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Model: Undi95/MLewd-ReMM-L2-Chat-20B Source: Original Platform
2026-05-08 02:07:08 +08:00
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+---
+license: cc-by-nc-4.0
+tags:
+- not-for-all-audiences
+- nsfw
+---
+
+First :
+```shell
+layer_slices:
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 0
+    end: 16
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 8
+    end: 20
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 17
+    end: 32
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 21
+    end: 40
+```
+
+Inverted:
+```shell
+layer_slices:
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 0
+    end: 16
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 8
+    end: 20
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 17
+    end: 32
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 21
+    end: 40
+```
+
+Precise:
+```shell
+layer_slices:
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 0
+    end: 8
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 4
+    end: 12
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 9
+    end: 16
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 13
+    end: 22
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 17
+    end: 24
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 23
+    end: 32
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 25
+    end: 32
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 33
+    end: 40
+```
+
+PreciseInverted:
+```shell
+layer_slices:
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 0
+    end: 8
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 4
+    end: 12
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 9
+    end: 16
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 13
+    end: 22
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 17
+    end: 24
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 23
+    end: 32
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 25
+    end: 32
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 33
+    end: 40
+```
+
+Part1 = ReMM v2.1 merged /w MLewd low weight to keep consistency. I call this "dilution" and result show consistency and coherency without repeat/loop beside the small amount of duplicated datas.
+
+The goal is to find the best way to interlace layers the best way possible to have a sweetspot between 13B and +30B.
+
+Normal/Inverted is by chunk of 16 layers and Precise/PreciseInverted is by chunk of 8 layers.
+
+All the models are made of 64(+1) layers. Need testing.
+
+## Prompt template: Alpaca
+
+```
+Below is an instruction that describes a task. Write a response that completes the request.
+
+### Instruction:
+{prompt}
+
+### Response:
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-ReMM-L2-Chat-20B)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 53.33   |
+| ARC (25-shot)         | 62.46          |
+| HellaSwag (10-shot)   | 85.62    |
+| MMLU (5-shot)         | 59.13         |
+| TruthfulQA (0-shot)   | 55.63   |
+| Winogrande (5-shot)   | 77.19   |
+| GSM8K (5-shot)        | 10.92        |
+| DROP (3-shot)         | 22.33         |