---
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}
```