Update README.md

README.md

@@ -3,200 +3,54 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
-
 <!-- Provide a quick summary of what the model is/does. -->
 
-사용법
+# 🤗 모델 사용 방법
+```python
 peft_config = LoraConfig.from_pretrained("bkk21/triper2_KoAlpaca-6B")
 tokenizer = AutoTokenizer.from_pretrained("bkk21/triper2_KoAlpaca-6B", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("bkk21/triper2_KoAlpaca-6B", config=peft_config, device_map="auto")
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```
+
+# 📑 모델 사용 함수
+```python
+#model 사용함수 정의
+def gen(x):
+  system = """
+너는 서울에 대해 잘 알고 있는 여행 작가야. 
+
+서울에 대해 잘 알고 있어서 사용자가 추천을 해달라고 하면 적절한 추천을 할 수 있어. 단, 서울이 아닌 다른 지역의 장소는 추천하면 안 돼.
+
+서울의 행정구역 별로 알고 있고, 추천할 수 있는 주제는 ["맛집", "카페", "핫플", "숙소", "놀거리"]야. 
+
+예를 들어, 용산동 장소를 추천해달라고 하면, 용산동의 맛집 1개, 카페 1개, 핫플 1개, 숙소 1개, 놀거리 1개를 필수로 추천해줘.
+
+만약 한 주제만 추천해달라고 하면 하나의 주제에 대해 5개 추천해줘. 그리고 각각의 장소는 주소와 영업정보를 꼭 알려줘야 해.
+"""
+
+  gened = model.generate(
+      **tokenizer(
+          f"###instruction: {system}\n\n### input: {x}\n\n### output:",
+          return_tensors='pt',
+          return_token_type_ids=False
+      ).to("cuda"),
+      max_new_tokens=512,
+      early_stopping=True,
+      do_sample=True,
+      eos_token_id=2,
+  )
+  output_text = tokenizer.decode(gened[0])
+  output_only = re.search(r'### output:\s*(.*)', output_text, re.DOTALL)
+
+  print(output_text)
+
+  if output_only:
+      return output_only.group(1).strip()
+```
+
+# 😎 함수 실행 및 결과 확인
+```python
+text = "용산구 한식 맛집을 추천해줘"
+gen(text)
+```
+---