---
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         
```

## Dendrite-L3-10B-iMat-GGUF


Quantized from fp32 with love.
* Weighted quantizations were calculated with fp32 GGUF using groups_merged.txt in 96 chunks and n_ctx=512 using [this process](https://huggingface.co/jukofyork/WizardLM-2-8x22B-imatrix)

<b>Important Note - Quantized with llama.cpp release b2787, post [PR6920](https://github.com/ggerganov/llama.cpp/pull/6920). There may still be some remaining issues with the bpe tokenizer so consider these quantizations experimental for now. Feedback is encouraged.</b>


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 try higher quants (Q6 or above) of this model due to the unique nature of its pseudotokens.

Original model card [here](https://huggingface.co/Envoid/Dendrite-L3-10B) and below

---
# This model is experimental and thus results cannot be gauranteed. 

![](https://files.catbox.moe/rx5tfs.jpg)
# Dendrite-L3-10B

In a similar vein to [Libra-19B](https://huggingface.co/Envoid/Libra-19B) this model was created by taking all of the layers of one model and stacking along with them the first number of layers (8 in this case) from a donor model but in the reverse order. 

In this case the base model used was [Poppy_Porpoise-DADA-8B](https://huggingface.co/Envoid/Poppy_Porpoise-DADA-8B) and the donor model used was [Llama-3-8B-Instruct-DADA](https://huggingface.co/Envoid/Llama-3-8B-Instruct-DADA)

It was then finetuned for 10 epochs on the Dendrite dataset at a low learning rate to repair the disorder and integrate the donor layers. 

The following mergekit config was used:
```
slices:
  - sources:
    - model: ./Poppy_Porpoise-DADA-8B
      layer_range: [0, 32]
  - sources:
    - model: ./Llama-3-8B-Instruct-DADA
      layer_range: [7, 8]
  - sources:
    - model: ./Llama-3-8B-Instruct-DADA
      layer_range: [6, 7]
  - sources:
    - model: ./Llama-3-8B-Instruct-DADA
      layer_range: [5, 6]
  - sources:
    - model: ./Llama-3-8B-Instruct-DADA
      layer_range: [4, 5]
  - sources:
    - model: ./Llama-3-8B-Instruct-DADA
      layer_range: [3, 4]
  - sources:
    - model: ./Llama-3-8B-Instruct-DADA
      layer_range: [2, 3]
  - sources:
    - model: ./Llama-3-8B-Instruct-DADA
      layer_range: [1, 2]
  - sources:
    - model: ./Llama-3-8B-Instruct-DADA
      layer_range: [0, 1]
merge_method: passthrough
dtype: float16
```

Unlike in the case of Libra-19B this models moral alignment seems very much intact.

In order to get the best results from this model you should uncheck "skip special tokens" on your front-end and add "<|eot_id|>" to your custom stopping strings. 

It has been tested with a number of different Llama-3 prompt templates and seems to work well.

It regained its base assistant personality during the retraining process, however, using assistant style prompt templates and assistant cards in SillyTavern gives it fairly interesting replies.

It has been tested in RP, assistant and creative writing use cases and at a quick glance seems to work well. 

Training was done using [qlora-pipe](https://github.com/tdrussell/qlora-pipe)

exl2 RPCAL care of [Qaunt Cartel](https://huggingface.co/Quant-Cartel/Dendrite-L3-10B-exl2-rpcal)