---
library_name: transformers
tags:
- llada
- sft
- instruction-tuning
- korean
- english
language:
- ko
- en
base_model:
- GSAI-ML/LLaDA-8B-Instruct
---

# Model Card for yevvonlim/Llada-8B-Instruct-Kor

<!-- Provide a quick summary of what the model is/does. -->

yevvonlim/Llada-8B-Instruct-Kor is an instruction-tuned variant of LLADA-8B designed for high-quality conversational responses in both Korean and English. Fine-tuned with supervised data, it excels at understanding and generating context-aware replies for chat applications.

## Model Details

### Model Description

This model is a supervised fine-tuned (SFT) version of [GSAI-ML/LLaDA-8B-Instruct], developed and shared by Sionic AI. It leverages parameter-efficient fine-tuning (PEFT) with LoRA to adapt the base LLADA-8B model to instruction-following tasks.

- **Developed by:** yevvonlim
- **Model type:** 8B-parameter encoder-only transformer  
- **Language(s):** Korean, English  
- **License:** Apache-2.0  
- **Fine-tuned from:** GSAI-ML/LLaDA-8B-Instruct  

### Model Sources

- **Repository:** https://github.com/yevvonlim/CAS4133-LLaDA-Kor
- **Paper:** N/A  
- **Demo:** N/A  

## Uses

### Direct Use

- Conversational agents and chatbots in Korean and English  
- Instruction-following and question-answering tasks  
- Assistive tools for writing, translation, and summarization  

### Out-of-Scope Use

- Tasks requiring specialized domain knowledge outside the training data  
- Real-time high-stakes decision-making without human oversight  

## Bias, Risks, and Limitations

- May produce incorrect or outdated facts  
- Vulnerable to generating biased or stereotypical language present in training data  

### Recommendations

Users should review and verify model outputs before deployment in critical applications. Implement human-in-the-loop validation for high-stakes use cases.

## How to Get Started with the Model

Use the code below to load and generate with the model. Ensure you have defined or imported the `generate_stream` function provided in the repository.

```python

from transformers import AutoTokenizer, AutoModel


device = "cuda"
model_path = "yevvonlim/Llada-8B-Instruct-Kor"

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(device).eval()

prompt = "6나누기 0은 뭐야? let's think step by step."
chat_input = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    tokenize=False,
)
prompt_ids = tokenizer(chat_input, return_tensors="pt").input_ids.to(device)

final_ids = model.generate(prompt_ids)[0, prompt_ids.shape[1]:]
print(tokenizer.decode(final_ids, skip_special_tokens=True))
```

## Contact

For issues or questions, please open an issue on the repo or contact ga06033@gmail.com