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Another trial of merging models with different sizes, still under testing, should be more stable, but I have no ideia if it's improving or degrading the base model.
In this I changed something, to have more Westlake. Recipe:
merge_method: task_anysize
base_model: princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT
models:
- model: senseable/WestLake-7B-v2
parameters:
weight: 1.0
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 36.31 |
| AI2 Reasoning Challenge (25-Shot) | 34.04 |
| HellaSwag (10-Shot) | 58.05 |
| MMLU (5-Shot) | 26.24 |
| TruthfulQA (0-shot) | 42.64 |
| Winogrande (5-shot) | 56.91 |
| GSM8k (5-shot) | 0.00 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard34.040
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard58.050
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard26.240
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard42.640
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard56.910
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.000