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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a ๐Ÿค— transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- 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
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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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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- #### 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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- #### Hardware
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- #### Software
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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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- **APA:**
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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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- ## Model Card Authors [optional]
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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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  ---
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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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+
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+ ### **Loading the Model**
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+
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+ Use the following Python code to load the model:
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+
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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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+
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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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+
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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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+
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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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+
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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 ์šฐ์ฃผ ์‚ฐ์—…์€ ๊ธฐ์ˆ ๊ณผ ๊ณผํ•™์˜ ๊ฝƒ์ž…๋‹ˆ๋‹ค.<|eot_id|>
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+
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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: ๊ธฐ์ˆ ๊ณผ ๊ธฐ์ดˆ๊ณผํ•™์€ ์—ฐ๊ตฌ ์ธก๋ฉด์—์„œ ๋งค์šฐ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ํ•œ ๊ตญ๊ฐ€์˜ ์‚ฐ์—… ๋ฐœ์ „์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นฉ๋‹ˆ๋‹ค. ์ •๋ถ€ ์ •์ฑ…์€ ์—ฐ๊ตฌ ์˜ˆ์‚ฐ์„ ํ†ต์ œํ•ฉ๋‹ˆ๋‹ค.<|eot_id|>
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+ ```
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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]