File size: 4,379 Bytes
2972ebe 78b2b8c 2972ebe 78b2b8c 2972ebe 78b2b8c 799d253 78b2b8c 2972ebe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
license: apache-2.0
language:
- ko
base_model:
- Qwen/Qwen2.5-14B-Instruct
---
# 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.
<div style="text-align: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/rCloMEgq16D8-UuCkM8Pa.png" width="700px" height="450px" title="polyglot_budget" alt="polyglot_budget"/>
</div>
We also share results with inference-time scaling. For more details have a look into our [blog](https://www.onelineai.com/blog/test-time-scaling).
<!--   -->
<div style="display: flex; justify-content: space-between; align-items: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/Qa2-91s0nvwIsx0cjRM-W.png" alt="alt-text-1" title="title-1" style="width: 48%;"/>
<img src="https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/8eATw4sMb-OrgqhRujR0z.png" alt="alt-text-2" title="title-2" style="width: 48%;"/>
</div>
## Getting Started 🚀
OLAFv2 is now available on Hugging Face! You can start using it by accessing our repository:
```python
# 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]
```
|