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---
license: cc-by-4.0
tags:
- merge
- moe
model-index:
- name: Open_Gpt4_8x7B
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: 69.28
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B
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: 86.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B
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: 71.2
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B
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: 70.39
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B
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: 81.77
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B
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: 59.21
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B
name: Open LLM Leaderboard
---
Open_Gpt4
cc
____________________________________________________________________________________________
VERSION 0.2 OUT NOW:
Fp16:
- https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2
q8_0.gguf:
- https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2_q8_0_gguf
____________________________________________________________________________________________
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/T7QKB0fKNHQvNqAjm8zrH.jpeg)
This model is a TIES merger of notux-8x7b-v1 and UNAversal-8x7B-v1beta with MixtralOrochi8x7B being the Base model.
I was very impressed with MixtralOrochi8x7B performance and multifaceted usecases as it is already a merger of many usefull Mixtral models such as Mixtral instruct,
Noromaid-v0.1-mixtral, openbuddy-mixtral and possibly other models that were not named. My goal was to expand the models capabilities and make it even more useful of a model, maybe even competitive with closed source models like Gpt-4. But for that more testing is required. I hope the community can help me determine if its deserving of its name. 😊
Base model:
- https://huggingface.co/smelborp/MixtralOrochi8x7B
Merged models:
- https://huggingface.co/fblgit/UNAversal-8x7B-v1beta
- https://huggingface.co/argilla/notux-8x7b-v1
Instruct template: Alpaca
Merger config:
```yaml
models:
- model: notux-8x7b-v1
parameters:
density: .5
weight: 1
- model: UNAversal-8x7B-v1beta
parameters:
density: .5
weight: 1
merge_method: ties
base_model: MixtralOrochi8x7B
parameters:
normalize: true
int8_mask: true
dtype: float16
```
# [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_rombodawg__Open_Gpt4_8x7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |73.10|
|AI2 Reasoning Challenge (25-Shot)|69.28|
|HellaSwag (10-Shot) |86.77|
|MMLU (5-Shot) |71.20|
|TruthfulQA (0-shot) |70.39|
|Winogrande (5-shot) |81.77|
|GSM8k (5-shot) |59.21|
|