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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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base_model: |
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- arcee-ai/Virtuoso-Small |
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- CultriX/SeQwence-14B-EvolMerge |
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- CultriX/Qwen2.5-14B-Wernicke |
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- sthenno-com/miscii-14b-1028 |
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- underwoods/medius-erebus-magnum-14b |
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- sometimesanotion/lamarck-14b-prose-model_stock |
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- sometimesanotion/lamarck-14b-reason-model_stock |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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model-index: |
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- name: Lamarck-14B-v0.3 |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: IFEval (0-Shot) |
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type: HuggingFaceH4/ifeval |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 50.32 |
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name: strict accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BBH (3-Shot) |
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type: BBH |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc_norm |
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value: 51.27 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MATH Lvl 5 (4-Shot) |
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type: hendrycks/competition_math |
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args: |
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num_few_shot: 4 |
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metrics: |
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- type: exact_match |
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value: 32.4 |
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name: exact match |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GPQA (0-shot) |
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type: Idavidrein/gpqa |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 18.46 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MuSR (0-shot) |
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type: TAUR-Lab/MuSR |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 18.0 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU-PRO (5-shot) |
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type: TIGER-Lab/MMLU-Pro |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 49.01 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sometimesanotion/Lamarck-14B-v0.3 |
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name: Open LLM Leaderboard |
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--- |
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 |
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--- |
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# merge |
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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. |
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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. |
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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! |
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**The merge strategy of Lamarck 0.3 can be summarized as:** |
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- Two model_stocks commence specialized branches for reasoning and prose quality. |
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- For refinement on both model_stocks, DELLA merges re-emphasize selected ancestors. |
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- For smooth instruction following, a SLERP merges Virtuoso with a DELLA merge of the two branches, where reason vs. prose quality are balanced. |
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- For finalization and normalization, a TIES merge. |
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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. |
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### Thanks go to: |
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- @arcee-ai's team for the ever-capable mergekit, and the exceptional Virtuoso Small model. |
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- @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. |
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- The authors behind the capable models that appear in the model_stock. |
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### Models Merged |
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**Top influences:** These ancestors are base models and present in the model_stocks, but are heavily re-emphasized in the DELLA and SLERP merges. |
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- **[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. |
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- **[CultriX/SeQwence-14B-EvolMerge](http://huggingface.co/CultriX/SeQwence-14B-EvolMerge)** - A top contender on reasoning benchmarks. |
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**Reason:** While Virtuoso is the strongest influence the starting ending layers, the reasoning mo |
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- **[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. |
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- **[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. |
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**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: |
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- **[sthenno-com/miscii-14b-1028](https://huggingface.co/sthenno-com/miscii-14b-1028)** |
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- **[underwoods/medius-erebus-magnum-14b](https://huggingface.co/underwoods/medius-erebus-magnum-14b)** |
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**Model stock:** Two model_stock merges, specialized for specific aspects of performance, are used to mildly influence a large range of the model. |
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- **[sometimesanotion/lamarck-14b-reason-model_stock](https://huggingface.co/sometimesanotion/lamarck-14b-reason-model_stock)** |
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- **[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). |
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**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. |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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name: lamarck-14b-reason-della # This contributes the knowledge and reasoning pool, later to be merged |
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merge_method: della # with the dominant instruction-following model |
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base_model: arcee-ai/Virtuoso-Small |
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tokenizer_source: arcee-ai/Virtuoso-Small |
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parameters: |
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int8_mask: false |
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normalize: true |
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rescale: false |
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density: 0.30 |
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weight: 0.50 |
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epsilon: 0.08 |
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lambda: 1.00 |
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models: |
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- model: CultriX/SeQwence-14B-EvolMerge |
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parameters: |
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density: 0.70 |
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weight: 0.90 |
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- model: sometimesanotion/lamarck-14b-reason-model_stock |
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parameters: |
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density: 0.90 |
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weight: 0.60 |
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- model: CultriX/Qwen2.5-14B-Wernicke |
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parameters: |
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density: 0.20 |
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weight: 0.30 |
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dtype: bfloat16 |
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out_dtype: bfloat16 |
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--- |
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name: lamarck-14b-prose-della # This contributes the prose, later to be merged |
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merge_method: della # with the dominant instruction-following model |
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base_model: arcee-ai/Virtuoso-Small |
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tokenizer_source: arcee-ai/Virtuoso-Small |
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parameters: |
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int8_mask: false |
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normalize: true |
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rescale: false |
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density: 0.30 |
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weight: 0.50 |
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epsilon: 0.08 |
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lambda: 0.95 |
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models: |
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- model: sthenno-com/miscii-14b-1028 |
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parameters: |
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density: 0.40 |
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weight: 0.90 |
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- model: sometimesanotion/lamarck-14b-prose-model_stock |
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parameters: |
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density: 0.60 |
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weight: 0.70 |
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- model: underwoods/medius-erebus-magnum-14b |
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dtype: bfloat16 |
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out_dtype: bfloat16 |
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--- |
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name: lamarck-14b-converge-della # This is the strongest control point to quickly |
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merge_method: della # re-balance reasoning vs. prose |
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base_model: arcee-ai/Virtuoso-Small |
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tokenizer_source: arcee-ai/Virtuoso-Small |
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parameters: |
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int8_mask: false |
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normalize: true |
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rescale: false |
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density: 0.30 |
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weight: 0.50 |
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epsilon: 0.08 |
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lambda: 1.00 |
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models: |
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- model: sometimesanotion/lamarck-14b-reason-della |
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parameters: |
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density: 0.80 |
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weight: 1.00 |
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- model: arcee-ai/Virtuoso-Small |
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parameters: |
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density: 0.40 |
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weight: 0.50 |
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- model: sometimesanotion/lamarck-14b-prose-della |
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parameters: |
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density: 0.10 |
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weight: 0.40 |
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dtype: bfloat16 |
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out_dtype: bfloat16 |
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--- |
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name: lamarck-14b-converge # Virtuoso has good capabilities all-around; it is 100% of the first |
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merge_method: slerp # two layers, and blends into the reasoning+prose convergance |
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base_model: arcee-ai/Virtuoso-Small # for some interesting boosts |
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tokenizer_source: base |
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parameters: |
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t: [ 0.00, 0.60, 0.80, 0.80, 0.80, 0.70, 0.40 ] |
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slices: |
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- sources: |
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- layer_range: [ 0, 2 ] |
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model: arcee-ai/Virtuoso-Small |
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- layer_range: [ 0, 2 ] |
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model: merges/lamarck-14b-converge-della |
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t: [ 0.00, 0.00 ] |
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- sources: |
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- layer_range: [ 2, 8 ] |
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model: arcee-ai/Virtuoso-Small |
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- layer_range: [ 2, 8 ] |
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model: merges/lamarck-14b-converge-della |
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t: [ 0.00, 0.60 ] |
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- sources: |
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- layer_range: [ 8, 16 ] |
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model: arcee-ai/Virtuoso-Small |
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- layer_range: [ 8, 16 ] |
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model: merges/lamarck-14b-converge-della |
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t: [ 0.60, 0.70 ] |
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- sources: |
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- layer_range: [ 16, 24 ] |
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model: arcee-ai/Virtuoso-Small |
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- layer_range: [ 16, 24 ] |
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model: merges/lamarck-14b-converge-della |
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t: [ 0.70, 0.70 ] |
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- sources: |
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- layer_range: [ 24, 32 ] |
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model: arcee-ai/Virtuoso-Small |
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- layer_range: [ 24, 32 ] |
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model: merges/lamarck-14b-converge-della |
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t: [ 0.70, 0.70 ] |
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- sources: |
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- layer_range: [ 32, 40 ] |
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model: arcee-ai/Virtuoso-Small |
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- layer_range: [ 32, 40 ] |
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model: merges/lamarck-14b-converge-della |
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t: [ 0.70, 0.60 ] |
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- sources: |
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- layer_range: [ 40, 48 ] |
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model: arcee-ai/Virtuoso-Small |
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- layer_range: [ 40, 48 ] |
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model: merges/lamarck-14b-converge-della |
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t: [ 0.60, 0.40 ] |
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dtype: bfloat16 |
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out_dtype: bfloat16 |
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--- |
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name: lamarck-14b-finalize |
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merge_method: ties |
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base_model: Qwen/Qwen2.5-14B |
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tokenizer_source: Qwen/Qwen2.5-14B-Instruct |
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parameters: |
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int8_mask: false |
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normalize: true |
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rescale: false |
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density: 1.00 |
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weight: 1.00 |
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models: |
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- model: merges/lamarck-14b-converge |
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dtype: bfloat16 |
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out_dtype: bfloat16 |
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--- |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sometimesanotion__Lamarck-14B-v0.3) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |36.58| |
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|IFEval (0-Shot) |50.32| |
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|BBH (3-Shot) |51.27| |
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|MATH Lvl 5 (4-Shot)|32.40| |
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|GPQA (0-shot) |18.46| |
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|MuSR (0-shot) |18.00| |
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|MMLU-PRO (5-shot) |49.01| |
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