Announcing OLAFv2: The Next Step in Korean Language Understanding 🚀

We are thrilled to announce the release of OLAFv2, our state-of-the-art Korean language model, now available on Hugging Face! 🎉 Designed to excel in complex reasoning, mathematical problem-solving, and general language understanding, OLAFv2 represents a significant leap forward in NLP capabilities for the Korean language.

Key Features of OLAFv2 🌟

Two Model Sizes for Flexibility

OLAFv2 is available in two parameter sizes:

14B (Billion) Parameters: For maximum performance. 🏋️‍♂️
1.5B (Billion) Parameters: For lightweight applications and hardware-constrained environments. 🪶

Reasoning Mode for Complex Tasks 🤔

One of OLAFv2's standout features is its Reasoning Mode, specifically designed for:

Complex mathematical problem-solving. ✖️➗
STEM (Science, Technology, Engineering, Mathematics) applications. 🔬📐
Tasks requiring detailed step-by-step reasoning. 🧠

This mode can be effectively utilized for Test-Time Scaling, enabling the model to harness additional computational resources during inference. This approach enhances output detail and accuracy, achieving performance levels that surpass GPT-4o. 📈

Long Context Support 📜

With support for up to 32K tokens, OLAFv2 is perfect for:

Retrieval-Augmented Generation (RAG). 🛠️
Tasks requiring long-context understanding and reasoning. 🧵

Benchmarks and Performance 📊

We share evaluation results across three benchmarks, KMMLU, HRM8K and LogicKor.

We also share results with inference-time scaling. For more details have a look into our blog.

Getting Started 🚀

OLAFv2 is now available on Hugging Face! You can start using it by accessing our repository:

# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OLAResearch/OLAF2-14B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "introduce yourself!"
messages = [
    {"role": "system", "content": "You're name is OLAF. A large language model made by OneLineAI, specializing in Korean culture and finance."},
    # for reasoning mode
    #{"role": "system", "content": "You're name is OLAF. A large language model made by OneLineAI, specializing in Korean culture and finance.Perform two-step reasoning. Return your answers in \\boxed{N} format."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

OLAResearch
/

OLAF2-14B