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This model is intended to be a strong base suitable for downstream fine-tuning on a variety of tasks. Based on our internal evaluations, we believe it's one of the strongest models for most down-stream tasks. You can read more about our development and evaluation process [here](https://openpipe.ai/blog/mistral-7b-fine-tune-optimized).
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This model is intended to be a strong base suitable for downstream fine-tuning on a variety of tasks. Based on our internal evaluations, we believe it's one of the strongest models for most down-stream tasks. You can read more about our development and evaluation process [here](https://openpipe.ai/blog/mistral-7b-fine-tune-optimized).
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[Mergekit](https://github.com/cg123/mergekit) config used to create this model:
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```yaml
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slices:
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- sources:
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- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
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layer_range: [0, 32]
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- model: Q-bert/MetaMath-Cybertron-Starling
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layer_range: [0, 32]
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merge_method: slerp
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base_model: mistralai/Mistral-7B-v0.1
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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 # fallback for rest of tensors
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dtype: bfloat16
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```
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*Update*: It appears that https://huggingface.co/Weyaxi/Seraph-7B was merged from the same base models using the same [mergekit](https://github.com/cg123/mergekit) defaults as this model. So major credit goes to @Weyaxi both for creating one of the base merges this model was merged from, as well as being the first one to perform this exact merge as well!
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