SJ-Donald/SOLAR-10.7B-slerp
SJ-Donald/SOLAR-10.7B-slerp is a 10.7 billion parameter language model created by SJ-Donald through a slerp merge of LDCC/LDCC-SOLAR-10.7B and upstage/SOLAR-10.7B-Instruct-v1.0. This model demonstrates strong performance across various benchmarks, including an average of 72.58 on the Open LLM Leaderboard and 56.93 on the Open-Ko-LLM-Leaderboard, indicating its capabilities in general reasoning and Korean language tasks. It is suitable for applications requiring a balanced performance across diverse linguistic and reasoning challenges.
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Model Overview
SJ-Donald/SOLAR-10.7B-slerp is a 10.7 billion parameter language model developed by SJ-Donald. It was created using the slerp (spherical linear interpolation) merge method via mergekit, combining two base models: LDCC/LDCC-SOLAR-10.7B and upstage/SOLAR-10.7B-Instruct-v1.0.
Key Capabilities & Performance
This model exhibits robust performance across a range of benchmarks:
- Open LLM Leaderboard: Achieves an average score of 72.58, with notable results in:
- AI2 Reasoning Challenge (25-Shot): 68.17
- HellaSwag (10-Shot): 86.91
- MMLU (5-Shot): 66.73
- TruthfulQA (0-shot): 67.42
- Winogrande (5-shot): 84.06
- GSM8k (5-shot): 62.17
- Open-Ko-LLM-Leaderboard: Demonstrates an average score of 56.93, indicating proficiency in Korean language tasks, including:
- Ko-ARC: 53.58
- Ko-HellaSwag: 62.03
- Ko-MMLU: 53.31
- Ko-TruthfulQA: 57.16
- Ko-CommonGen V2: 58.56
When to Use This Model
SOLAR-10.7B-slerp is well-suited for use cases requiring a versatile language model with strong general reasoning and language understanding capabilities. Its performance on both general and Korean-specific benchmarks suggests it can be effectively applied in multilingual contexts or applications where a balanced performance across diverse tasks is critical. Developers can integrate it using the provided Hugging Face transformers library example.