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---
license: apache-2.0
library_name: peft
---
# Food Order Understanding in Korean
This is a LoRA adapter as a result of fine-tuning the pre-trained model `meta-llama/Llama-2-13b-chat-hf`. It is designed with the expectation of understanding Korean food ordering sentences, and analyzing food menus, option names, and quantities.
## Usage
Here is an example of loading the model.
Note the pretrained model is `meta-llama/Llama-2-13b-chat-hf`.
```python
peft_model_id = "jangmin/qlora-llama2-13b-chat-hf-food-order-understanding-30K"
config = PeftConfig.from_pretrained(peft_model_id)
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, quantization_config=bnb_config, cache_dir=cache_dir, device_map={"":0})
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, cache_dir=cache_dir)
model.eval()
```
Inferece can be done as follows.
```python
instruction_prompt_template = """
๋ค์์ ๋งค์ฅ์์ ๊ณ ๊ฐ์ด ์์์ ์ฃผ๋ฌธํ๋ ์ฃผ๋ฌธ ๋ฌธ์ฅ์ด๋ค. ์ด๋ฅผ ๋ถ์ํ์ฌ ์์๋ช
, ์ต์
, ์๋์ ์ถ์ถํ์ฌ ๊ณ ๊ฐ์ ์๋๋ฅผ ์ดํดํ๊ณ ์ ํ๋ค.
๋ถ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฑํด์ฃผ๊ธฐ ๋ฐ๋๋ค.
### ์ฃผ๋ฌธ ๋ฌธ์ฅ: {0} ### ๋ถ์ ๊ฒฐ๊ณผ:
"""
def gen(x):
q = instruction_prompt_template.format(x)
gened = model.generate(
**tokenizer(
q,
return_tensors='pt',
return_token_type_ids=False
).to('cuda'),
max_new_tokens=256,
early_stopping=True,
do_sample=True,
eos_token_id=tokenizer.eos_token_id
)
decoded_results = tokenizer.batch_decode(gened, skip_special_tokens=True)
return decoded_results[0]
```
A generated sample is as follows.
```python
print(gen("์์ด์ค์๋ฉ๋ฆฌ์นด๋
ธ ํจ์ฌ์ด์ฆ ํ์ ํ๊ณ ์. ๋ธ๊ธฐ์ค๋ฌด๋ ํ์ ์ฃผ์ธ์. ๋, ์ฝ๋๋ธ๋ฃจ๋ผ๋ผ ํ๋์."))
```
```
๋ค์์ ๋งค์ฅ์์ ๊ณ ๊ฐ์ด ์์์ ์ฃผ๋ฌธํ๋ ์ฃผ๋ฌธ ๋ฌธ์ฅ์ด๋ค. ์ด๋ฅผ ๋ถ์ํ์ฌ ์์๋ช
, ์ต์
๋ช
, ์๋์ ์ถ์ถํ์ฌ ๊ณ ๊ฐ์ ์๋๋ฅผ ์ดํดํ๊ณ ์ ํ๋ค.
๋ถ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฑํด์ฃผ๊ธฐ ๋ฐ๋๋ค.
### ๋ช
๋ น: ์์ด์ค์๋ฉ๋ฆฌ์นด๋
ธ ํจ์ฌ์ด์ฆ ํ์ ํ๊ณ ์. ๋ธ๊ธฐ์ค๋ฌด๋ ํ์ ์ฃผ์ธ์. ๋, ์ฝ๋๋ธ๋ฃจ๋ผ๋ผ ํ๋์. ### ์๋ต:
- ๋ถ์ ๊ฒฐ๊ณผ 0: ์์๋ช
:์์ด์ค์๋ฉ๋ฆฌ์นด๋
ธ, ์ต์
:ํจ์ฌ์ด์ฆ, ์๋:ํ์
- ๋ถ์ ๊ฒฐ๊ณผ 1: ์์๋ช
:๋ธ๊ธฐ์ค๋ฌด๋, ์๋:ํ์
- ๋ถ์ ๊ฒฐ๊ณผ 2: ์์๋ช
:์ฝ๋๋ธ๋ฃจ๋ผ๋ผ, ์๋:ํ๋
``````
## Training
Fine-tuning was conducted using https://github.com/artidoro/qlora on an RTX-4090 machine, and took approximately 9 hours.
The max_steps parameter was set to 5,000, which allowed nearly two complete scans of the entire dataset.
Below is my training script.
```bash
python qlora.py \
--cache_dir /Jupyter/huggingface/.cache \
--model_name_or_path meta-llama/Llama-2-13b-chat-hf \
--use_auth \
--output_dir ../output/llama2-gpt4-30k-food-order-understanding-13b \
--logging_steps 10 \
--save_strategy steps \
--data_seed 42 \
--save_steps 500 \
--save_total_limit 40 \
--evaluation_strategy steps \
--eval_dataset_size 1024 \
--max_eval_samples 1000 \
--per_device_eval_batch_size 12 \
--max_new_tokens 32 \
--dataloader_num_workers 1 \
--group_by_length \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--do_eval \
--lora_r 64 \
--lora_alpha 16 \
--lora_modules all \
--double_quant \
--quant_type nf4 \
--bf16 \
--bits 4 \
--warmup_ratio 0.03 \
--lr_scheduler_type constant \
--gradient_checkpointing \
--dataset /Jupyter/dev_src/ASR-for-noisy-edge-devices/data/food-order-understanding-gpt4-30k.json \
--target_max_len 512 \
--per_device_train_batch_size 12 \
--gradient_accumulation_steps 1 \
--max_steps 5000 \
--eval_steps 500 \
--learning_rate 0.0002 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--lora_dropout 0.1 \
--weight_decay 0.0 \
--seed 0 \
--report_to tensorboard
```
## Dataset
The dataset was constructed using GPT-API with `gpt-4`. A prompt template is desginged to generate examples of sentence pairs of a food order and its understanding. Total 30k examples were generated.
Note that it cost about $400 to generate 30K examples through 3,000 API calls.
Some generated examples are as follows:
```json
{
'input': '๋ค์์ ๋งค์ฅ์์ ๊ณ ๊ฐ์ด ์์์ ์ฃผ๋ฌธํ๋ ์ฃผ๋ฌธ ๋ฌธ์ฅ์ด๋ค. ์ด๋ฅผ ๋ถ์ํ์ฌ ์์๋ช
, ์ต์
๋ช
, ์๋์ ์ถ์ถํ์ฌ ๊ณ ๊ฐ์ ์๋๋ฅผ ์ดํดํ๊ณ ์ ํ๋ค.\n๋ถ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฑํด์ฃผ๊ธฐ ๋ฐ๋๋ค.\n\n### ๋ช
๋ น: ์ ์ก๋ณถ์ ํ๊ทธ๋ฆํ๊ณ ์, ๋น๋น๋ฐฅ ํ๊ทธ๋ฆ ์ถ๊ฐํด์ฃผ์ธ์. ### ์๋ต:\n',
'output': '- ๋ถ์ ๊ฒฐ๊ณผ 0: ์์๋ช
:์ ์ก๋ณถ์,์๋:ํ๊ทธ๋ฆ\n- ๋ถ์ ๊ฒฐ๊ณผ 1: ์์๋ช
:๋น๋น๋ฐฅ,์๋:ํ๊ทธ๋ฆ'
},
{
'input': '๋ค์์ ๋งค์ฅ์์ ๊ณ ๊ฐ์ด ์์์ ์ฃผ๋ฌธํ๋ ์ฃผ๋ฌธ ๋ฌธ์ฅ์ด๋ค. ์ด๋ฅผ ๋ถ์ํ์ฌ ์์๋ช
, ์ต์
๋ช
, ์๋์ ์ถ์ถํ์ฌ ๊ณ ๊ฐ์ ์๋๋ฅผ ์ดํดํ๊ณ ์ ํ๋ค.\n๋ถ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฑํด์ฃผ๊ธฐ ๋ฐ๋๋ค.\n\n### ๋ช
๋ น: ์ฌ์ฒํ์์ก ๊ณฑ๋ฐฐ๊ธฐ ์ฃผ๋ฌธํ๊ณ ์, ์ค์ํฌ๋ฆผ์นํจ๋ ํ๋ ์ถ๊ฐํด์ฃผ์ธ์. ### ์๋ต:\n',
'output': '- ๋ถ์ ๊ฒฐ๊ณผ 0: ์์๋ช
:์ฌ์ฒํ์์ก,์ต์
:๊ณฑ๋ฐฐ๊ธฐ\n- ๋ถ์ ๊ฒฐ๊ณผ 1: ์์๋ช
:์ค์ํฌ๋ฆผ์นํจ,์๋:ํ๋'
}
```
## Note
I have another fine-tuned Language Model, `jangmin/qlora-polyglot-ko-12.8b-food-order-understanding-32K`, which is based on `EleutherAI/polyglot-ko-12.8b`. The dataset was generated using `gpt-3.5-turbo-16k`. I believe that the quality of a dataset generated by `GPT-4` would be superior to that generated by `GPT-3.5`. |