OLAResearch
/

OLAF2-14B

@@ -1,99 +1,111 @@
----
-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.
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/KxAsxe10pZkaqC6x82qH3.png)
-## 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. 📈
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/aZlD94ZAqxePTaGdb4TQ8.png)
-### **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).
-<!-- ![alt-text-1](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/Qa2-91s0nvwIsx0cjRM-W.png "title-1") ![alt-text-2](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/8eATw4sMb-OrgqhRujR0z.png "title-2") -->
-<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]
-```

+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+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.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/KxAsxe10pZkaqC6x82qH3.png)
+## 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. 📈
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/aZlD94ZAqxePTaGdb4TQ8.png)
+### **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).
+<!-- ![alt-text-1](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/Qa2-91s0nvwIsx0cjRM-W.png "title-1") ![alt-text-2](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/8eATw4sMb-OrgqhRujR0z.png "title-2") -->
+<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]
+```