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---
license: apache-2.0
tags:
- mergekit
- merge
- Etheria
base_model:
- brucethemoose/Yi-34B-200K-DARE-megamerge-v8
- one-man-army/UNA-34Beagles-32K-bf16-v1
model-index:
- name: VerB-Etheria-55b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 65.96
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/VerB-Etheria-55b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 81.48
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/VerB-Etheria-55b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 73.78
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/VerB-Etheria-55b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 57.52
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/VerB-Etheria-55b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 75.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/VerB-Etheria-55b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 28.81
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/VerB-Etheria-55b
name: Open LLM Leaderboard
---
# 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:
```yaml
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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Steelskull__VerB-Etheria-55b)
| 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|
|