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README.md
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---
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base_model:
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- meta-llama/Meta-Llama-3.1-8B-Instruct
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- grimjim/Llama-3-Instruct-abliteration-LoRA-8B
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library_name: transformers
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tags:
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- mergekit
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- merge
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license: llama3.1
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pipeline_tag: text-generation
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---
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# QuantFactory/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF
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This is quantized version of [grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter](https://huggingface.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter) created using llama.cpp
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# Original Model Card
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# Llama-3.1-8B-Instruct-abliterated_via_adapter
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This model is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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A LoRA was applied to "abliterate" refusals in [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). The result appears to work despite the LoRA having been derived from Llama 3 instead of Llama 3.1, which implies that there is significant feature commonality between the 3 and 3.1 models.
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The LoRA was extracted from [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
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Built with Llama.
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## Merge Details
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### Merge Method
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This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) + [grimjim/Llama-3-Instruct-abliteration-LoRA-8B](https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-LoRA-8B) as a base.
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
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dtype: bfloat16
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merge_method: task_arithmetic
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parameters:
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normalize: false
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slices:
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- sources:
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- layer_range: [0, 32]
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model: meta-llama/Meta-Llama-3.1-8B-Instruct+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
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parameters:
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weight: 1.0
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```
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