MN-12B-Inferor-v0.0 / README.md
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---
library_name: transformers
tags:
- mergekit
- merge
base_model:
- nbeerbower/Mistral-Nemo-Gutenberg-Doppel-12B-v2
- nothingiisreal/MN-12B-Starcannon-v3
- anthracite-org/magnum-v4-12b
- Fizzarolli/MN-12b-Sunrose
model-index:
- name: MN-12B-Inferor-v0.0
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 57.08
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Svak/MN-12B-Inferor-v0.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 30.85
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Svak/MN-12B-Inferor-v0.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 10.05
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Svak/MN-12B-Inferor-v0.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 7.83
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Svak/MN-12B-Inferor-v0.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 18.09
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Svak/MN-12B-Inferor-v0.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 28.43
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Svak/MN-12B-Inferor-v0.0
name: Open LLM Leaderboard
---
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64be962a38953777feaabfc0/DncyZQ6V2NAEfFeEerxcw.png)
# Inferor
My first merge yay!
#### This was made thanks to [infermatic.ai](https://infermatic.ai/)
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [anthracite-org/magnum-v4-12b](https://huggingface.co/anthracite-org/magnum-v4-12b) as a base.
### Models Merged
The following models were included in the merge:
* [nbeerbower/Mistral-Nemo-Gutenberg-Doppel-12B-v2](https://huggingface.co/nbeerbower/Mistral-Nemo-Gutenberg-Doppel-12B-v2)
* [nothingiisreal/MN-12B-Starcannon-v3](https://huggingface.co/nothingiisreal/MN-12B-Starcannon-v3)
* [Fizzarolli/MN-12b-Sunrose](https://huggingface.co/Fizzarolli/MN-12b-Sunrose)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: anthracite-org/magnum-v4-12b
dtype: bfloat16
merge_method: model_stock
slices:
- sources:
- layer_range: [0, 40]
model: Fizzarolli/MN-12b-Sunrose
- layer_range: [0, 40]
model: nbeerbower/Mistral-Nemo-Gutenberg-Doppel-12B-v2
- layer_range: [0, 40]
model: nothingiisreal/MN-12B-Starcannon-v3
- layer_range: [0, 40]
model: anthracite-org/magnum-v4-12b
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Svak__MN-12B-Inferor-v0.0)
| Metric |Value|
|-------------------|----:|
|Avg. |25.39|
|IFEval (0-Shot) |57.08|
|BBH (3-Shot) |30.85|
|MATH Lvl 5 (4-Shot)|10.05|
|GPQA (0-shot) | 7.83|
|MuSR (0-shot) |18.09|
|MMLU-PRO (5-shot) |28.43|