51 lines
1.9 KiB
Markdown
51 lines
1.9 KiB
Markdown
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---
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library_name: transformers
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tags:
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- mergekit
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- merge
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- multislerp
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- task-vector
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base_model:
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- deepseek-ai/deepseek-llm-7b-base
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- deepseek-ai/deepseek-math-7b-instruct
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- deepseek-ai/deepseek-coder-7b-instruct-v1.5
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---
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# NEXS Deepseek-7B Multi-SLERP Merge
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A multi-SLERP merge of LlamaForCausalLM domain experts covering **math and code**, produced with
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[mergekit](https://github.com/arcee-ai/mergekit). Part of the
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[NEXS multi-SLERP merge collection](https://huggingface.co/anjohn0077/NEXS-multislerp-merges).
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## Method
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Multi-SLERP (`multislerp`) performs barycentric spherical interpolation on a hypersphere for more than two models: it projects the models into the tangent space at their weighted Euclidean mean, interpolates, and projects back. Here it is run in **task-vector space** — each source's delta from the shared base model is computed, the deltas are spherically averaged with equal weight (`normalize_weights: true`, `eps: 1e-8`), and the result is added back to the base. Merging was done with [mergekit](https://github.com/arcee-ai/mergekit).
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All variants share an identical vocabulary (102400); no tokenizer reconciliation was needed.
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## Sources
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Base model (task-vector reference): [`deepseek-ai/deepseek-llm-7b-base`](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base)
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Merged variants (equal weight `1.0` each):
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- [`deepseek-ai/deepseek-math-7b-instruct`](https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct)
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- [`deepseek-ai/deepseek-coder-7b-instruct-v1.5`](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5)
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## mergekit config
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```yaml
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merge_method: multislerp
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base_model: deepseek-ai/deepseek-llm-7b-base
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tokenizer_source: base
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dtype: float32
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out_dtype: bfloat16
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parameters:
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normalize_weights: true
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eps: 1.0e-8
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models:
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- model: deepseek-ai/deepseek-math-7b-instruct
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parameters: {weight: 1.0}
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- model: deepseek-ai/deepseek-coder-7b-instruct-v1.5
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parameters: {weight: 1.0}
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```
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