gagan3012/MetaModelv2
gagan3012/MetaModelv2 is a 10.7 billion parameter hybrid language model, combining elements from VAGOsolutions/SauerkrautLM-SOLAR-Instruct and kyujinpy/Sakura-SOLAR-Instruct. This model is designed for general language understanding and generation tasks, demonstrating strong performance across various benchmarks. With a 4096-token context length, it offers robust capabilities for diverse applications requiring comprehensive text processing.
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MetaModelv2: A Hybrid 10.7B Language Model
MetaModelv2, developed by gagan3012, is a 10.7 billion parameter language model built as a hybrid of two existing models: VAGOsolutions/SauerkrautLM-SOLAR-Instruct and kyujinpy/Sakura-SOLAR-Instruct. This approach leverages the strengths of its constituent models to deliver a versatile and capable LLM.
Key Capabilities & Performance
Evaluated on the Open LLM Leaderboard, MetaModelv2 demonstrates solid performance across a range of benchmarks, indicating its proficiency in various reasoning and language understanding tasks. Its average score of 74.24 highlights its balanced capabilities. Specific benchmark results include:
- ARC (25-shot): 71.08
- HellaSwag (10-shot): 88.56
- MMLU (5-shot): 66.29
- TruthfulQA (0-shot): 71.94
- Winogrande (5-shot): 83.11
- GSM8K (5-shot): 64.44
With a context length of 4096 tokens, MetaModelv2 can process moderately long inputs, making it suitable for tasks requiring a decent understanding of conversational flow or document content.
Good for
- General-purpose language generation: Its balanced performance suggests suitability for a wide array of text generation tasks.
- Reasoning and question answering: Scores on ARC and MMLU indicate capabilities in logical inference and knowledge-based QA.
- Applications requiring moderate context: The 4096-token context window supports tasks like summarization of short documents or extended dialogue.