WestMaid_HermesMonarchv0.1
This model benchmarks quite well compared to other 7b models, and has exceptional MT-Bench and EQ-Bench v2.1 scores, ranking higher than ChatGPT-3.5-turbo and Claude-1 in both tests, and Goliath-120b, and other 70B models in the latter .
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. Density was chosen deterministically between the models chosen for this merge. After testing many densities, I settled on 0.58 for each of the chosen models as it returned the highest EQ-Bench score. Not much testing was done with the weights, but I thought that I'd try gradients. Conceptually, Westlake and a Distilled version of Open Heremes are heavier in the initial layers (guiding understanding, and thoughts), before Noromaid and AlphaMonarch come in to guide its wants, reasoning, and conversation.
Models Merged
The following models were included in the merge:
- mlabonne/AlphaMonarch-7B
- NeverSleep/Noromaid-7B-0.4-DPO
- senseable/WestLake-7B-v2
- argilla/distilabeled-OpenHermes-2.5-Mistral-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: senseable/WestLake-7B-v2
parameters:
density: 0.58
weight: [0.50, 0.40, 0.25, 0.05]
- model: NeverSleep/Noromaid-7B-0.4-DPO
parameters:
density: 0.58
weight: [0.05, 0.05, 0.25, 0.40]
- model: argilla/distilabeled-OpenHermes-2.5-Mistral-7B
parameters:
density: 0.58
weight: [0.40, 0.50, 0.25, 0.05]
- model: mlabonne/AlphaMonarch-7B
parameters:
density: 0.58
weight: [0.05, 0.05, 0.25, 0.50]
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
Benchmark Testing
MT-Bench
EQ-Bench Leaderboard
Table of Benchmarks
Open LLM Leaderboard
Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |
---|---|---|---|---|---|---|---|
giraffe176/WestMaid_HermesMonarchv0.1 | 72.62 | 70.22 | 87.42 | 64.31 | 61.99 | 82.16 | 69.6 |
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 |
teknium/OpenHermes-2.5-Mistral-7B | 61.52 | 64.93 | 84.18 | 63.64 | 52.24 | 78.06 | 26.08 |
NeverSleep/Noromaid-7B-0.4-DPO | 59.08 | 62.29 | 84.32 | 63.2 | 42.28 | 76.95 | 25.47 |
Yet Another LLM Leaderboard benchmarks
Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
---|---|---|---|---|---|
WestMaid_HermesMonarchv0.1 | 45.34 | 76.33 | 61.99 | 46.02 | 57.42 |
Misc. Benchmarks
MT-Bench | EQ-Bench v2.1 | |
---|---|---|
giraffe176/WestMaid_HermesMonarchv0.1 | 8.021875 | 77.19 (3 Shot, ooba) |
AlphaMonarch-7B | 7.928125 | 76.08 |
senseable/WestLake-7B-v2 | 78.7 | |
teknium/OpenHermes-2.5-Mistral-7B | 66.89 | |
claude-v1 | 7.900000 | 76.83 |
gpt-3.5-turbo | 7.943750 | 71.74 |
(Paper) | (Paper) Leaderboard |
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Evaluation results
- self-reported on EQ-BenchEQ-Bench v2.177.190
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard70.220
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard87.420
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.310
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard61.990
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.160
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard69.600