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. 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 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: dtype: bfloat16
Table of Benchmarks
Open LLM Leaderboard
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |
|---|---|---|---|---|---|---|---|
| giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 | 74.9 | 71.76 | 88.15 | 65.07 | 67.92 | 84.53 | 71.95 |
| 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 | 44.99 | 76.93 | 68.04 | 47.71 | 59.42 |
| mlabonne/AlphaMonarch-7B | 45.37 | 77 | 78.39 | 50.2 | 62.74 |
| berkeley-nest/Starling-LM-7B-alpha | 42.06 | 72.72 | 47.33 | 42.53 | 51.16 |
Misc. Benchmarks
| MT-Bench | EQ-Bench v2.1 | |
|---|---|---|
| giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 | 8.109375 | 80.01 (3 Shot, ChatML, ooba) |
| mlabonne/AlphaMonarch-7B | 8.23750 | 76.08 |
| senseable/WestLake-7B-v2 | X | 78.7 |
| berkeley-nest/Starling-LM-7B-alpha | 8.09 | 68.69 (1 Shot, ChatML, ooba) |
| senseable/garten2-7b | X | 75.03 |
| claude-v1 | 7.900000 | 76.83 |
| gpt-3.5-turbo | 7.943750 | 71.74 |
| (Paper) | (Paper) Leaderboard |