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README.md
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---
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base_model: []
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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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- llama 3
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- 70b
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- arimas
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- story
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- roleplay
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- rp
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---
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# EXL2 quants of [ryzen88/Llama-3-70b-Arimas-story-RP-V1.6](https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1.6)
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[3.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.0bpw-h6-exl2)
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[3.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.5bpw-h6-exl2)
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[4.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.0bpw-h6-exl2)
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[4.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.5bpw-h6-exl2)
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[6.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-6.0bpw-h6-exl2)
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[8.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-8.0bpw-h8-exl2)
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Created using the defaults from exllamav2 1.4.0 convert.py
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3.0bpw to 6.0bpw head bits = 6
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8.0bpw head bits = 8
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length = 8192
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dataset rows = 200
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measurement rows = 32
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measurement length = 8192
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# model
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Llama-3-70b-Arimas-story-RP-V1.6
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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I Greatly expanded the amount of models used in this merge, experimented a lot with different idea's.
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This version feels a lot more convincing than V1.5 Hopefully the long context window will also remain strong after Quants.
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Because of the many merges switched back from BFloat to Float.
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Tried breadcrums without the Ties, that went very poorly.
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### Merge Method
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This model was merged using the breadcrumbs_ties merge method using I:\Llama-3-70B-Instruct-Gradient-262k as a base.
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### Models Merged
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The following models were included in the merge:
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* \Smaug-Llama-3-70B-Instruct
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* \Meta-LLama-3-Cat-Smaug-LLama-70b
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* \Meta-LLama-3-Cat-A-LLama-70b
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* \Llama-3-70B-Synthia-v3.5
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* \Llama-3-70B-Instruct-Gradient-524k
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* \Llama-3-70B-Instruct-Gradient-262k
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* \Tess-2.0-Llama-3-70B-v0.2
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* \Llama-3-Lumimaid-70B-v0.1-alt
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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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models:
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- model: \Llama-3-70B-Instruct-Gradient-262k
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parameters:
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weight: 0.25
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density: 0.90
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gamma: 0.01
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- model: \Meta-LLama-3-Cat-Smaug-LLama-70b
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parameters:
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weight: 0.28
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density: 0.90
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gamma: 0.01
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- model: \Llama-3-Lumimaid-70B-v0.1-alt
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parameters:
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weight: 0.15
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density: 0.90
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gamma: 0.01
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- model: \Tess-2.0-Llama-3-70B-v0.2
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parameters:
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weight: 0.06
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density: 0.90
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gamma: 0.01
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- model: \Smaug-Llama-3-70B-Instruct
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parameters:
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weight: 0.04
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density: 0.90
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gamma: 0.01
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- model: \Llama-3-70B-Synthia-v3.5
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parameters:
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weight: 0.05
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density: 0.90
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gamma: 0.01
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- model: \Llama-3-70B-Instruct-Gradient-524k
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parameters:
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weight: 0.03
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density: 0.90
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gamma: 0.01
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- model: \Meta-LLama-3-Cat-A-LLama-70b
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parameters:
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weight: 0.14
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density: 0.90
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gamma: 0.01
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merge_method: breadcrumbs_ties
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base_model: I:\Llama-3-70B-Instruct-Gradient-262k
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dtype: float16
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```
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