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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- mergekit |
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- text-generation |
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- merge |
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--- |
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# Mistral-NeuralHermes-Merge-7B-slerp |
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## Model Description |
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The `Mistral-Merge-7B-slerp` is a merged model which leverages the spherical linear interpolation (SLERP) technique to blend layers from two distinct transformer-based models. This merging strategy is aimed at synthesizing a model that incorporates the robust linguistic capabilities of `OpenPipe/mistral-ft-optimized-1218` and the nuanced understanding of `mlabonne/NeuralHermes-2.5-Mistral-7B`. |
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## Configuration |
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The merging process was configured to apply a SLERP method across all comparable layers of the two source models. Below is the YAML configuration used for merging: |
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```yaml |
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slices: |
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- sources: |
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- model: OpenPipe/mistral-ft-optimized-1218 |
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layer_range: [0, 32] |
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- model: mlabonne/NeuralHermes-2.5-Mistral-7B |
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layer_range: [0, 32] |
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merge_method: slerp |
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base_model: OpenPipe/mistral-ft-optimized-1218 |
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parameters: |
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t: |
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- filter: self_attn |
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value: [0, 0.5, 0.3, 0.7, 1] |
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- filter: mlp |
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value: [1, 0.5, 0.7, 0.3, 0] |
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- value: 0.5 |
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dtype: bfloat16 |
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``` |
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This configuration ensures that both self-attention and MLP (multi-layer perceptron) layers undergo interpolation with a gradient of weights to optimize the integration of features from both models. |
