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OpenChat-3.5-0106_BlockExpansion-44Layers-End
This is NOT your usual frankenmerge created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method, but employing a variation of the Block Expansion method described in the paper LLaMA Pro: Progressive LLaMA with Block Expansion.
The authors of the paper added new layers interleaved in between the original layers of the model, setting the parameters of the o_proj and down_proj layers to zero. This effectively adds layers that will just output their input (as if they were "transparent") allowing the model to remain functional even without further training. These new layers can then be targeted during training or fine-tuning without risking catastrophic forgetting, if you follow the author's training method to freeze the original layers and only train the new layers.
I used the same method but added the new layers to the end of the model. My rationale is that the level of abstraction increases with each layer of the model. So, while new layers spread along the original layers will help the model to learn new tasks, adding layers to the end of the model and then re-training/fine-tuning the model on tasks it already performs well could improve the models understanding of those task and perform them better by employing more complex reasoning.
This model has not yet received additional training, so it should perform close to the original model.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [0, 32]
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [31, 32]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
merge_method: passthrough
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 22.55 |
IFEval (0-Shot) | 59.61 |
BBH (3-Shot) | 24.06 |
MATH Lvl 5 (4-Shot) | 6.80 |
GPQA (0-shot) | 7.61 |
MuSR (0-shot) | 11.78 |
MMLU-PRO (5-shot) | 25.44 |
Citation
@misc{wu2024llamaproprogressivellama,
title={LLaMA Pro: Progressive LLaMA with Block Expansion},
author={Chengyue Wu and Yukang Gan and Yixiao Ge and Zeyu Lu and Jiahao Wang and Ye Feng and Ying Shan and Ping Luo},
year={2024},
eprint={2401.02415},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2401.02415},
}
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard59.610
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard24.060
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard6.800
- acc_norm on GPQA (0-shot)Open LLM Leaderboard7.610
- acc_norm on MuSR (0-shot)Open LLM Leaderboard11.780
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard25.440