yevvonlim/Llada-8B-Instruct-Kor

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

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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• —May produce incorrect or outdated facts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.

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