transformers safetensors llama text-generation conversational autotrain_compatible text-generation-inference endpoints_compatible region:us
---
library_name: transformers
tags: []
---

# Model Card for `leejaymin/etri-ones-llama3.1-8b-ko`

## Model Summary

This model is a fine-tuned version of LLaMA 3.1 (8B) using QLoRA (Quantized Low-Rank Adaptation) techniques, specifically trained on Korean language datasets. It is optimized for understanding and generating text in Korean, making it suitable for various NLP tasks in this language, including text generation, translation, and comprehension. 

## Model Details

- **Developed by:** Leejaymin / ETRI
- **Finetuned from:** LLaMA 3.1 (8B)
- **Language(s):** Korean
- **Model type:** Causal Language Model
- **License:** [More Information Needed]
- **Framework:** Hugging Face 🤗 Transformers

## Model Sources

- **Repository:** [Link to Hugging Face Repo](https://huggingface.co/leejaymin/etri-ones-llama3.1-8b-ko)

## Uses

### Direct Use
The model is designed for direct application in various Korean NLP tasks such as:
- Text generation
- Summarization
- Machine translation
- Conversational agents (chatbots)

### Downstream Use
This model can be further fine-tuned for specific tasks such as sentiment analysis, information extraction, or more focused conversational systems tailored for different domains in the Korean language.

### Out-of-Scope Use
The model is not designed for:
- Applications requiring real-time inference in highly constrained environments
- Non-Korean languages (performance will be poor on non-Korean text)

## Bias, Risks, and Limitations

Given that the model was fine-tuned on a specific Korean dataset, it may inherit biases present in the original data. Users should be aware that biases in the training data may propagate into the model's outputs.

### Recommendations
Users are advised to be cautious when deploying the model in sensitive or high-stakes environments. Fine-tuning on domain-specific data or conducting bias evaluations may be necessary depending on the intended use case.

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("leejaymin/etri-ones-llama3.1-8b-ko")
model = AutoModelForCausalLM.from_pretrained("leejaymin/etri-ones-llama3.1-8b-ko")

inputs = tokenizer("안녕하세요!", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Citation
```bibtex
@misc{leejaymin2024etrionesllama,
  title={ETRI LLaMA 3.1 8B KO},
  author={Lee, Jaymin and ETRI},
  year={2024},
  url={https://huggingface.co/leejaymin/etri-ones-llama3.1-8b-ko}
}
```



