base_model:
- Nexesenex/Llama_3.x_70b_Smarteaz_0.2_R1
- Nexesenex/Llama_3.x_70b_Smarteaz_0.2_NMT
- Nexesenex/Llama_3.x_70b_Smarteaz_0.1
library_name: transformers
tags:
- mergekit
- merge
license: llama3.3
about
The Teaz series is my third attempt at making merges, this time on L3.x 70b, after the L3.2 3b Kostume and Kermes series.
This time, the goal was to make a smart model with a low perplexity, in accordance to the principles of the Kermes series, but with a merge of 3 merged models like on the kostume series.
Huihui's abliterated models were used:
- Llama 3.3 70b as the pivot of the first/main model.
- Nemotron 3.1 70b and Deepseek R1 Distill 70b as the pillars.
- and Tulu 3 70b as the backer of the 2nd and 3rd models.
Bingo again. I hit 3.45 ppl512 wikieng, 62+ or ARC-C, and 82+ on ARC-E. Absolute top of the class for L3.x 70b, like Kermes is for L3 3.2 3b.
No cheating, no contaminating, just the wonderful MergeKit model-stock merge technique leveraged to a new level (compared to what I already saw being done, at least).
Next projects will involve that model as the "smarts pillar" of further merges, aimed at any use case.
credits
Kudos go to the model authors, and to the Arcee / MergeKit folks, as well as to HF hosting the MergeKit App. Also a big-up to SteelSkull, observing him cooking Nevoria decided me to try to make some merges by myself.
historic
First : On the Kostume series started on the 11/02/0205 I tried to make a triple stock merge of 3 intermediary stock merges of a dozen of model or so. This, to see if I could pile up their abilities.
- Not bad, but nothing special about it, it's a bit hard for me to judge at 3b.
Second : On the Kermes series started the day after, I defined a simpler approach:
Perplexity is the main constraint. Usual L3.2 3b finetunes are around 10.5-11 ppl512wikieng, Hermes is around 9.5.
I also measure in French and Serbian to observe the variances.
Arc Challenge and Easy are the second constraint to judge its basic logics.
Usual L3.2 3b finetunes hit 40 and 60-65 respectively, Hermes3 hits 47+ and 70+.
Lack of censorship. I always keep in mind to pick models compatible with that AMAP.
This, may it be through the picked models' abliteration or the datasets they use.
And of course, the test, both In Kobold/Croco.CPP (spamming very offensive requests), and in ST (a 10k prompt with a big lorebook).
Kermes series are basically stock merges on the top of anothers.
- The goal was to maintain as much the qualities of the models used, so I stay on 1+2 models for the first merge, and 1+2 for the second as well.
And bingo. Perplexity goes down still, ARC remain stable, it's quite unhinged still, and.. quite coherent, event at 10k+ context.
quantizations
GGUF static quantizations (Thanks Mradermacher!) :
https://huggingface.co./mradermacher/Llama_3.x_70b_Smarteaz_V1-GGUF
GGUF iMatrix quantizations (Thanks Mradermacher!) :
https://huggingface.co./mradermacher/Llama_3.x_70b_Smarteaz_V1-i1-GGUF
merge
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Model Stock merge method using Nexesenex/Llama_3.x_70b_Smarteaz_0.1 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
merge_method: model_stock
models:
- model: Nexesenex/Llama_3.x_70b_Smarteaz_0.2_NMT
parameters:
weight: 1.0
- model: Nexesenex/Llama_3.x_70b_Smarteaz_0.2_R1
parameters:
weight: 1.0
base_model: Nexesenex/Llama_3.x_70b_Smarteaz_0.1
dtype: bfloat16
normalize: true