Name: Qwen2.5-14B-Vimarckoso-v3
Availability: InStock

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Vimarckoso is a reasoning-focused part of the Lamarck project. It began with a recipe based on Wernicke, and then I set out to boost instruction following without any great loss to reasoning. The results surpassed my expectations.

As of this writing, with open-llm-leaderboard catching up on rankings, Vimarckoso v3 should join Arcee AI's Virtuoso-Small, Sthenno's miscii-14b-1225 and Cultrix's Qwen2.5-14B-Brocav3 at the top of the 14B parameter text generation LLM category on this site. As the recipe below will show, their models are strong contributors to Vimarckoso. Congratulations to everyone whose work went into this!

Wernicke and Vimarckoso both inherit very strong reasoning, and hence high GPQA and MUSR scores, from EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2. Prose quality gets a boost from models blended in Qwenvergence-14B-v6-Prose, and instruction following gets healed after the merges thanks to LoRAs based on huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2.

Thank you, @mradermacher, @Sangto, and @MaziyarPanahi for the GGUFs. Anyone who needs to use them with Ollama can use the same modelfile as any Qwen2.5 14B Instruct model. I recommend a temperature of 0.8.

Configuration

The following YAML configuration was used to produce this model:

yaml

name:                Qwenvergence-14B-v6-Prose-model_stock
merge_method:        model_stock
base_model:          Qwen/Qwen2.5-14B
tokenizer_source:    huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2
parameters:
  int8_mask:         true
  normalize:         true
  rescale:           false
models:
  - model:           arcee-ai/Virtuoso-Small
  - model:           sometimesanotion/Lamarck-14B-v0.3
  - model:           EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
  - model:           allura-org/TQ2.5-14B-Sugarquill-v1
  - model:           oxyapi/oxy-1-small
  - model:           v000000/Qwen2.5-Lumen-14B
  - model:           sthenno-com/miscii-14b-1225
  - model:           sthenno-com/miscii-14b-1225
  - model:           underwoods/medius-erebus-magnum-14b
  - model:           huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2
dtype:               float32
out_dtype:           bfloat16
---
# Nifty TIES to allow a series of LoRA exchange among the above models
---
name:                Qwenvergence-14B-v6-Prose
merge_method:        ties
base_model:          Qwen/Qwen2.5-14B
tokenizer_source:    base
parameters:         
  density:           1.00
  weight:            1.00
  int8_mask:         true
  normalize:         true
  rescale:           false
dtype:               float32
out_dtype:           bfloat16
models:
  - model:           sometimesanotion/Qwenvergence-14B-v6-Prose-slerp
    parameters:
      density:       1.00
      weight:        1.00
---
# The last stable version of the Qwentinuum project which used successive breadcrumbs and SLERP merges to boost IFEval, merged back into Qwenvergence 
name:                Qwentinuum-14B-v6-Prose-slerp
merge_method:        slerp
base_model:          sometimesanotion/Qwenvergence-14B-v6-Prose
tokenizer_source:    sometimesanotion/Qwenvergence-14B-v6-Prose
dtype:               bfloat16
out_dtype:           bfloat16
parameters:         
  int8_mask:         true
  normalize:         true
  rescale:           false
parameters:
  t:
    - value:         0.40
slices:
  - sources:
      - model:       sometimesanotion/Qwenvergence-14B-v6-Prose
        layer_range: [ 0, 8 ]
      - model:       sometimesanotion/Qwentinuum-14B-v6
        layer_range: [ 0, 8 ]
  - sources:
      - model:       sometimesanotion/Qwenvergence-14B-v6-Prose
        layer_range: [ 8, 16 ]
      - model:       sometimesanotion/Qwentinuum-14B-v6
        layer_range: [ 8, 16 ]
  - sources:
      - model:       sometimesanotion/Qwenvergence-14B-v6-Prose
        layer_range: [ 16, 24 ]
      - model:       sometimesanotion/Qwentinuum-14B-v6
        layer_range: [ 16, 24 ]
  - sources:
      - model:       sometimesanotion/Qwenvergence-14B-v6-Prose
        layer_range: [ 24, 32 ]
      - model:       sometimesanotion/Qwentinuum-14B-v6
        layer_range: [ 24, 32 ]
  - sources:
      - model:       sometimesanotion/Qwenvergence-14B-v6-Prose
        layer_range: [ 32, 40 ]
      - model:       sometimesanotion/Qwentinuum-14B-v6
        layer_range: [ 32, 40 ]
  - sources:
      - model:       sometimesanotion/Qwenvergence-14B-v6-Prose
        layer_range: [ 40, 48 ]
      - model:       sometimesanotion/Qwentinuum-14B-v6
        layer_range: [ 40, 48 ]

---
name:                Qwen2.5-14B-Vimarckoso-v3-model_stock
merge_method:        model_stock
base_model:          sometimesanotion/Base-Qwenvergence
tokenizer_source:    sometimesanotion/Abliterate-Qwenvergence
dtype:               bfloat16
out_dtype:           bfloat16
parameters:
  int8_mask:         true
  normalize:         true
  rescale:           false
# With this many models, it's good to pre-merge some LoRAs from Abliterate-Qwenvergence, with their ranks indicated in the suffixes.
models:
  - model:           EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2-qv512
  - model:           arcee-ai/Virtuoso-Small-qv128
  - model:           v000000/Qwen2.5-Lumen-14B-qv256
  - model:           VAGOsolutions/SauerkrautLM-v2-14b-DPO-qv256
  - model:           rombodawg/Rombos-LLM-V2.6-Qwen-14b
  - model:           sometimesanotion/Qwentinuum-14B-v013
  - model:           sometimesanotion/Abliterate-Qwenvergence
---
name:                Qwen2.5-14B-Vimarckoso-v3-slerp
merge_method:        slerp
base_model:          sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-model_stock
tokenizer_source:    base
dtype:               float32
out_dtype:           bfloat16
parameters:
  t:
    - value:         0.20
slices:
  - sources:
      - model:       sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-model_stock
        layer_range: [ 0, 48 ]
      - model:       sometimesanotion/Qwentinuum-14B-v6-Prose+sometimesanotion/Qwenvergence-Abliterate-256
        layer_range: [ 0, 48 ]