr-joker-llama