https://github.com/dexter-gt-86/sinklora
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Papers with Code 아카이브
Instruction Following
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 보존된 평가 기록
- 4
Instruction following is the basic task of the model. This task is dedicated to evaluating the ability of the large model to follow human instructions. It is hoped that the model can generate controllable and safe answers.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dinhvietcuong1996/icme25-inova
https://github.com/dortsur/heavywater_simplexwater
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
https://github.com/efeslab/Nanoflow
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
https://github.com/ericchen321/ros_x_habitat
https://github.com/erikaawang/probing-multilingual-dynamics
https://github.com/etendue/habitat-api
https://github.com/facebookresearch/habitat-challenge
https://github.com/facebookresearch/habitat-lab
https://github.com/facebookresearch/multimodal
https://github.com/facico/chinese-vicuna
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/fanqiwan/fusellm
https://github.com/flagai-open/aquila2
https://github.com/flagopen/flaginstruct
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/flowritecom/flow-merge
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
https://github.com/for-ai/language-confusion
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
https://github.com/fsoft-ai4code/codecapybara
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/fudandisc/reform-eval
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?
https://github.com/funaudiollm/inspiremusic
https://github.com/gagan3012/self_rewarding_models
https://github.com/ghwang-s/abkd
DistiLLM: Towards Streamlined Distillation for Large Language Models
https://github.com/google-deepmind/streetlearn
https://github.com/gregor-ge/mblip
https://github.com/guijinSON/MM-Eval
https://github.com/h-zhao1997/cobra
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
https://github.com/h2r/sloop
Spatial Language Understanding for Object Search in Partially Observed City-scale Environments
https://github.com/hamishivi/automated-instruction-selection
https://github.com/hitz-zentroa/gollie
EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models
https://github.com/hkust-nlp/simplerl-reason
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild
https://github.com/huang-research-group/InstructionGPT-4
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
https://github.com/huggingface/text-generation-inference
https://github.com/huggingface/transformers
https://github.com/hyintell/citb
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/iinemo/lm-polygraph
https://github.com/imoneoi/openchat
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/instructlab/instructlab
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.