https://github.com/thu-coai/miniplm
공식 코드MiniPLM: Knowledge Distillation for Pre-Training Language Models
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 11
- 보존된 평가 기록
- 90
Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thu-keg/agentif
공식 코드AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios
https://github.com/thu-keg/kola
공식 코드KoLA: Carefully Benchmarking World Knowledge of Large Language Models
https://github.com/thu-keg/rm-bench
공식 코드RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style
https://github.com/thu-ml/adaptive-sparse-trainer
공식 코드Pruning Large Language Models with Semi-Structural Adaptive Sparse Training
https://github.com/thua919/NeRF-SLAM-Benchmark-CVPR24
공식 코드Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM
https://github.com/thudm/alignbench
공식 코드AlignBench: Benchmarking Chinese Alignment of Large Language Models
https://github.com/thudm/grb
공식 코드Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
https://github.com/thudm/hgb
공식 코드https://github.com/thudm/paper-source-trace
공식 코드PST-Bench: Tracing and Benchmarking the Source of Publications
https://github.com/thuiar/CRL
공식 코드Consistent Representation Learning for Continual Relation Extraction
https://github.com/thumnlab/nas-bench-graph
공식 코드NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search
https://github.com/thunlp-mt/ckd
공식 코드Continual Knowledge Distillation for Neural Machine Translation
https://github.com/thunlp-mt/cospace
공식 코드CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models
https://github.com/thunlp-mt/dbkd-plm
공식 코드https://github.com/thunlp-mt/stabletoolbench
공식 코드StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models
https://github.com/thunlp/Knowledge-Inheritance
공식 코드https://github.com/thunlp/tritonbench
공식 코드TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
https://github.com/thy960112/SpectralKD
공식 코드https://github.com/thyrixyang/idtoolkit
공식 코드IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics
https://github.com/tianduowang/dpo-st
공식 코드Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning
https://github.com/tianyao-aka/mugsi
공식 코드MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
https://github.com/tianyipeng-lab/digital-twin-simulation
공식 코드https://github.com/tianyucodings/cdiff
공식 코드Conditional diffusions for amortized neural posterior estimation
https://github.com/tiiiger/benchmark_llm_summarization
공식 코드https://github.com/tim-tadj/iot_datatrust_rwi
공식 코드https://github.com/timbervision/timbervision
공식 코드https://github.com/timinar/babyllama
공식 코드https://github.com/timok93/blind_knowledge_distillation
공식 코드Blind Knowledge Distillation for Robust Image Classification
https://github.com/tinkoff-ai/CORL
공식 코드CORL: Research-oriented Deep Offline Reinforcement Learning Library
https://github.com/tinnerhrhe/HIB_Policy
공식 코드Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective
https://github.com/tinytigerpan/bckd
공식 코드Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection
https://github.com/tiwarylab/latentthermoflows
공식 코드https://github.com/tjujianyu/rfc
공식 코드Rich Feature Construction for the Optimization-Generalization Dilemma
https://github.com/tkforgeron/ocppm
공식 코드HOEG: A New Approach for Object-Centric Predictive Process Monitoring
https://github.com/tmbdev/clstm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.