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]