merge
OI, if this merge was a game, it would be as good as CONCORD was.
that really was the biggest insult I could think of, which says some about me, but enough about this merge.
Simply put, don't bother with this one. It was a gamble, and it didn't pan out.
Merge Details
Merge Method
This model was merged using the TIES merge method using NousResearch/Meta-Llama-3-8B + grimjim/Llama-3-Instruct-abliteration-LoRA-8B as a base.
Models Merged
The following models were included in the merge:
- DreadPoor/Aurora_faustus-8B-LINEAR
- DreadPoor/Aspire1.2-8B-TIES
- DreadPoor/Heart_Stolen-8B-Model_Stock
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- layer_range: [0, 32]
model: DreadPoor/Heart_Stolen-8B-Model_Stock
parameters:
weight: 1
- layer_range: [0, 32]
model: DreadPoor/Aurora_faustus-8B-LINEAR
parameters:
weight: 1
- layer_range: [0, 32]
model: DreadPoor/Aspire1.2-8B-TIES
parameters:
weight: 1
- layer_range: [0, 32]
model: NousResearch/Meta-Llama-3-8B+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
parameters:
density: 1
base_model: NousResearch/Meta-Llama-3-8B+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
merge_method: ties
dtype: bfloat16
parameters:
normalize: true
int8_mask: true
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 24.73 |
IFEval (0-Shot) | 47.76 |
BBH (3-Shot) | 34.52 |
MATH Lvl 5 (4-Shot) | 15.11 |
GPQA (0-shot) | 7.61 |
MuSR (0-shot) | 12.72 |
MMLU-PRO (5-shot) | 30.68 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard47.760
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard34.520
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard15.110
- acc_norm on GPQA (0-shot)Open LLM Leaderboard7.610
- acc_norm on MuSR (0-shot)Open LLM Leaderboard12.720
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard30.680