EmbeddedLLM/Mistral-7B-Merge-02-v0

Model Description

This is an experiment to compare merging 2 models using DARE TIES versus SLERP 🦙

We are mainly interested to compare against Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp

The 2 models involved in the merge as follows:

1. teknium/OpenHermes-2.5-Mistral-7B
2. Intel/neural-chat-7b-v3-3

• base model: mistralai/Mistral-7B-v0.1

The yaml config file for the merge is:

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: teknium/OpenHermes-2.5-Mistral-7B
    parameters:
      weight: 0.5
      density: 0.5
  - model: Intel/neural-chat-7b-v3-3
    parameters:
      weight: 0.5
      density: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

Open LLM Leaderboard

Note that with more tuning DARE TIES might achieve better results.

	DARE TIES	SLERP
Average	70.69	71.38
ARC	67.49	68.09
HellaSwag	85.78	86.2
MMLU	64.1	64.26
TruthfulQA	60.52	62.78
Winogrande	79.01	79.16
GSM8K	67.25	67.78