---
language:
- ko
pipeline_tag: text-generation
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

The model is a fined tuned version of a Korean Large Language model [KT-AI/midm-bitext-S-7B-inst-v1](https://huggingface.co/KT-AI/midm-bitext-S-7B-inst-v1).

The purpose of the model is to analyze any "food order sentence" and extract information of product from the sentence. 

For example, let's assume an ordering sentence:
```
여기요 춘천닭갈비 4인분하고요. 라면사리 추가하겠습니다. 콜라 300ml 두캔주세요.
```
Then the model is expected to generate product informations like:
```
- 분석 결과 0: 음식명:춘천닭갈비, 수량:4인분
- 분석 결과 1: 음식명:라면사리
- 분석 결과 2: 음식명:콜라, 옵션:300ml, 수량:두캔
```

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [Jangmin Oh](https://huggingface.co/jangmin)
- **Model type:** a Decoder-only Transformer
- **Language(s) (NLP):** ko
- **License:** You should keep the CC-BY-NC 4.0 form KT-AI.
- **Finetuned from model:** [KT-AI/midm-bitext-S-7B-inst-v1](https://huggingface.co/KT-AI/midm-bitext-S-7B-inst-v1)



## Bias, Risks, and Limitations

The current model was developed using the GPT-4 API to generate a dataset for order sentences, and it has been fine-tuned on this dataset. Please note that we do not assume any responsibility for risks or damages caused by this model.

## How to Get Started with the Model

This is a simple example of usage of the model.
If you want to load the fined-tuned model in INT4, please specify @load_in_4bit=True@ instead of @load_in_8bit=True@.

``` python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

model_id = 'jangmin/merged-midm-7B-food-order-understanding-30K'

prompt_template = """###System;{System}
###User;{User}
###Midm;"""

default_system_msg = (
    "너는 먼저 사용자가 입력한 주문 문장을 분석하는 에이전트이다. 이로부터 주문을 구성하는 음식명, 옵션명, 수량을 차례대로 추출해야 한다."
)

def wrapper_generate(model, tokenizer, input_prompt, do_stream=False):
    data = tokenizer(input_prompt, return_tensors="pt")
    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    input_ids = data.input_ids[..., :-1]
    with torch.no_grad():
        pred = model.generate(
            input_ids=input_ids.cuda(),
            streamer=streamer if do_stream else None,
            use_cache=True,
            max_new_tokens=float('inf'),
            do_sample=False
        )
    decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)
    decoded_text = decoded_text[0].replace("<[!newline]>", "\n")
    return (decoded_text[len(input_prompt):])

trained_model = AutoModelForCausalLM.from_pretrained(
    model_id,
    load_in_8bit=True,
    device_map="auto",,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)

sentence = "아이스아메리카노 톨사이즈 한잔 하고요. 딸기스무디 한잔 주세요. 또, 콜드브루라떼 하나요."
analysis = wrapper_generate(
    model=trained_model,
    tokenizer=tokenizer,
    input_prompt=prompt_template.format(System=default_system_msg, User=sentence),
    do_stream=False
)
print(analysis)
```

## Training Details

### Training Data

The dataset was generated by GPT-4 API with a carefully designed prompt. 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': '다음은 매장에서 고객이 음식을 주문하는 주문 문장이다. 이를 분석하여 음식명, 옵션명, 수량을 추출하여 고객의 의도를 이해하고자 한다.\n분석 결과를 완성해주기 바란다.\n\n### 명령: 제육볶음 한그릇하고요, 비빔밥 한그릇 추가해주세요. ### 응답:\n',
  'output': '- 분석 결과 0: 음식명:제육볶음,수량:한그릇\n- 분석 결과 1: 음식명:비빔밥,수량:한그릇'
},
{
  'input': '다음은 매장에서 고객이 음식을 주문하는 주문 문장이다. 이를 분석하여 음식명, 옵션명, 수량을 추출하여 고객의 의도를 이해하고자 한다.\n분석 결과를 완성해주기 바란다.\n\n### 명령: 사천탕수육 곱배기 주문하고요, 샤워크림치킨도 하나 추가해주세요. ### 응답:\n',
  'output': '- 분석 결과 0: 음식명:사천탕수육,옵션:곱배기\n- 분석 결과 1: 음식명:샤워크림치킨,수량:하나'
}

```

## Evaluation

"The evaluation dataset comprises 3,004 examples, each consisting of a pair: a 'food-order sentence' and its corresponding 'analysis result' as a reference."

The bleu scores on the dataset are as follows. 

|  | llama-2 model | midm model |
|---|---|---|
| score | 93.323054 | 93.878258 |
| counts | [81382, 76854, 72280, 67869] | [81616, 77246, 72840, 68586] |
| totals | [84327, 81323, 78319, 75315] | [84376, 81372, 78368, 75364] |
| precisions | [96.51, 94.5, 92.29, 90.11] | [96.73, 94.93, 92.95, 91.01] |
| bp | 1.0 | 1.0 |
| sys_len | 84327 | 84376 |
| ref_len | 84124 | 84124 |

llama-2 model referes the result of the [jangmin/merged-llama2-7b-chat-hf-food-order-understanding-30K], which was fine-tuned above llama-2-7b-chat-hf.

## Note for Pretrained Model

The citation of the pretrained model:

```
@misc{kt-mi:dm,
  title         = {Mi:dm: KT Bilingual (Korean,English) Generative Pre-trained Transformer},
  author        = {KT},
  year          = {2023},
  url           = {https://huggingface.co/KT-AT/midm-bitext-S-7B-inst-v1}
  howpublished  = {\url{https://genielabs.ai}},
}
```

## Model Card Authors

Jangmin Oh

## Model Card Contact

Jangmin Oh