DeepSeek-V2-Lite-Dense-3B-DO-ACP-K6
Research artifact, not a general-purpose model. These weights are released so that the experiments in the paper Pruning and Distilling Mixture-of-Experts into Dense Language Models can be reproduced. Each student is distilled on a fixed, small token budget (~4B tokens for Qwen3, 0.3B tokens for DeepSeek-V2-Lite and GPT-OSS) purely so that expert scoring and grouping methods can be compared under an equal budget. Absolute quality is therefore far below the teacher and below pretrained models of the same size, and no instruction tuning or alignment was applied. Please do not use this as an off-the-shelf assistant.
What this is
A dense student obtained by pruning and distilling a Mixture-of-Experts teacher, from the paper Pruning and Distilling Mixture-of-Experts into Dense Language Models. Code: https://github.com/krafton-ai/moe-to-dense
Best DeepSeek student in the paper. It is also the only scoring method there that prefers pure pruning over K=12 merging, which is the evidence that diversity-aware selection is what makes K equal to the teacher's top-k pay off.
Results
Full comparison group for this architecture, DeepSeek-V2-Lite to dense, 0.3B-token distillation (paper Table 17). This model's row is in bold, and rows whose weights are also released link to them.
| Configuration | Wino | Hella | ARC-E | ARC-C | MMLU | Avg |
|---|---|---|---|---|---|---|
| SF, K=6 | 54.9 | 38.9 | 52.4 | 27.1 | 26.9 | 40.04 |
| SF, K=12 | 56.9 | 41.1 | 54.8 | 28.4 | 24.6 | 41.16 |
| CP, K=6 | 53.0 | 36.9 | 49.4 | 25.7 | 25.3 | 38.07 |
| CP, K=12 | 55.6 | 40.2 | 53.5 | 26.6 | 26.8 | 40.53 |
| ACP, K=6 | 56.8 | 38.6 | 51.0 | 27.5 | 28.1 | 40.37 |
| ACP, K=12 | 57.1 | 40.9 | 52.9 | 27.4 | 26.4 | 40.93 |
| DO-ACP, K=6 | 60.3 | 41.0 | 53.7 | 28.2 | 28.7 | 42.39 |
| DO-ACP, K=12 | 59.0 | 41.5 | 51.7 | 26.2 | 26.9 | 41.07 |
| Random FFN + teacher attn | 50.6 | 25.6 | 30.6 | 20.9 | 23.6 | 30.25 |
| Random initialization | 50.1 | 25.4 | 28.8 | 24.1 | 22.9 | 30.27 |
| Teacher (DeepSeek-V2-Lite) | 76.2 | 80.5 | 84.4 | 56.3 | 58.0 | 71.09 |
Downstream accuracy is Winogrande 5-shot, HellaSwag 10-shot, ARC-Easy 25-shot, ARC-Challenge 25-shot and MMLU 5-shot. Avg is the unweighted mean of the five benchmarks.
Configuration
| Field | Value |
|---|---|
| Teacher | deepseek-ai/DeepSeek-V2-Lite |
| Student parameters | 2.66B |
| Distillation data | FineWeb-Edu (sample-10BT), 0.3B tokens |
| Expert scoring | DO-ACP |
| Experts kept (K) | 6 |
| Grouping | pure pruning on the routed experts, 8 groups total (2 shared + 6 routed) |
| Down-projection scaling | shared experts copied unscaled, routed groups scaled by average conditional probability |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"jhyuckkim/DeepSeek-V2-Lite-Dense-3B-DO-ACP-K6", dtype="bfloat16", trust_remote_code=True
)
tok = AutoTokenizer.from_pretrained("jhyuckkim/DeepSeek-V2-Lite-Dense-3B-DO-ACP-K6", trust_remote_code=True)
trust_remote_code=True is required. The student keeps DeepSeek's own model
class so that MLA attention and the shared experts are preserved unchanged, and
configuration_deepseek.py / modeling_deepseek.py ship with the repo.
Citation
@article{kim2026pruning,
title={Pruning and Distilling Mixture-of-Experts into Dense Language Models},
author={Kim, Junhyuck and Yun, Jihun and Kim, Haechan and Kim, Gyeongman and Bae, Joonghyun and Cho, Jaewoong},
journal={arXiv preprint arXiv:2605.28207},
year={2026}
}