Muhammad2003/Myriad-7B-Slerp
Muhammad2003/Myriad-7B-Slerp is a 7 billion parameter language model created by Muhammad2003, formed by merging MaziyarPanahi/Calme-7B-Instruct-v0.9 and yam-peleg/Experiment26-7B using the Slerp method. This model leverages a context length of 8192 tokens and is designed to combine the strengths of its constituent models. It demonstrates competitive performance across various benchmarks, including MMLU, Hellaswag, and GSM8k, making it suitable for general-purpose conversational AI and reasoning tasks.
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Myriad-7B-Slerp: A Merged 7B Language Model
Myriad-7B-Slerp is a 7 billion parameter language model developed by Muhammad2003, created through a Slerp merge of two distinct models: MaziyarPanahi/Calme-7B-Instruct-v0.9 and yam-peleg/Experiment26-7B. This merging technique, facilitated by LazyMergekit, aims to combine the capabilities of its base models to achieve enhanced performance.
Key Capabilities & Performance
This model operates with an 8192-token context window and has been evaluated across several common benchmarks, showcasing its general utility:
- ARC: 73.38
- Hellaswag: 89.05
- MMLU: 64.32
- TruthfulQA: 77.95
- Winogrande: 84.85
- GSM8k: 70.28
These scores indicate its proficiency in areas such as common sense reasoning, language understanding, and mathematical problem-solving.
When to Use This Model
Myriad-7B-Slerp is a strong candidate for applications requiring a capable 7B model with a balanced performance profile. Its merged architecture suggests a broad range of potential uses, including:
- General-purpose conversational AI
- Text generation and summarization
- Reasoning and question-answering tasks
- Educational tools and content creation
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.