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---
license: llama3
library_name: transformers
tags:
- mergekit
- merge
base_model:
- failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
model-index:
- name: AbL3In-15B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 61.77
name: normalized accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 78.42
name: normalized accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.57
name: accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.53
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 74.74
name: accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 70.74
name: accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=TheSkullery/AbL3In-15B
name: Open LLM Leaderboard
---
# merge
This is a testing model using the zeroing method used by [elinas/Llama-3-15B-Instruct-zeroed](https://huggingface.co./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:
```yaml
Ab = Abliterated
L3 = Llama-3
In = Instruct
15b = its 15b perameters
```
## GGUF's
[GGUF by @Mradermacher](https://huggingface.co./mradermacher/AbL3In-15B-GGUF)
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co./failspy/Meta-Llama-3-8B-Instruct-abliterated-v3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
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](https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_TheSkullery__AbL3In-15B)
| 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|
|