Alpaca构建方式探秘：低成本构造指令数据增强LLM

{
    "id":"seed_task_0",
    "name":"breakfast_suggestion",
    "instruction":"Is there anything I can eat for a breakfast that doesn't include eggs, yet includes protein, and has roughly 700-1000 calories?",
    "instances":[
        {
            "input":"",
            "output":"Yes, you can have 1 oatmeal banana protein shake and 4 strips of bacon. The oatmeal banana protein shake may contain 1/2 cup oatmeal, 60 grams whey protein powder, 1/2 medium banana, 1tbsp flaxseed oil and 1/2 cup watter, totalling about 550 calories. The 4 strips of bacon contains about 200 calories."
        }
    ],
    "is_classification":false
}
{
    "id":"seed_task_1",
    "name":"antonym_relation",
    "instruction":"What is the relation between the given pairs?",
    "instances":[
        {
            "input":"Night : Day :: Right : Left",
            "output":"The relation between the given pairs is that they are opposites."
        }
    ],
    "is_classification":false
}
{
    "id":"seed_task_2",
    "name":"one_sentence_description",
    "instruction":"Generate a one-sentence description for each of the following people.",
    "instances":[
        {
            "input":"- Brack Obama\n- Elon Musk\n- Taylor Swift",
            "output":"- Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017.\n- Elon Musk is the founder, CEO, and chief engineer of SpaceX; angel investor, CEO and product architect of Tesla, Inc.; founder of The Boring Company; co-founder of Neuralink and OpenAI; president of the Musk Foundation; and owner and CEO of Twitter, Inc.\n- Taylor Alison Swift is an American singer-songwriter."
        }
    ]
    "is_classification":false
},
{
    "id":"seed_task_3",
    "name":"harmful_stereotype_example",
    "instruction":"Describe a situation in which the given stereotype can harm you.",
    "instances":[
        {
            "input":"\"All Asians are smart!\"",
            "output":"Imagine you're a small child of Asian descent. You hear your whole life how \"all Asians are smart,\" but you know that does not feel like it's true for you. You have a hard time in school. Reading is difficult. Math is impossible. Everyone around you seems to pressure you because you are supposed to be \"smart.\" Every time you do not bring home that \"A\" assignment, you feel like a failure. Like you alone missed out on the gene that makes \"all Asians smart.\"\nAs educational opportunities like scholarships and colleges close their door on you, your future looks bleak. You have spent years trying to be the stereotypically smart Asian instead of finding the talents that could have taken you to success in other areas. As you can see, although the stereotype doesn't sound like a bad thing at first, it can put a lot of weight on the people who do not fit into it."
        }
    ]
    "is_classification":false
},
{
    "id":"seed_task_4",
    "name":"email_subject_generation",
    "instruction":"Generate an appropriate subjective title for the following email:",
    "instances":[
        {
            "input":"Hi [person name],\n\nI'm writing to ask you if you are happy to be a panelist of our workshop on multimodality at CVPR. The workshop will be held on June 20, 2023. \n\nBest,\n[my name]",
            "output":"Invitition to be a panelist for CVPR 2023 workshop on Multimodality"
        }
    ],
    "is_classification":false
}

You are asked to come up with a set of 20 diverse task instructions. These task instructions will be given to a GPT model and we will evaluate the GPT model for completing the instructions.

Here are the requirements:
1. Try not to repeat the verb for each instruction to maximize diversity.
2. The language used for the instruction also should be diverse. For example, you should combine questions with imperative instrucitons.
3. The type of instructions should be diverse. The list should include diverse types of tasks like open-ended generation, classification, editing, etc.
4. A GPT language model should be able to complete the instruction. For example, do not ask the assistant to create any visual or audio output. For another example, do not ask the assistant to wake you up at 5pm or set a reminder because it cannot perform any action.
5. The instructions should be in English.
6. The instructions should be 1 to 2 sentences long. Either an imperative sentence or a question is permitted.
7. You should generate an appropriate input to the instruction. The input field should contain a specific example provided for the instruction. It should involve realistic data and should not contain simple placeholders. The input should provide substantial content to make the instruction challenging but should ideally not exceed 100 words.
8. Not all instructions require input. For example, when a instruction asks about some general information, "what is the highest peak in the world", it is not necssary to provide a specific context. In this case, we simply put "<noinput>" in the input field.
9. The output should be an appropriate response to the instruction and the input. Make sure the output is less than 100 words.

List of 20 tasks:

torchrun --nproc_per_node=4 --master_port=<your_random_port> train.py \
    --model_name_or_path <your_path_to_hf_converted_llama_ckpt_and_tokenizer> \
    --data_path ./alpaca_data.json \
    --bf16 True \
    --output_dir <your_output_dir> \
    --num_train_epochs 3 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 8 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 2000 \
    --save_total_limit 1 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --fsdp "full_shard auto_wrap" \
    --fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
    --tf32 True

torchrun --nproc_per_node=4 --master_port=<your_random_port> train.py \
    --model_name_or_path "facebook/opt-6.7b" \
    --data_path ./alpaca_data.json \
    --bf16 True \
    --output_dir <your_output_dir> \
    --num_train_epochs 3 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 8 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 2000 \
    --save_total_limit 1 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --fsdp "full_shard auto_wrap" \
    --fsdp_transformer_layer_cls_to_wrap 'OPTDecoderLayer' \
    --tf32 True