How to Use

Fine-tune using SLM with classification loss

accelerate launch \
    --config_file configs/accelerate_config_8gpu.yaml \
    main.py \
    --task_type classfication \
    --dataset javascript \
    --model codebert-base \
    --max_seq_length 512

Zero-shot using LLM

accelerate launch \
    --config_file configs/accelerate_config_8gpu.yaml \
    main.py \
    --task_type generation \
    --dataset javascript \
    --model codeqwen1.5-7b-chat \
    --prompt_version 1 \
    --eval_only

Few-shot using LLM with random examples

accelerate launch \
    --config_file configs/accelerate_config_8gpu.yaml \
    main.py \
    --task_type generation \
    --dataset javascript \
    --model codeqwen1.5-7b-chat \
    --prompt_version 1 \
    --eval_only \
    --few_shot_k 3 \
    --max_seq_length 4096 \ 
    --per_device_eval_batch_size 4

Few-shot using LLM with similar examples based on embedding model

accelerate launch \
    --config_file configs/accelerate_config_8gpu.yaml \
    main.py \
    --task_type generation \
    --dataset javascript \
    --model codeqwen1.5-7b-chat \
    --prompt_version 1 \
    --eval_only \
    --few_shot_k 3 \
    --embedding_model simcse-bert-base

Fine-tune (QLoRA) using LLM with causal LM loss

accelerate launch \
    --config_file configs/accelerate_config_8gpu.yaml \
    main.py \
    --task_type generation \
    --dataset javascript \
    --model codeqwen1.5-7b \
    --prompt_version 1

Fine-tune (QLoRA) using LLM with classification loss

accelerate launch \
    --config_file configs/accelerate_config_8gpu.yaml \
    main.py \
    --task_type classfication \
    --dataset javascript \
    --model codeqwen1.5-7b