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README.md
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library_name: transformers
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---
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#
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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]
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[More Information Needed]
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#### Training Hyperparameters
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- **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]
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## Evaluation
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<!-- 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
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<!-- 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]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### 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]
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<!-- 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]
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---
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language:
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- en
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- ko
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license: llama2
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library_name: transformers
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tags:
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- tech
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- translation
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- enko
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- ko
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base_model:
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- meta-llama/Meta-Llama-3-8B-Instruct
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datasets:
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- nayohan/026_tech_translation
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pipeline_tag: text-generation
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# **Introduction**
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The model was trained to translate a single sentence from English to Korean with a 1.18M dataset in the general domain.
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Dataset: [nayohan/aihub-en-ko-translation-1.2m](https://huggingface.co/datasets/nayohan/aihub-en-ko-translation-1.2m)
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### **Loading the Model**
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Use the following Python code to load the model:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "nayohan/llama3-8b-it-translation-general-en-ko-1sent"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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device_map="auto",
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torch_dtype=torch.bfloat16
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)
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```
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### **Generating Text**
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To generate text, use the following Python code: Currently, this model only support English to Korean, not other languages or reverse and styles.
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```python
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style="written"
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SYSTEM_PROMPT=f"Acts as a translator. Translate en sentences into ko sentences in {style} style."
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s = "The aerospace industry is a flower in the field of technology and science."
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conversation = [{'role': 'system', 'content': SYSTEM_PROMPT},
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{'role': 'user', 'content': s}]
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inputs = tokenizer.apply_chat_template(
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conversation,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors='pt'
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).to("cuda")
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outputs = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0][len(inputs[0]):]))
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```
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```
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# Result
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# INPUT: <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nActs as a translator. Translate en sentences into ko sentences in colloquial style.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nThe aerospace industry is a flower in the field of technology and science.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n
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# OUTPUT: ํญ๊ณต ์ฐ์ฃผ ์ฐ์
์ ๊ธฐ์ ๊ณผ ๊ณผํ์ ๊ฝ์
๋๋ค.<|eot_id|>
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# INPUT: <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nActs as a translator. Translate en sentences into ko sentences in colloquial style.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTechnical and basic sciences are very important in terms of research. It has a significant impact on the industrial development of a country. Government policies control the research budget.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n
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# OUTPUT: ๊ธฐ์ ๊ณผ ๊ธฐ์ด๊ณผํ์ ์ฐ๊ตฌ ์ธก๋ฉด์์ ๋งค์ฐ ์ค์ํฉ๋๋ค. ํ ๊ตญ๊ฐ์ ์ฐ์
๋ฐ์ ์ ํฐ ์ํฅ์ ๋ฏธ์นฉ๋๋ค. ์ ๋ถ ์ ์ฑ
์ ์ฐ๊ตฌ ์์ฐ์ ํต์ ํฉ๋๋ค.<|eot_id|>
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```
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### **Citation**
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```bibtex
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@article{llama3modelcard,
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title={Llama 3 Model Card},
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author={AI@Meta},
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year={2024},
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url={https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
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}
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```
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Our trainig code can be found here: [TBD]
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