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This is the un-quantized fp16 version for training and merging. If you want the quantized version for inference please refer to the repo bellow:


This model is a TIES merger of Mixtral-8x7B-Instruct-v0.1 and bagel-dpo-8x7b-v0.2 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. 😊

This is the second iteration of this model, using better models in the merger to improve performance (hopefully).

Base model:

Merged models:

Instruct template: Alpaca

Merger config:

  - model: Mixtral-8x7B-Instruct-v0.1

      density: .5
      weight: 1
  - model: bagel-dpo-8x7b-v0.2
      density: .5
      weight: .7

merge_method: ties
base_model: MixtralOrochi8x7B
  normalize: true
  int8_mask: true
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 73.59
AI2 Reasoning Challenge (25-Shot) 68.69
HellaSwag (10-Shot) 86.16
MMLU (5-Shot) 72.07
TruthfulQA (0-shot) 71.92
Winogrande (5-shot) 83.58
GSM8k (5-shot) 59.14
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Model size
46.7B params
Tensor type

Space using rombodawg/Open_Gpt4_8x7B_v0.2 1

Evaluation results