131 lines
3.1 KiB
Markdown
131 lines
3.1 KiB
Markdown
---
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license: cc-by-nc-4.0
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tags:
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- not-for-all-audiences
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- nsfw
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---
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First :
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```shell
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layer_slices:
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- model: Undi95/MLewd-L2-Chat-13B
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start: 0
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end: 16
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 8
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end: 20
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- model: Undi95/MLewd-L2-Chat-13B
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start: 17
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end: 32
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 21
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end: 40
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```
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Inverted:
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```shell
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layer_slices:
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 0
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end: 16
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- model: Undi95/MLewd-L2-Chat-13B
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start: 8
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end: 20
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 17
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end: 32
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- model: Undi95/MLewd-L2-Chat-13B
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start: 21
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end: 40
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```
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Precise:
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```shell
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layer_slices:
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- model: Undi95/MLewd-L2-Chat-13B
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start: 0
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end: 8
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 4
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end: 12
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- model: Undi95/MLewd-L2-Chat-13B
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start: 9
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end: 16
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 13
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end: 22
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- model: Undi95/MLewd-L2-Chat-13B
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start: 17
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end: 24
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 23
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end: 32
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- model: Undi95/MLewd-L2-Chat-13B
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start: 25
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end: 32
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 33
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end: 40
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```
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PreciseInverted:
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```shell
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layer_slices:
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 0
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end: 8
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- model: Undi95/MLewd-L2-Chat-13B
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start: 4
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end: 12
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 9
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end: 16
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- model: Undi95/MLewd-L2-Chat-13B
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start: 13
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end: 22
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 17
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end: 24
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- model: Undi95/MLewd-L2-Chat-13B
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start: 23
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end: 32
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- model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
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start: 25
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end: 32
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- model: Undi95/MLewd-L2-Chat-13B
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start: 33
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end: 40
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```
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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.
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The goal is to find the best way to interlace layers the best way possible to have a sweetspot between 13B and +30B.
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Normal/Inverted is by chunk of 16 layers and Precise/PreciseInverted is by chunk of 8 layers.
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All the models are made of 64(+1) layers. Need testing.
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## Prompt template: Alpaca
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```
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Below is an instruction that describes a task. Write a response that completes the request.
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### Instruction:
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{prompt}
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### Response:
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-ReMM-L2-Chat-20B)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 53.33 |
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| ARC (25-shot) | 62.46 |
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| HellaSwag (10-shot) | 85.62 |
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| MMLU (5-shot) | 59.13 |
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| TruthfulQA (0-shot) | 55.63 |
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| Winogrande (5-shot) | 77.19 |
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| GSM8K (5-shot) | 10.92 |
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| DROP (3-shot) | 22.33 |
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