Update README.md

README.md

@@ -1,3 +1,73 @@
----
-license: apache-2.0
----
+---
+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. 🧵
+
+## 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]
+```
+