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Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ming-k9/mft
공식 코드https://github.com/mingyue-cheng/bingjian
공식 코드Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
https://github.com/mingyuj666/stockagent
공식 코드https://github.com/mininglamp-mllm/hmllm
공식 코드https://github.com/mirunabetianu/dallmi
공식 코드DALLMi: Domain Adaption for LLM-based Multi-label Classifier
https://github.com/mistyreed63849/graph-llm
공식 코드GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
https://github.com/mit-han-lab/omniserve
공식 코드QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
https://github.com/mit-han-lab/qserve
공식 코드QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
https://github.com/mit-ll/linkq
공식 코드LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering
https://github.com/mitmedialab/ai-false-memories
공식 코드Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews
https://github.com/mitsuichen14/lrsclip
공식 코드LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer Text
https://github.com/miulab/lmrec-survey
공식 코드Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
https://github.com/miulab/taiwan-llama
공식 코드Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
https://github.com/miulab/taiwan-llm
공식 코드Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
https://github.com/mj-storytelling/DiversityTuning
공식 코드Modifying Large Language Model Post-Training for Diverse Creative Writing
https://github.com/mkandai/llms-in-the-loop
공식 코드https://github.com/ml-ailab/ophglm
공식 코드https://github.com/mlfoundations/rtfm
공식 코드Large Scale Transfer Learning for Tabular Data via Language Modeling
https://github.com/mlfoundations/tabliblib
공식 코드Large Scale Transfer Learning for Tabular Data via Language Modeling
https://github.com/mlgroupjlu/matrixnuclearnorm
공식 코드https://github.com/mllm-tool/mllm-tool
공식 코드MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning
https://github.com/mllmkcbench/mllmkc
공식 코드Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models
https://github.com/mlplyler/ICDA
공식 코드https://github.com/mlvlab/RALF
공식 코드https://github.com/mlvlab/llamo
공식 코드https://github.com/mmurrad/tweet-data-classification
공식 코드Multi-Lingual Cyber Threat Detection in Tweets/X Using ML, DL, and LLM: A Comparative Analysis
https://github.com/mnoukhov/async_rlhf
공식 코드Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models
https://github.com/mobs-fbk/llm_interaction_simulator
공식 코드https://github.com/modeeric/orbit-llama
공식 코드https://github.com/modelscope/mcpbench
공식 코드https://github.com/modeltc/llmc
공식 코드LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
https://github.com/mohanraj-nlp/chatgpt-malaysian-english
공식 코드https://github.com/moment-timeseries-foundation-model/timeseriesgym
공식 코드TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents
https://github.com/monarch-initiative/dragon-ai-results
공식 코드Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)
https://github.com/monarch-initiative/gpt-ontology-completion-analysis
공식 코드Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.