Llama-3-8B-Instruct-DADA-exl2-rpcal

  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 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

Original model README below.

---

Llama-3-8B-Instruct-DADA

Warning: This model is experimental and thus potentially unpredictable.

This model employs the same strategy as Mixtral Instruct ITR DADA

I trained 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)

Training was done using qlora-pipe

GGUFs care of Quant Cartel