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@@ -20,17 +20,6 @@ roleplaying chat model intended to replicate the experience of 1-on-1 roleplay o
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  IRC/Discord-style RP (aka "Markdown format") is not supported yet. The model does not include instruction tuning,
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  only manually picked and slightly edited RP conversations with persona and scenario data.
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- ## Important notes on generation settings
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- It's recommended not to go overboard with low tail-free-sampling (TFS) values. From previous testing with Llama-2,
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- decreasing it too much appeared to easily yield rather repetitive responses. Extensive testing with Mistral has not
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- been performed yet, but suggested starting generation settings are:
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-
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- - TFS = 0.92~0.95
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- - Temperature = 0.70~0.85
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- - Repetition penalty = 1.05~1.10
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- - top-k = 0 (disabled)
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- - top-p = 1 (disabled)
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-
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  ## Prompt format
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  Same as before. It uses the [extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca),
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  with `### Input:` immediately preceding user inputs and `### Response:` immediately preceding
@@ -99,6 +88,16 @@ your desired response length:
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  ![settings](https://files.catbox.moe/6lcz0u.png)
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  ## Training procedure
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  [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
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  on a 2x NVidia A40 GPU cluster.
 
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  IRC/Discord-style RP (aka "Markdown format") is not supported yet. The model does not include instruction tuning,
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  only manually picked and slightly edited RP conversations with persona and scenario data.
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  ## Prompt format
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  Same as before. It uses the [extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca),
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  with `### Input:` immediately preceding user inputs and `### Response:` immediately preceding
 
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  ![settings](https://files.catbox.moe/6lcz0u.png)
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+ ## Text generation settings
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+ Extensive testing with Mistral has not been performed yet, but suggested starting text
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+ generation settings may be:
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+
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+ - TFS = 0.92~0.95
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+ - Temperature = 0.70~0.85
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+ - Repetition penalty = 1.05~1.10
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+ - top-k = 0 (disabled)
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+ - top-p = 1 (disabled)
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+
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  ## Training procedure
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  [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
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  on a 2x NVidia A40 GPU cluster.