Shengkun/DarwinLM-4.6B-Pruned

Paper: https://arxiv.org/pdf/2502.07780
Code: https://github.com/IST-DASLab/DarwinLM
Models: DarwinLM-2.7B, DarwinLM-4.6B, DarwinLM-8.4B
Pruned Models without Post-training: DarwinLM-2.7B-Pruned, DarwinLM-4.6B-Pruned, DarwinLM-8.4B-Pruned

---

This repository contains the weights of DarwinLM, an evolutionary structured pruning methods for large language models, as introduced in our paper. DarwinLM builds upon an evolutionary search process, generating multiple offspring models in each generation through mutation, and selecting the fittest for survival.

# Please add trust_remote_code=True as the repo includes custom code to load and run DarwinLM
model = AutoModelForCausalLM.from_pretrained("Shengkun/DarwinLM-4.6B-Pruned", trust_remote_code=True)

Downstream Tasks

2.7B

Method	Param.	SciQ	PIQA	WG	ArcE	ArcC	HS	LogiQA	BoolQ	Avg
Dense	6.7B	93.7	78.1	69.3	76.4	53.0	78.6	30.7	77.7	69.2
Uniform	3.4B	44.1	57.1	53.3	33.5	32.2	27.3	25.0	49.0	40.1
ZipLM	4.0B	87.4	64.4	58.3	53.2	33.6	50.1	25.5	63.6	54.5
ShearedLLama	2.7B	84.5	66.4	53.4	49.8	28.4	47.6	27.6	50.9	51.0
DarwinLM (one-shot)	2.7B	85.6	70.8	55.8	63.3	38.1	53.2	28.5	62.7	57.2
ShearedLLama (50B)	2.7B	90.8	75.8	64.2	67.0	41.2	70.8	28.2	63.0	62.6
ShearedLLama (10B†)	2.7B	92.0	73.6	63.1	69.8	42.0	64.4	29.0	62.1	61.9
DarwinLM (10B)	2.6B	90.8	72.2	65.1	68.5	45.0	67.2	28.5	64.6	62.8

4.6B

Model	Method	Param.	SciQ	PIQA	WG	ArcE	ArcC	HS	LogiQA	BoolQ	MMLU	Avg
Llama-3.1-8B	Dense	8B	96.3	81.2	74.3	81.4	58.2	81.7	31.1	84.0	65.2	72.8
	Uniform	4.5B	29.1	53.6	51.7	26.0	23.6	27.1	25.5	62.1	25.7	36.1
	ZipLM	6B	65.5	60.6	56.0	40.2	34.4	34.4	28.1	63.0	27.9	45.7
	DarwinLM (one-shot)	4.6B	84.9	69.4	57.3	59.6	34.2	44.6	24.1	62.2	28.5	51.6
	OLMO (2.5T)	7B	92.8	79.4	70.4	73.3	44.9	77.1	27.9	72.5	28.3	62.9
	DarwinLM (10.0B)	4.6B	93.2	74.8	67.4	73.2	51.6	71.3	30.7	71.1	40.6	63.7

8.4B

Model	Method	Param.	SciQ	PIQA	WG	ArcE	ArcC	HS	LogiQA	BoolQ	MMLU	Avg
Qwen-2.5-14B-Instruct	Dense	14B	96.8	81.9	79.1	85.7	72.8	85.1	38.5	87.9	80.0	78.6
	Uniform	8.6B	78.2	72.7	57.6	76.1	45.6	47.0	28.1	61.6	45.5	56.9
	ZipLM	8.5B	69.0	66.4	52.8	60.1	38.3	43.3	29.6	60.2	25.0	49.4
	DarwinLM (one-shot)	8.4B	84.3	73.9	60.5	75.7	48.0	53.3	29.3	66.9	43.1	59.4
	OLMO-0424 (2.05T)	7B	96.1	80.1	72.1	73.8	49.2	78.0	29.3	80.8	52.1	67.9
	DarwinLM (10.0B)	8.4B	89.5	78.1	70.7	79.6	57.6	74.9	33.5	73.9	57.9	68.4

Bibtex

@article{tang2025darwinlm,
  title={DarwinLM: Evolutionary Structured Pruning of Large Language Models},
  author={Tang, Shengkun and Sieberling, Oliver and Kurtic, Eldar and Shen, Zhiqiang and Alistarh, Dan},
  journal={arXiv preprint arXiv:2502.07780},
  year={2025}
}

---

For any issues or questions, please open an issue or contact us directly. 🚀