Notes

Qwenvergence is a component of the Lamarck project, which iteratively merges a model_stock alongside its previous version as a first step to a complex merge strategy.

Some of the models have pre-applied LoRAs. In this case, a rank 128 adapter from Lamarck 0.7 was used to prevent sharp regressions in its performance.

I attribute this model's record-breaking MATH score of 44.18%, for a 14B model on the Open LLM Leaderboard, to its combination of Krystalan/DRT-o1-14B and huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated. These are strong models individually, but this is an area of synergy when they are merged.

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-v10
merge_method:        model_stock
base_model:          sometimesanotion/Qwenvergence-14B-v9
tokenizer_source:    base
dtype:               float32
out_dtype:           bfloat16
parameters:
  int8_mask:         true
  normalize:         true
  rescale:           false
models:
  - model:           sometimesanotion/Lamarck-14B-v0.7
  - model:           sometimesanotion/Qwenvergence-14B-v3-Prose+sometimesanotion/LoRA-la128
  - model:           huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated
  - model:           sometimesanotion/Lamarck-14B-v0.3+sometimesanotion/LoRA-la128
  - model:           huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated
  - model:           Krystalan/DRT-o1-14B
  - model:           sometimesanotion/Qwenvergence-14B-v9+sometimesanotion/LoRA-la128
  - model:           sometimesanotion/Qwenvergence-14B-v3-Prose+sometimesanotion/LoRA-la128
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