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 something 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. Too late in the process, I learned that dare_ties has a random element to it. Valuable information for next time, I guess. After concluding that project, I began collecting more data, this time setting a specified seed in mergekit for reproducibility. As I was collecting data, I hit the goal I had set for myself. 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

It also surpasses its components in the GSM8K benchmark, with a score of 71.95. I'll be looking to bring out more logic and emotion in the next evolution of this model.

It also earned 8.109 on MT-Bench(paper), outscoring Chat-GPT 3.5 and Claude v1.

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:

• berkeley-nest/Starling-LM-7B-alpha
• mlabonne/AlphaMonarch-7B
• cognitivecomputations/WestLake-7B-v2-laser
• senseable/garten2-7b

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

Yet Another LLM Leaderboard benchmarks

Misc. Benchmarks