https://github.com/znlp/implicit-cross-lingual-rewarding
공식 코드Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment
Papers with Code 아카이브
Instruction Following
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/znlp/language-imbalance-driven-rewarding
공식 코드Language Imbalance Driven Rewarding for Multilingual Self-improving
https://github.com/znlp/x-instruction
공식 코드https://github.com/zrrskywalker/llama-adapter
공식 코드LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://github.com/zwq803/mm-skin
공식 코드https://github.com/zytx121/geoground
공식 코드GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding
https://gitlab.com/ones-ai/eval-quant-llms
공식 코드https://github.com/0nutation/speechgpt
https://github.com/12kimih/hicupid
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip_2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/chatglm
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
https://github.com/BKAUTO/Trinocular-habitat-sim
https://github.com/BatsResearch/LexC-Gen
https://github.com/BirgerMoell/SwedishLLMBenchmark
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
https://github.com/BohdanPetryshyn/code-llama-fim-fine-tuning
https://github.com/DYEvaLab/EvalMuse
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model
https://github.com/Divs1159/STING-BEE
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
https://github.com/Forence1999/PRoLoRA
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
https://github.com/LLaVA-VL/LLaVA-NeXT
https://github.com/Lightning-AI/lit-llama
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://github.com/Luohh5/Chain-of-Exemplar
https://github.com/MS-P3/code3/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/Max-Fu/tvl
https://github.com/Mind23-2/MindCode-140
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://github.com/MindCode-4/code-4/tree/main/qwen2
https://github.com/Peter-Devine/multilingual_mt_bench
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
https://github.com/Pointcept/GPT4Point
https://github.com/QwenLM/Qwen-7B
https://github.com/QwenLM/Qwen2.5-Omni
https://github.com/Rain9876/ShareLoRA
https://github.com/Shalev-Lifshitz/STEVE-1
STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
https://github.com/Spico197/Humback
https://github.com/Sreyan88/GAMA
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
https://github.com/Sunnyzhr/habitat-lab
https://github.com/TIGER-AI-Lab/CritiqueFineTuning
Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate
https://github.com/TIGER-AI-Lab/ImagenHub
GenAI Arena: An Open Evaluation Platform for Generative Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.