Models
Datasets
Spaces
Posts
Docs
Pricing
Log In
Sign Up

Collections

Discover the best community collections!

Collections including paper arxiv:2403.19270

Alignment-DPO-line

sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 31
Advancing LLM Reasoning Generalists with Preference Trees

Paper • 2404.02078 • Published Apr 2 • 41
Learn Your Reference Model for Real Good Alignment

Paper • 2404.09656 • Published Apr 15 • 79

Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 99
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 31
ViTAR: Vision Transformer with Any Resolution

Paper • 2403.18361 • Published Mar 27 • 48
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Paper • 2403.18814 • Published Mar 27 • 37

On the Societal Impact of Open Foundation Models

Paper • 2403.07918 • Published Feb 27 • 16
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 31
Hallucinations or Attention Misdirection? The Path to Strategic Value Extraction in Business Using Large Language Models

Paper • 2402.14002 • Published Feb 21
Evaluating the Social Impact of Generative AI Systems in Systems and Society

Paper • 2306.05949 • Published Jun 9, 2023 • 6

Adapting Large Language Models via Reading Comprehension

Paper • 2309.09530 • Published Sep 18, 2023 • 69
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 31

Papers - Model - SFT - Alpaca and DPO - Solar

sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 31

Papers - Fine-Tuning - SFT

about 1 month ago

InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26 • 25
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 31
Learn Your Reference Model for Real Good Alignment

Paper • 2404.09656 • Published Apr 15 • 79
OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data

Paper • 2404.12195 • Published Apr 18 • 11

Papers - Fine-tuning - DPO

Refer to additional papers: https://link.springer.com/article/10.1007/s10994-014-5458-8 and https://link.springer.com/article/10.1007/BF00992696

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 37
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

Paper • 2402.09320 • Published Feb 14 • 6
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 31
Dueling RL: Reinforcement Learning with Trajectory Preferences

Paper • 2111.04850 • Published Nov 8, 2021 • 2

ORPO: Monolithic Preference Optimization without Reference Model

Paper • 2403.07691 • Published Mar 12 • 56
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 31
Teaching Large Language Models to Reason with Reinforcement Learning

Paper • 2403.04642 • Published Mar 7 • 43
Best Practices and Lessons Learned on Synthetic Data for Language Models

Paper • 2404.07503 • Published Apr 11 • 25

Papers - Fine-tuning

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

Paper • 2310.20587 • Published Oct 31, 2023 • 15
SELF: Language-Driven Self-Evolution for Large Language Model

Paper • 2310.00533 • Published Oct 1, 2023 • 2
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 41
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 43

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6 • 172
RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15 • 61
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Paper • 2403.13372 • Published Mar 20 • 54
InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26 • 25

Previous
1
2
Next

Company

© Hugging Face

TOS Privacy About Jobs

Website

Models Datasets Spaces Pricing Docs