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Large Language Model
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/FoundationVision/Infinity
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
https://github.com/FreedomIntelligence/BlenderLLM
https://github.com/FreedomIntelligence/PlatoLM
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator
https://github.com/FunAudioLLM/ThinkSound
https://github.com/GLUS-video/GLUS
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
https://github.com/GitF82/NLP-Embeddings-Protoformer-Paper
https://github.com/Grecil/Corrective-RAG
https://github.com/HectorHHZ/Sparse_Matrix_Tuning
https://github.com/HettyPatel/TRAWL
TRAWL: Tensor Reduced and Approximated Weights for Large Language Models
https://github.com/Hyeongkeun/LAVCap
LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport
https://github.com/ICT-GoKnow/KnowCoder
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction
https://github.com/IDEA-FinAI/Simple-HHEA
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
https://github.com/JesusCarr/JARVIS
https://github.com/JingfenQiao/ColPali-Reproducibility
ColPali: Efficient Document Retrieval with Vision Language Models
https://github.com/Jl-wei/ai-gen-ui
https://github.com/KastanDay/video-pretrained-transformer
https://github.com/KaveeshaSIlva/Baseline-FYP
PathVQA: 30000+ Questions for Medical Visual Question Answering
https://github.com/KaveeshaSIlva/PathVQA
PathVQA: 30000+ Questions for Medical Visual Question Answering
https://github.com/LaVi-Lab/Video-3D-LLM
Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors
https://github.com/Leeroo-AI/mergoo
https://github.com/LiuHuijie6410/OmniDish
Omni-Dish: Photorealistic and Faithful Image Generation and Editing for Arbitrary Chinese Dishes
https://github.com/MS-P3/code3/tree/main/jamba
https://github.com/MS-P3/code5/tree/main/llama3
An empirical study of LLaMA3 quantization: from LLMs to MLLMs
https://github.com/MS-P3/code6/tree/main/rag
RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
https://github.com/Mind23-2/MindCode-134
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
https://github.com/MindCode-4/code-12/tree/main/isr
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
https://github.com/MindCode-4/code-14/tree/main/CAJ
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
https://github.com/MindCode-4/code-4/tree/main/qwen2_vl
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
https://github.com/MindSpore-paper-code-2/code400/tree/main/DeepText
https://github.com/MindSpore-paper-code-3/code10/tree/main/guided_anchoring
https://github.com/MindSpore-paper-code-3/code9/tree/main/DeepText
https://github.com/MindSpore-scientific-2/code-10/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-10/tree/main/QuantExplainNLP_AlgoAnalysis
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
https://github.com/MindSpore-scientific-2/code-14/tree/main/jamba
https://github.com/MindSpore-scientific-2/code-4/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.