DistilabelCerberus-7B-slerp
DistilabelCerberus-7B-slerp is a merge of the following models using mergekit:
🧩 Configuration
slices:
- sources:
- model: dvilasuero/DistilabelBeagle14-7B
layer_range: [0, 32]
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
Results
| ARC-C | Hellaswag | ThruthfulQA | Winogrande | GSM8K | |||
|---|---|---|---|---|---|---|---|
| OpenHermes-2.5-Mistral-7B | 61.26 | 65.22 | 52.24 | 78.06 | 26.08 | ||
| DistilabelBeagle14-7B | ? | ? | 71.66 | ? | ? | ||
| DistilabelCerberus-7B-slerp | 65.44 | 69.29 | 60.93 | 79.48 | 69.82 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 71.56 |
| AI2 Reasoning Challenge (25-Shot) | 68.17 |
| HellaSwag (10-Shot) | 86.78 |
| MMLU (5-Shot) | 64.20 |
| TruthfulQA (0-shot) | 60.93 |
| Winogrande (5-shot) | 79.48 |
| GSM8k (5-shot) | 69.83 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.170
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.780
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.200
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard60.930
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard79.480
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard69.830