This is a quant of merge of pre-trained language models created using
mergekit.
Exllamav3 was used to create a quant at 4bpw with h6. With 16GB VRAM, it's possible to run 16K context at fp16 with some room to spare.
The model vision component was excised from all merge contributions.
Creative text generation outputs seem to trend toward the short side, sometimes to the point of feeling choppy, hence the model name. This model is not the most stellar, but the result is interesting, going against the individual tendency of the two contributing models toward longer outputs.
This model was merged using the
Task Arithmetic merge method using
mrfakename/mistral-small-3.1-24b-base-2503-hf as a base.
1base_model: mrfakename/mistral-small-3.1-24b-base-2503-hf
2dtype: bfloat16
3merge_method: task_arithmetic
4parameters:
5 normalize: true
6models:
7 - model: mrfakename/mistral-small-3.1-24b-base-2503-hf
8 - model: Doctor-Shotgun/MS3.2-24B-Magnum-Diamond
9 parameters:
10 weight: 0.5
11 - model: PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
12 parameters:
13 weight: 0.5
14