Eric111/Mayo
Eric111/Mayo is a 7 billion parameter language model created by Eric111, formed by merging mlabonne/NeuralBeagle14-7B and openchat/openchat-3.5-0106 using the slerp merge method. This model leverages the strengths of its constituent models to offer a balanced performance profile. It is suitable for general-purpose language generation tasks within its 4096-token context window.
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Mayo Model Overview
Mayo is a 7 billion parameter language model developed by Eric111, created through a merge of two distinct base models: mlabonne/NeuralBeagle14-7B and openchat/openchat-3.5-0106. This merge was performed using mergekit with the slerp (spherical linear interpolation) method, aiming to combine the capabilities of its components.
Key Characteristics
- Merged Architecture: Combines
mlabonne/NeuralBeagle14-7Bandopenchat/openchat-3.5-0106. - Merge Method: Utilizes
slerpfor layer-wise interpolation, with specific parameter weighting for self-attention and MLP layers. - Parameter Count: 7 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context window of 4096 tokens.
- Data Type: Configured to use
bfloat16for efficient processing.
Intended Use Cases
Mayo is designed for general language generation and understanding tasks, benefiting from the combined strengths of its merged predecessors. It is suitable for applications requiring a capable 7B model with a standard context window, where a blend of reasoning and conversational abilities is desired.