Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+library_name: peft
+base_model: google/gemma-1.1-7b-it
+language:
+- ko
+- en
+tags:
+- translation
+- gemma
 ---
 
 # Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
 ## 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. -->
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-- **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. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[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]
+- **Developed by:** [Kang Seok Ju]
+- **Contact:** [brildev7@gmail.com]
 
 ## 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. -->
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-[More Information Needed]
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-### Training Procedure 
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### 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 -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
-
-
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-[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
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
-
-
+https://huggingface.co/datasets/traintogpb/aihub-koen-translation-integrated-tiny-100k
+
+# Inference Examples
+```
+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+
+model_id = "google/gemma-1.1-7b-it"
+peft_model_id = "brildev7/gemma-1.1-7b-it-translation-ko-sft-qlora"
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    quantization_config=quantization_config, 
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+    attn_implementation="flash_attention_2",
+)
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
+tokenizer.pad_token_id = tokenizer.eos_token_id
+
+# example
+prompt_template = """Translate the following into English:
+{}
+
+output:
+"""
+passage = "달이 해를 완전히 가리는 '개기일식'이 북미 대륙에서 7년 만에 관측되면서 전 세계 수억명의 관심이 집중됐다. 멕시코에서 시작해 캐나다까지 북미를 가로지르며 나타난 '우주쇼'를 보기 위해 사람들은 하던 일을 멈추고 하늘을 올려다봤다. 개기일식으로 창출된 경제효과도 수조원에 이른다는 분석이 나온다."
+prompt = prompt_template.format(passage)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs,
+                        max_new_tokens=1024,
+                        temperature=0.2,
+                        top_p=0.95,
+                        do_sample=True,
+                        use_cache=False)
+print(tokenizer.decode(outputs[0]))
+- 7 years after the last solar eclipse, when the moon completely covered the sun was observed in North America, tens of millions of people around the world focused their attention. People stopped what they were doing and looked up to watch the 'cosmic show' that appeared across North America, from Mexico to Canada. An analysis showed that the economic effect created by the lunar eclipse was also in the hundreds of billions of won.
+
+# example
+prompt_template = """Translate the following into English:
+{}
+
+output:
+"""
+passage = "이틀째 황사 현상이 이어지며 시야가 흐린 하루였습니다. 오늘도 서울 도심은 황사에 갇혀 종일 뿌옇고 누런빛까지 띠었습니다. 내일도 대기 중에 황사가 남아 미세먼지 농도가 높게 나타나겠습니다."
+prompt = prompt_template.format(passage)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs,
+                        max_new_tokens=1024,
+                        temperature=1,
+                        top_p=0.95,
+                        do_sample=True,
+                        use_cache=False)
+print(tokenizer.decode(outputs[0]))
+- On the second day of the yellow dust, the day was misty with the continuous phenomenon. On this day, downtown Seoul was covered with yellow dust and covered with yellow dust throughout the day. Yellow dust remained from tomorrow, so the fine dust concentration would be high.
+```