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  ---
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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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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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  ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-
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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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- ## 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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- [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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- **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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  library_name: transformers
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+ license: llama3
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+ language:
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+ - ja
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+ - en
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  ---
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+ ## Llama-3-ELYZA-JP-8B
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+ ![Llama-3-ELYZA-JP-8B-image](./key_visual.png)
 
 
 
 
 
 
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  ### Model Description
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+ **Llama-3-ELYZA-JP-8B** is a large language model trained by [ELYZA, Inc](https://elyza.ai/).
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+ Based on [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), it has been enhanced for Japanese usage through additional pre-training and instruction tuning.
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+
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+ For more details, please refer to [our blog post](https://note.com/elyza/n/n360b6084fdbd).
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+
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+ ### Usage
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+
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+ ```python
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
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+ text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"
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+
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+ model_name = "elyza/Llama-3-ELYZA-JP-8B"
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+
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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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+ torch_dtype="auto",
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+ device_map="auto",
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+ )
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+ model.eval()
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+
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+ messages = [
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+ {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
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+ {"role": "user", "content": text},
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+ ]
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+ prompt = tokenizer.apply_chat_template(
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+ messages,
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+ token_ids = tokenizer.encode(
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+ prompt, add_special_tokens=False, return_tensors="pt"
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+ )
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+
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+ with torch.no_grad():
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+ output_ids = model.generate(
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+ token_ids.to(model.device),
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+ max_new_tokens=1200,
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+ do_sample=True,
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+ temperature=0.6,
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+ top_p=0.9,
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+ )
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+ output = tokenizer.decode(
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+ output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
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+ )
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+ print(output)
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+ ```
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+
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+ ### Developers
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+ Listed in alphabetical order.
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+ - [Masato Hirakawa](https://huggingface.co/m-hirakawa)
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+ - [Shintaro Horie](https://huggingface.co/e-mon)
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+ - [Tomoaki Nakamura](https://huggingface.co/tyoyo)
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+ - [Daisuke Oba](https://huggingface.co/daisuk30ba)
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+ - [Sam Passaglia](https://huggingface.co/passaglia)
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+ - [Akira Sasaki](https://huggingface.co/akirasasaki)
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+
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+ ### License
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+ [Meta Llama 3 Community License](https://llama.meta.com/llama3/license/)
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+
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+ ### How to Cite
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+ ```tex
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+ @misc{elyzallama2024,
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+ title={elyza/Llama-3-ELYZA-JP-8B},
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+ url={https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B},
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+ author={Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura and Daisuke Oba and Sam Passaglia and Akira Sasaki},
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+ year={2024},
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+ }
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+ ```
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+
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+ ### Citations
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+
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+ ```tex
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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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+ ```