File size: 2,510 Bytes
91ff6d5 5f5fa86 91ff6d5 5f5fa86 3e7d289 5f5fa86 9ae8f7b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
license: cc-by-nc-4.0
tags:
- GGUF
- iMat
- llama3
---
```
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-iMat-GGUF
Quantized from fp16 with love.
* Weighted quantizations were calculated using groups_merged.txt with 105 chunks (recommended amount for this file) and n_ctx=512. Special thanks to jukofyork for sharing [this process](https://huggingface.co/jukofyork/WizardLM-2-8x22B-imatrix)
For a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747)
<b>All quants are verified working prior to uploading to repo for your safety and convenience. </b>
Please note importance matrix quantizations are a work in progress. IQ4 and above is recommended for best results.
Original model card [here](https://huggingface.co/Envoid/Llama-3-8B-Instruct-DADA/edit/main/README.md) and 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](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)
Training was done using [qlora-pipe](https://github.com/tdrussell/qlora-pipe) |