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Model: anjohn0077/NEXS-deepseek-7b-multislerp
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- mergekit
+- merge
+- multislerp
+- task-vector
+base_model:
+- deepseek-ai/deepseek-llm-7b-base
+- deepseek-ai/deepseek-math-7b-instruct
+- deepseek-ai/deepseek-coder-7b-instruct-v1.5
+---
+
+# NEXS Deepseek-7B Multi-SLERP Merge
+
+A multi-SLERP merge of LlamaForCausalLM domain experts covering **math and code**, produced with
+[mergekit](https://github.com/arcee-ai/mergekit). Part of the
+[NEXS multi-SLERP merge collection](https://huggingface.co/anjohn0077/NEXS-multislerp-merges).
+
+## Method
+
+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).
+
+All variants share an identical vocabulary (102400); no tokenizer reconciliation was needed.
+
+## Sources
+
+Base model (task-vector reference): [`deepseek-ai/deepseek-llm-7b-base`](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base)
+
+Merged variants (equal weight `1.0` each):
+- [`deepseek-ai/deepseek-math-7b-instruct`](https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct)
+- [`deepseek-ai/deepseek-coder-7b-instruct-v1.5`](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5)
+
+## mergekit config
+
+```yaml
+merge_method: multislerp
+base_model: deepseek-ai/deepseek-llm-7b-base
+tokenizer_source: base
+dtype: float32
+out_dtype: bfloat16
+parameters:
+  normalize_weights: true
+  eps: 1.0e-8
+models:
+  - model: deepseek-ai/deepseek-math-7b-instruct
+    parameters: {weight: 1.0}
+  - model: deepseek-ai/deepseek-coder-7b-instruct-v1.5
+    parameters: {weight: 1.0}
+```