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--- |
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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-Inverted) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 50.81 | |
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| ARC (25-shot) | 61.69 | |
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| HellaSwag (10-shot) | 85.32 | |
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| MMLU (5-shot) | 58.0 | |
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| TruthfulQA (0-shot) | 53.77 | |
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| Winogrande (5-shot) | 75.61 | |
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| GSM8K (5-shot) | 9.1 | |
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| DROP (3-shot) | 12.16 | |
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