Fine-Tuning
updated
Metadata Might Make Language Models Better
Paper
• 2211.10086
• Published • 4
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques
for LLMs
Paper
• 2304.14999
• Published • 2
PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and
Ensemble Techniques
Paper
• 2401.02122
• Published • 2
Zephyr: Direct Distillation of LM Alignment
Paper
• 2310.16944
• Published • 123
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper
• 2310.05914
• Published • 14
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language
Models
Paper
• 2401.01335
• Published • 68
LLaMA-Reviewer: Advancing Code Review Automation with Large Language
Models through Parameter-Efficient Fine-Tuning
Paper
• 2308.11148
• Published • 2
An Unsupervised Method for Estimating Class Separability of Datasets
with Application to LLMs Fine-Tuning
Paper
• 2305.15016
• Published • 5
DoRA: Weight-Decomposed Low-Rank Adaptation
Paper
• 2402.09353
• Published • 32
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action
Recognition
Paper
• 2401.11649
• Published • 3
When Scaling Meets LLM Finetuning: The Effect of Data, Model and
Finetuning Method
Paper
• 2402.17193
• Published • 26
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized
Diffusion Model
Paper
• 2402.17412
• Published • 23
Teaching Large Language Models to Reason with Reinforcement Learning
Paper
• 2403.04642
• Published • 48
Yi: Open Foundation Models by 01.AI
Paper
• 2403.04652
• Published • 65
Scaling Laws of RoPE-based Extrapolation
Paper
• 2310.05209
• Published • 8
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper
• 2310.09263
• Published • 40
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper
• 2403.13372
• Published • 182
The Unreasonable Ineffectiveness of the Deeper Layers
Paper
• 2403.17887
• Published • 82