A curated list for Efficient Large Language Models
[ICLR-2025-SLLM Spotlight 🔥]MobiLlama : Small Language Model tailored for edge devices
[ICML 2024] CLLMs: Consistency Large Language Models
[NAACL' 25 main] Lillama: Large Language Model Compression via Low-Rank Feature Distillation
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Official implementation of ARC-Decode, a training-free speculative decoding framework with risk-bounded acceptance. ICML 2026 Poster.
[ICLR 2026] GlowQ: Group-Shared LOw-Rank Approximation for Quantized LLMs
Intelligent layer pruning toolkit for LLMs featuring iterative optimization, self-healing algorithms, and comprehensive benchmarking.
Multimodal-MoE-Slimming is a post-training compression framework for multimodal Mixture-of-Experts models. It analyzes how experts respond differently to visual and textual information, then uses this modality-aware behavior to reduce redundant expert capacity and build compact MoE checkpoints for more efficient deployment.
Colab-friendly BitNet distillation engine: collect KD traces from a teacher, train a ternary Mini-BitNet, and dry-run 7B memory. Multi-provider + Drive/S3
Research code for ProbeRoute, a probe-initialized sparse routing method for frozen-backbone multi-token prediction
A Curated Paper List for Efficient Large Models
Official implementation of "An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning" — TMLR 2025. Efficient structured sparse fine-tuning for LLMs.
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