Exotic Frankenmerges 🥨
Collection
Merges of models of different architectures and sizes that end up working surprisingly well
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CarbonBeagle-11B
An experiment in merging models of different architectures and sizes. Here are the steps:
- Upscale mlabonne/NeuralBeagle14-7B to vicgalle/franken-Beagle-11B.
- DPO-tune vicgalle/franken-Beagle-11B to vicgalle/NeuralBeagle-11B.
- Merge vicgalle/NeuralBeagle-11B and jeonsworld/CarbonVillain-en-10.7B-v4.
Merge Details
Merge Method
This model was merged using the linear merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: jeonsworld/CarbonVillain-en-10.7B-v4
parameters:
weight: 1.0
- model: vicgalle/NeuralBeagle-11B
parameters:
weight: 0.5
merge_method: linear
dtype: float16
Evaluations
At the time of its creation (21-01-2024), it is the best model in the Open LLM Leaderboard for its size class (10.7B-11B), and also 13B models:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 74.64 |
| AI2 Reasoning Challenge (25-Shot) | 71.84 |
| HellaSwag (10-Shot) | 88.93 |
| MMLU (5-Shot) | 66.62 |
| TruthfulQA (0-shot) | 69.43 |
| Winogrande (5-shot) | 84.06 |
| GSM8k (5-shot) | 66.94 |
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Merge of
Collection including vicgalle/CarbonBeagle-11B
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard71.840
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.930
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.620
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard69.430
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.060
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard66.940