--- license: cc-by-nc-4.0 --- ``` e88 88e d8 d888 888b 8888 8888 ,"Y88b 888 8e d88 C8888 8888D 8888 8888 "8" 888 888 88b d88888 Y888 888P Y888 888P ,ee 888 888 888 888 "88 88" "88 88" "88 888 888 888 888 b 8b, e88'Y88 d8 888 d888 'Y ,"Y88b 888,8, d88 ,e e, 888 C8888 "8" 888 888 " d88888 d88 88b 888 Y888 ,d ,ee 888 888 888 888 , 888 "88,d88 "88 888 888 888 "YeeP" 888 PROUDLY PRESENTS ``` # Llama-3-8B-Instruct-DADA-exl2-rpcal Quantized using 200 samples of 8192 tokens from an RP-oriented [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) dataset. Branches: - `main` -- `measurement.json` - `8b8h` -- 8bpw, 8bit lm_head - `6b6h` -- 6bpw, 6bit lm_head - `4b6h` -- 4bpw, 6bit lm_head Original model link: [Envoid/Llama-3-8B-Instruct-DADA](https://huggingface.co/Envoid/Llama-3-8B-Instruct-DADA) Original model README below. ----- ## Llama-3-8B-Instruct-DADA ![](https://files.catbox.moe/oyqv9v.jpg) # Warning: This model is experimental and thus potentially unpredictable. This model employs the same strategy as [Mixtral Instruct ITR DADA](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-DADA-8x7B) I trained [Llama-3-8B-Instruct](meta-llama/Meta-Llama-3-8B-Instruct) on the Alpaca-DADA dataset for 10 epochs at 1e-6 learning rate. I then did a 50/50 SLERP merge of the resulting model back onto Llama-3-8B-Instruct This model may require custom stopping strings to tame due to current issues surrounding Llama-3 EOS tokens and various back-ends. It certainly gives some interesting answers using an assistant template/card in SillyTavern, though. The below answer is one of the more interesting answers I've gotten out of an LLM on the same query, although there was an indentiation error (indicated by the red circle) ![](https://files.catbox.moe/mvao98.png) Training was done using [qlora-pipe](https://github.com/tdrussell/qlora-pipe) [GGUFs care of Quant Cartel](https://huggingface.co/Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF)