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We are thrilled to announce the release of **OLAFv2**, our state-of-the-art Korean language model, now available on Hugging Face! π 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.
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## Key Features of OLAFv2 π
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### **Two Model Sizes for Flexibility**
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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. π
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### **Long Context Support** π
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With support for up to **32K tokens**, OLAFv2 is perfect for:
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- Retrieval-Augmented Generation (RAG). π οΈ
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- Tasks requiring long-context understanding and reasoning. π§΅
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## Getting Started π
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OLAFv2 is now available on Hugging Face! You can start using it by accessing our repository:
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We are thrilled to announce the release of **OLAFv2**, our state-of-the-art Korean language model, now available on Hugging Face! π 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.
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/KxAsxe10pZkaqC6x82qH3.png)
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## Key Features of OLAFv2 π
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### **Two Model Sizes for Flexibility**
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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. π
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/aZlD94ZAqxePTaGdb4TQ8.png)
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### **Long Context Support** π
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With support for up to **32K tokens**, OLAFv2 is perfect for:
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- Retrieval-Augmented Generation (RAG). π οΈ
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- Tasks requiring long-context understanding and reasoning. π§΅
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## Benchmarks and Performance π
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We share evaluation results across three benchmarks, KMMLU, HRM8K and LogicKor.
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<div style="text-align: center;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/rCloMEgq16D8-UuCkM8Pa.png" width="700px" height="450px" title="polyglot_budget" alt="polyglot_budget"/>
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</div>
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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).
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<!-- ![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") -->
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<div style="display: flex; justify-content: space-between; align-items: center;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/Qa2-91s0nvwIsx0cjRM-W.png" alt="alt-text-1" title="title-1" style="width: 40%;"/>
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650c0029987b1ae4e51fa2d4/8eATw4sMb-OrgqhRujR0z.png" alt="alt-text-2" title="title-2" style="width: 40%;"/>
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</div>
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## Getting Started π
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OLAFv2 is now available on Hugging Face! You can start using it by accessing our repository:
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