Qwen 2.5 Merges
Collection
A series of merges prioritizing instruction following and reasoning, with a little attention to prose quality as well.
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A newer version of this model is available:
sometimesanotion/Lamarck-14B-v0.4-Qwenvergence
merge
Lamarck-14B is the product of a multi-stage merge which emphasizes arcee-ai/Virtuoso-Small in early and finishing layers, and midway features strong emphasis on reasoning, and ends balanced somewhat towards Virtuoso again.
For GGUFs, 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.
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 - A brand new model from Arcee, refined from the notable cross-architecture Llama-to-Qwen distillation 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 - 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 - 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 - 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:
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-prose-model_stock - This brings in a little influence from EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2, oxyapi/oxy-1-small, and allura-org/TQ2.5-14B-Sugarquill-v1.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| 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 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard50.320
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard51.270
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard32.400
- acc_norm on GPQA (0-shot)Open LLM Leaderboard18.460
- acc_norm on MuSR (0-shot)Open LLM Leaderboard18.000
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard49.010