---
title: NeuralMathsRolePlay-7b
canonical_url: "https://www.modelscope.cn/models/Kukedlc/NeuralMathsRolePlay-7b"
md_url: "https://www.modelscope.cn/models/Kukedlc/NeuralMathsRolePlay-7b.md"
repository: Kukedlc/NeuralMathsRolePlay-7b
last_updated: 2026-06-01
model_type:
  - mistral
architectures:
  - MistralForCausalLM
base_model:
  - mlabonne/AlphaMonarch-7B
  - Kukedlc/NeuralMaths-Experiment-7b
base_model_relation: merge
parameters: 7.2B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 14
stars: 0
tags:
  - mergekit
  - merge
---

# NeuralMathsRolePlay-7b

> NeuralMathsRolePlay-7b - Kukedlc 在 ModelScope 开源的模型。This is a merge of pre-trained language models created using mergekit.

Kukedlc/NeuralMathsRolePlay-7b 是 ModelScope 魔搭社区上的 7.2B 参数机器学习模型，基于 mlabonne/AlphaMonarch-7B、Kukedlc/NeuralMaths-Experiment-7b 构建。

- **Repository**: Kukedlc/NeuralMathsRolePlay-7b
- **Parameters**: 7.2B
- **Base model**: mlabonne/AlphaMonarch-7B, Kukedlc/NeuralMaths-Experiment-7b
- **Tags**: mergekit, merge
- **Downloads**: 14
- **Stars**: 0
- **Last updated**: 2026-06-01

Source: https://www.modelscope.cn/models/Kukedlc/NeuralMathsRolePlay-7b

---

# merge

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the SLERP merge method.

### Models Merged

The following models were included in the merge:
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [Kukedlc/NeuralMaths-Experiment-7b](https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
  - sources:
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [0, 32]
      - model: Kukedlc/NeuralMaths-Experiment-7b
        layer_range: [0, 32]
merge_method: slerp
base_model: Kukedlc/NeuralMaths-Experiment-7b
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```