Kukedlc/NeuralSynthesis-7b-v0.4-slerp
NeuralSynthesis-7b-v0.4-slerp is a 7 billion parameter language model created by Kukedlc, resulting from a slerp merge of allknowingroger/MultiverseEx26-7B-slerp and Kukedlc/NeuralSynthesis-7B-v0.1. This model leverages a specific layer-wise parameter interpolation to combine the strengths of its base models. It is designed for general text generation tasks, offering a balanced performance derived from its merged components.
Loading preview...
Overview
NeuralSynthesis-7b-v0.4-slerp is a 7 billion parameter language model developed by Kukedlc. It is a product of a slerp merge (spherical linear interpolation) of two distinct models: allknowingroger/MultiverseEx26-7B-slerp and Kukedlc/NeuralSynthesis-7B-v0.1. This merging technique allows for a nuanced combination of the characteristics of its constituent models.
Merge Configuration
The model was created using LazyMergekit with a specific configuration:
- Source Models:
allknowingroger/MultiverseEx26-7B-slerpandKukedlc/NeuralSynthesis-7B-v0.1. - Layer Range: Both models contributed layers from 0 to 32.
- Merge Method: Slerp (spherical linear interpolation).
- Base Model:
Kukedlc/NeuralSynthesis-7B-v0.1. - Parameter Weights: Different interpolation values were applied to self-attention (
self_attn) and MLP (mlp) layers, with a general value of 0.5 for other parameters, indicating a balanced blend.
Usage
This model is suitable for general text generation tasks. Developers can easily integrate it using the Hugging Face transformers library, as demonstrated in the provided Python example for generating text based on user prompts.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.