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May produce incorrect or outdated facts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