VerB-Etheria-55b

An attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models, this is Version B or VerB, it is a Double Model Passthrough merge. with a 50/50 split between high performing models.

Roadmap:

Depending on quality, I Might private the other Version. Then generate a sacrificial 55b and perform a 55b Dare ties merge or Slerp merge.

1: If the Dual Model Merge performs well I will make a direct inverse of the config then merge.

2: If the single model performs well I will generate a 55b of the most performant model the either Slerp or Dare ties merge.

3: If both models perform well, then I will complete both 1 & 2 then change the naming scheme to match each of the new models.

Configuration

The following YAML configuration was used to produce this model:

dtype: bfloat16
slices:
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [0, 14]
- sources:
    - model: one-man-army/UNA-34Beagles-32K-bf16-v1
      layer_range: [7, 21]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [15, 29]
- sources:
    - model: one-man-army/UNA-34Beagles-32K-bf16-v1
      layer_range: [22, 36]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [30, 44]
- sources:
    - model: one-man-army/UNA-34Beagles-32K-bf16-v1
      layer_range: [37, 51]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [45, 59]
merge_method: passthrough

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	63.83
AI2 Reasoning Challenge (25-Shot)	65.96
HellaSwag (10-Shot)	81.48
MMLU (5-Shot)	73.78
TruthfulQA (0-shot)	57.52
Winogrande (5-shot)	75.45
GSM8k (5-shot)	28.81