metadata

base_model:
  - mistralai/Mistral-7B-v0.1
  - berkeley-nest/Starling-LM-7B-alpha
  - mlabonne/AlphaMonarch-7B
  - cognitivecomputations/WestLake-7B-v2-laser
  - senseable/garten2-7b
library_name: transformers
tags:
  - mergekit
  - merge
license: cc-by-nc-4.0

Starling_Monarch_Westlake_Garten-7B-v0.1

After experimenting with density for a previous merge (containing similar models), I decided to experiment with weight gradients. My thought that was that if the merge was done with care and attention, I'd be able to create something greater than the sum of its parts. Hoping that, through a merge of really good models, I'd be able to create soemthing greater than the sum of its parts.

I came across the EQ-Bench Benchmark (Paper) as part of my earlier testing. It is a very light and quick benchmark that yields powerful insights into how well the model performs in emotional intelligence related prompts. As part of this process, I tried to figure out if there was a way to determine an optimal set of gradient weights that would lead to the most successful merge as measured against EQ-Bench. At first, my goal was to simply exceed WestLake-7B, but then I kept pushing to see what I could come up with. Way too late in the process, did I learn that dare_ties has a random element to it, but considered it valuable information for next time. After concluding that project, I began collecting more data, this time setting a specified seed in mergekit for reproducibility. This model is not a result of the above work but is the genesis of how this model came to be.

I present, Starling_Monarch_Westlake_Garten-7B-v0.1, the only 7B model to score > 80 on the EQ-Bench v2.1 benchmark found here, outscoring larger models like abacusai/Smaug-72B-v0.1 and cognitivecomputations/dolphin-2.2-70b

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using mistralai/Mistral-7B-v0.1 as a base. The seed for this merge is 176

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model

  - model: cognitivecomputations/WestLake-7B-v2-laser
    parameters:
      density: 0.58
      weight:  [0.3877, 0.1636, 0.186, 0.0502]



  - model: senseable/garten2-7b
    parameters:
      density: 0.58
      weight:  [0.234, 0.2423, 0.2148, 0.2775]



  - model: berkeley-nest/Starling-LM-7B-alpha
    parameters:
      density: 0.58
      weight:  [0.1593, 0.1573, 0.1693, 0.3413]



  - model: mlabonne/AlphaMonarch-7B
    parameters:
      density: 0.58
      weight:  [0.219, 0.4368, 0.4299, 0.331]



merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

Table of Benchmarks

Open LLM Leaderboard

	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1	XX.XX	XX.XX	XX.XX	XX.XX	XX.XX	XX.XX	XX.XX
mlabonne/AlphaMonarch-7B	75.99	73.04	89.18	64.4	77.91	84.69	66.72
senseable/WestLake-7B-v2	74.68	73.04	88.65	64.71	67.06	86.98	67.63
berkeley-nest/Starling-LM-7B-alpha	67.13	63.82	84.9	63.64	46.39	80.58	62.4
senseable/garten2-7b	72.65	69.37	87.54	65.44	59.5	84.69	69.37

Yet Another LLM Leaderboard benchmarks

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1	XX.XX	XX.XX	XX.XX	XX.XX	XX.XX
mlabonne/AlphaMonarch-7B	62.74	45.37	77.01	78.39	50.2
berkeley-nest/Starling-LM-7B-alpha	51.16	42.06	72.72	47.33	42.53

Misc. Benchmarks

	MT-Bench	EQ-Bench v2.1
giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1	8.109375	80.01 (3 Shot, ooba)
mlabonne/AlphaMonarch-7B	7.928125	76.08
senseable/WestLake-7B-v2	X	78.7
berkeley-nest/Starling-LM-7B-alpha	8.09	XX.X
senseable/garten2-7b	X	XX.X
claude-v1	7.900000	76.83
gpt-3.5-turbo	7.943750	71.74
	(Paper)	(Paper) Leaderboard