Notes

For a model_stock merge, this has greatly exceeded my expectations. It beats Lamarck v0.7's average without introducing DeepSeek elements, mostly by scoring high on MATH without giving up much elsewhere. It also shows that the high-scoring Qwen2.5 14B merges are converging near the limits of the architecture. Here is how it benchmarks alongside the models it merges.

Merge Method

This model was merged using the Model Stock merge method using sometimesanotion/Qwenvergence-14B-v9 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

name:                Qwenvergence-14B-v11
merge_method:        model_stock
base_model:          sometimesanotion/Qwenvergence-14B-v9
tokenizer_source:    base
dtype:               bfloat16
out_dtype:           bfloat16
parameters:
  int8_mask:         true
  normalize:         true
  rescale:           false
models:
  - model:           sometimesanotion/Lamarck-14B-v0.6+sometimesanotion/LoRA-64-tempesthenno-ppo-ckpt40
  - model:           sometimesanotion/Qwenvergence-14B-v3-Prose+sometimesanotion/LoRA-64-tempesthenno-ppo-ckpt40
  - model:           sometimesanotion/Qwenvergence-14B-v9+sometimesanotion/LoRA-32-tempesthenno-ppo-ckpt40
  - model:           sometimesanotion/Lamarck-14B-v0.3+sometimesanotion/LoRA-64-tempesthenno-ppo-ckpt40
  - model:           sometimesanotion/Lamarck-14B-v0.6+sometimesanotion/LoRA-64-tempesthenno-ppo-ckpt40
  - model:           CultriX/Qwen2.5-14B-Hyperionv4
  - model:           Krystalan/DRT-o1-14B
  - model:           sthenno/tempesthenno-ppo-ckpt40