ko-r1-7b-v2.0.3

Model Card: OLAIR/ko-r1-7b-v2.0.3

This document describes the OLAIR/ko-r1-7b-v2.0.3 model, including its training data, intended use, performance benchmarks, limitations, and ethical considerations.

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1. Overview

Model Name: OLAIR/ko-r1-7b-v2.0.3
Model Type: Large Language Model (LLM) for Korean language understanding and reasoning
Version: 2.0.3

This model is designed to provide Korean language capabilities with a focus on reasoning tasks. It is the second version in its series, building upon previous iterations with improvements in training data and fine-tuning methodologies.

2. Training Data

The model was trained on the dataset provided by OLAIR, specifically the Open-R1-Ko-SFT-v2.0 dataset. This dataset includes a curated collection of Korean language data, optimized for supervised fine-tuning (SFT) to enhance reasoning and natural language understanding capabilities in Korean.

3. Benchmark Performance

The model's performance has been evaluated using the HAE-RAE Reasoning Challenge (HRC), which measures reasoning abilities across various domains. Below are the benchmark results for several models, including OLAIR/ko-r1-7b-v2.0.3: We've noticed some errors in the previous code and updated it.

<details> <summary>Depricated Score</summary> </details>

Note: The above table reflects performance across multiple reasoning domains. The metrics indicate that while OLAIR/ko-r1-7b-v2.0.3 shows competitive performance in certain areas (e.g., Math), there remain challenges, particularly in Chemistry and Physics-related tasks, compared to some higher-performing counterparts.

4. Limitations

• The model is still vulnerable to Korean-related inputs, leading to endless loops of thinking. We are working to fix it.

ETC

How to Cite

To be added

Contact

[email protected]