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---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
base_model:
- arcee-ai/Virtuoso-Small
- CultriX/SeQwence-14B-EvolMerge
- CultriX/Qwen2.5-14B-Wernicke
- sthenno-com/miscii-14b-1028
- underwoods/medius-erebus-magnum-14b
- sometimesanotion/lamarck-14b-prose-model_stock
- sometimesanotion/lamarck-14b-reason-model_stock
metrics:
- accuracy
pipeline_tag: text-generation
model-index:
- name: Lamarck-14B-v0.3
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 50.32
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 51.27
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 32.4
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 18.46
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 18.0
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.01
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3
name: Open LLM Leaderboard
---
---
# merge
Lamarck-14B is a carefully designed merge which emphasizes [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) in early and finishing layers, and midway features strong influence on reasoning and prose from [CultriX/SeQwence-14B-EvolMerge](http://huggingface.co/CultriX/SeQwence-14B-EvolMerge) especially, but a number of other models as well through its model_stock.
Version 0.3 is the product of a carefully planned and tested sequence of templated merges, produced by a toolchain which wraps around Arcee's mergekit.
For GGUFs, [mradermacher/Lamarck-14B-v0.3-i1-GGUF](https://huggingface.co/mradermacher/Lamarck-14B-v0.3-i1-GGUF) has you covered. Thank you @mradermacher!
**The merge strategy of Lamarck 0.3 can be summarized as:**
- Two model_stocks commence specialized branches for reasoning and prose quality.
- For refinement on both model_stocks, DELLA merges re-emphasize selected ancestors.
- For smooth instruction following, a SLERP merges Virtuoso with a DELLA merge of the two branches, where reason vs. prose quality are balanced.
- For finalization and normalization, a TIES merge.
The first two layers come entirely from Virtuoso. The choice to leave these layers untouched comes from [arxiv.org/abs/2307.03172](https://arxiv.org/abs/2307.03172) which identifies early attention glitches as a chief cause of hallucinations. Layers 3-8 feature a SLERP gradient into introducing the DELLA merge tree in which the reason branch is emphasized, the prose branch only given a small ranking.
### Thanks go to:
- @arcee-ai's team for the ever-capable mergekit, and the exceptional Virtuoso Small model.
- @CultriX for the helpful examples of memory-efficient sliced merges and evolutionary merging. Their contribution of tinyevals on version 0.1 of Lamarck did much to validate the hypotheses of the DELLA->SLERP gradient process used here.
- The authors behind the capable models that appear in the model_stock.
### Models Merged
**Top influences:** These ancestors are base models and present in the model_stocks, but are heavily re-emphasized in the DELLA and SLERP merges.
- **[arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small)** - A brand new model from Arcee, refined from the notable cross-architecture Llama-to-Qwen distillation [arcee-ai/SuperNova-Medius](https://huggingface.co/arcee-ai/SuperNova-Medius). The first two layers are nearly exclusively from Virtuoso. It has proven to be a well-rounded performer, and contributes a noticeable boost to the model's prose quality.
- **[CultriX/SeQwence-14B-EvolMerge](http://huggingface.co/CultriX/SeQwence-14B-EvolMerge)** - A top contender on reasoning benchmarks.
**Reason:** While Virtuoso is the strongest influence the starting ending layers, the reasoning mo
- **[CultriX/Qwen2.5-14B-Wernicke](http://huggingface.co/CultriX/Qwen2.5-14B-Wernicke)** - A top performer for Arc and GPQA, Wernicke is re-emphasized in small but highly-ranked portions of the model.
- **[VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO)** - This model's influence is understated, but aids BBH and coding capability.
**Prose:** While the prose module is gently applied, its impact is noticeable on Lamarck 0.3's prose quality, and a DELLA merge re-emphasizes the contributions of two models particularly:
- **[sthenno-com/miscii-14b-1028](https://huggingface.co/sthenno-com/miscii-14b-1028)**
- **[underwoods/medius-erebus-magnum-14b](https://huggingface.co/underwoods/medius-erebus-magnum-14b)**
**Model stock:** Two model_stock merges, specialized for specific aspects of performance, are used to mildly influence a large range of the model.
- **[sometimesanotion/lamarck-14b-reason-model_stock](https://huggingface.co/sometimesanotion/lamarck-14b-reason-model_stock)**
- **[sometimesanotion/lamarck-14b-prose-model_stock](https://huggingface.co/sometimesanotion/lamarck-14b-prose-model_stock)** - This brings in a little influence from [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2), [oxyapi/oxy-1-small](https://huggingface.co/oxyapi/oxy-1-small), and [allura-org/TQ2.5-14B-Sugarquill-v1](https://huggingface.co/allura-org/TQ2.5-14B-Sugarquill-v1).
**Note on abliteration:** This author believes that adjacent services and not language models themselves are where guardrails are best placed. Effort to de-censor Lamarck will resume after the model has been further studied.
### Configuration
The following YAML configuration was used to produce this model:
```yaml
name: lamarck-14b-reason-della # This contributes the knowledge and reasoning pool, later to be merged
merge_method: della # with the dominant instruction-following model
base_model: arcee-ai/Virtuoso-Small
tokenizer_source: arcee-ai/Virtuoso-Small
parameters:
int8_mask: false
normalize: true
rescale: false
density: 0.30
weight: 0.50
epsilon: 0.08
lambda: 1.00
models:
- model: CultriX/SeQwence-14B-EvolMerge
parameters:
density: 0.70
weight: 0.90
- model: sometimesanotion/lamarck-14b-reason-model_stock
parameters:
density: 0.90
weight: 0.60
- model: CultriX/Qwen2.5-14B-Wernicke
parameters:
density: 0.20
weight: 0.30
dtype: bfloat16
out_dtype: bfloat16
---
name: lamarck-14b-prose-della # This contributes the prose, later to be merged
merge_method: della # with the dominant instruction-following model
base_model: arcee-ai/Virtuoso-Small
tokenizer_source: arcee-ai/Virtuoso-Small
parameters:
int8_mask: false
normalize: true
rescale: false
density: 0.30
weight: 0.50
epsilon: 0.08
lambda: 0.95
models:
- model: sthenno-com/miscii-14b-1028
parameters:
density: 0.40
weight: 0.90
- model: sometimesanotion/lamarck-14b-prose-model_stock
parameters:
density: 0.60
weight: 0.70
- model: underwoods/medius-erebus-magnum-14b
dtype: bfloat16
out_dtype: bfloat16
---
name: lamarck-14b-converge-della # This is the strongest control point to quickly
merge_method: della # re-balance reasoning vs. prose
base_model: arcee-ai/Virtuoso-Small
tokenizer_source: arcee-ai/Virtuoso-Small
parameters:
int8_mask: false
normalize: true
rescale: false
density: 0.30
weight: 0.50
epsilon: 0.08
lambda: 1.00
models:
- model: sometimesanotion/lamarck-14b-reason-della
parameters:
density: 0.80
weight: 1.00
- model: arcee-ai/Virtuoso-Small
parameters:
density: 0.40
weight: 0.50
- model: sometimesanotion/lamarck-14b-prose-della
parameters:
density: 0.10
weight: 0.40
dtype: bfloat16
out_dtype: bfloat16
---
name: lamarck-14b-converge # Virtuoso has good capabilities all-around; it is 100% of the first
merge_method: slerp # two layers, and blends into the reasoning+prose convergance
base_model: arcee-ai/Virtuoso-Small # for some interesting boosts
tokenizer_source: base
parameters:
t: [ 0.00, 0.60, 0.80, 0.80, 0.80, 0.70, 0.40 ]
slices:
- sources:
- layer_range: [ 0, 2 ]
model: arcee-ai/Virtuoso-Small
- layer_range: [ 0, 2 ]
model: merges/lamarck-14b-converge-della
t: [ 0.00, 0.00 ]
- sources:
- layer_range: [ 2, 8 ]
model: arcee-ai/Virtuoso-Small
- layer_range: [ 2, 8 ]
model: merges/lamarck-14b-converge-della
t: [ 0.00, 0.60 ]
- sources:
- layer_range: [ 8, 16 ]
model: arcee-ai/Virtuoso-Small
- layer_range: [ 8, 16 ]
model: merges/lamarck-14b-converge-della
t: [ 0.60, 0.70 ]
- sources:
- layer_range: [ 16, 24 ]
model: arcee-ai/Virtuoso-Small
- layer_range: [ 16, 24 ]
model: merges/lamarck-14b-converge-della
t: [ 0.70, 0.70 ]
- sources:
- layer_range: [ 24, 32 ]
model: arcee-ai/Virtuoso-Small
- layer_range: [ 24, 32 ]
model: merges/lamarck-14b-converge-della
t: [ 0.70, 0.70 ]
- sources:
- layer_range: [ 32, 40 ]
model: arcee-ai/Virtuoso-Small
- layer_range: [ 32, 40 ]
model: merges/lamarck-14b-converge-della
t: [ 0.70, 0.60 ]
- sources:
- layer_range: [ 40, 48 ]
model: arcee-ai/Virtuoso-Small
- layer_range: [ 40, 48 ]
model: merges/lamarck-14b-converge-della
t: [ 0.60, 0.40 ]
dtype: bfloat16
out_dtype: bfloat16
---
name: lamarck-14b-finalize
merge_method: ties
base_model: Qwen/Qwen2.5-14B
tokenizer_source: Qwen/Qwen2.5-14B-Instruct
parameters:
int8_mask: false
normalize: true
rescale: false
density: 1.00
weight: 1.00
models:
- model: merges/lamarck-14b-converge
dtype: bfloat16
out_dtype: bfloat16
---
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sometimesanotion__Lamarck-14B-v0.3)
| Metric |Value|
|-------------------|----:|
|Avg. |36.58|
|IFEval (0-Shot) |50.32|
|BBH (3-Shot) |51.27|
|MATH Lvl 5 (4-Shot)|32.40|
|GPQA (0-shot) |18.46|
|MuSR (0-shot) |18.00|
|MMLU-PRO (5-shot) |49.01|
|