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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

Important Note - Quantized with llama.cpp release b2787, post PR6920. There may still be some remaining issues with the bpe tokenizer so consider these quantizations experimental for now. Feedback is encouraged.

For a brief rundown of iMatrix quant performance please see this PR

All quants are verified working prior to uploading to repo for your safety and convenience.

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 and below

This model is experimental and thus results cannot be gauranteed.

Dendrite-L3-10B

In a similar vein to 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 and the donor model used was 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

exl2 RPCAL care of Qaunt Cartel