merge
This is a testing model using the zeroing method used by elinas/Llama-3-15B-Instruct-zeroed.
If this model pans out in the way I hope, Ill heal it then reupload with a custom model card like the others. currently this is just an experiment.
In case anyone asks AbL3In-15b literally means:
Ab = Abliterated
L3 = Llama-3
In = Instruct
15b = its 15b perameters
GGUF's
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 24]
model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
- sources:
- layer_range: [8, 24]
model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 24]
model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 32]
model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 67.46 |
AI2 Reasoning Challenge (25-Shot) | 61.77 |
HellaSwag (10-Shot) | 78.42 |
MMLU (5-Shot) | 66.57 |
TruthfulQA (0-shot) | 52.53 |
Winogrande (5-shot) | 74.74 |
GSM8k (5-shot) | 70.74 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard61.770
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard78.420
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.570
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard52.530
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard74.740
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.740