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---
license: cc-by-nc-4.0
tags:
- GGUF
- iMat
- llama3
---
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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/) 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) |