model card is written.

README.md

@@ -2,4 +2,157 @@
 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.
+
+``` 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=tokenier,
+  input_prompt=prompt_templateformat(System=default_system_msg, User=sentece, do_stream=True)
+)
+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
+
+