---
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-EGO-iMat-GGUF


Quantized from fp32 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)

<b>**Note - Please use SillyTavern as well as the following prompt format:**</b>
```
[EGO]Name: Character name and then Everything that forms the personality and speech patterns.(i.e. scenario, sample dialogue, character definitions, etc)[/EGO]
[SEEN]User message.[/SEEN]
Character Name:
```


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>

It's highly recommended to stick to higher quants of this model due to the unique nature of its pseudotokens

Original model card [here](https://huggingface.co/Envoid/Llama-3-8B-EGO) and below

---

# This model isn't particularly great. It's just an undercooked experiment.

Releasing it anyways just in case it accidentally makes good merge meat. 

# It also has a tendency to produce mature content without warning. 

This model is tuned off of the base Llama-3-8B model. 

I adapted the leaked Undi dataset into training samples for custom formatting. This model pretty much only functions properly in SillyTavern. 

The formatting has two pairs of pseudotokens

```
[EGO]Name: Character name and then Everything that forms the personality and speech patterns.(i.e. scenario, sample dialogue, character definitions, etc)[/EGO]
[SEEN]User message.[/SEEN]
Character Name:
```

The self attention modules were fine tuned separately on this dataset and the pseudotokens were chosen because they made logical sense with respect to the character giving a reply without allowing the model to 'connect the dots' during training and figure out that it is indeed an AI language model.

After this was done all modules were then finetuned together on the dendrite dataset in order to connect the changes made to the attention modules.

So with regards to building a SillyTavern prompt template you basically want the entire story string and any additional stylistic instructions enclosed in the [EGO] tags and then the user messages enclosed in [SEEN] tags. 

It doesn't give particularly verbose replies unless you're continueing a roleplay with verbose messages. Otherwise it's pretty bad.