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Model: Undi95/MM-ReMM-L2-20B
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---
license: cc-by-nc-4.0
---
Merge:
```shell
layer_slices:
- model: Gryphe/MythoMax-L2-13b
start: 0
end: 16
- model: Undi95/MM-ReMM-L2-20B-Part1
start: 8
end: 20
- model: Gryphe/MythoMax-L2-13b
start: 17
end: 32
- model: Undi95/MM-ReMM-L2-20B-Part1
start: 21
end: 40
```
<!-- description start -->
## Models used
- Gryphe/MythoMax-L2-13b
- Undi95/ReMM-v2.1-L2-13B
<!-- description end -->
Part1 = ReMM v2.1 merged /w MythoMax 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.
## 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__MM-ReMM-L2-20B)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 51.14 |
| ARC (25-shot) | 60.84 |
| HellaSwag (10-shot) | 85.18 |
| MMLU (5-shot) | 56.45 |
| TruthfulQA (0-shot) | 53.33 |
| Winogrande (5-shot) | 75.77 |
| GSM8K (5-shot) | 7.73 |
| DROP (3-shot) | 18.66 |

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"use_cache": false,
"vocab_size": 32000
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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