Moko-SAMPLE: A Merged 7B Language Model
Moko-SAMPLE is a 7 billion parameter language model developed by Azazelle, constructed through a sophisticated merge of several pre-trained models. Utilizing the sample_ties merge method from mergekit, it builds upon the mistralai/Mistral-7B-v0.1 base model.
Key Capabilities & Composition
This model integrates the specialized strengths of three distinct models:
- WizardLM/WizardMath-7B-V1.1: Contributes to enhanced mathematical reasoning and problem-solving abilities.
- akjindal53244/Mistral-7B-v0.1-Open-Platypus: Likely improves general instruction following and conversational capabilities.
- Open-Orca/Mistral-7B-OpenOrca: Further refines instruction-tuned performance and broad knowledge.
The merge configuration involved specific density and weight gradients for each component, aiming to optimize the combined model's performance across various tasks. The process included normalization and int8 masking, with the final model operating in float16 precision.
When to Consider Moko-SAMPLE
This model is particularly suitable for use cases that benefit from a blend of:
- Improved mathematical reasoning: Due to the inclusion of WizardMath.
- Robust instruction following: Inherited from its Open-Platypus and OpenOrca components.
- General-purpose text generation: Leveraging the strong Mistral-7B base.
Developers looking for a 7B model that combines diverse capabilities from well-regarded instruction-tuned and specialized models should evaluate Moko-SAMPLE.