matlok
's Collections
Fine-Tuning
updated
Metadata Might Make Language Models Better
Paper
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2211.10086
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Published
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4
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques
for LLMs
Paper
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2304.14999
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Published
•
2
PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and
Ensemble Techniques
Paper
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2401.02122
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Published
•
2
Zephyr: Direct Distillation of LM Alignment
Paper
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2310.16944
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Published
•
121
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper
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2310.05914
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Published
•
14
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language
Models
Paper
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2401.01335
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Published
•
64
LLaMA-Reviewer: Advancing Code Review Automation with Large Language
Models through Parameter-Efficient Fine-Tuning
Paper
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2308.11148
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Published
•
2
An Unsupervised Method for Estimating Class Separability of Datasets
with Application to LLMs Fine-Tuning
Paper
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2305.15016
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Published
•
5
DoRA: Weight-Decomposed Low-Rank Adaptation
Paper
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2402.09353
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Published
•
26
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action
Recognition
Paper
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2401.11649
•
Published
•
3
When Scaling Meets LLM Finetuning: The Effect of Data, Model and
Finetuning Method
Paper
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2402.17193
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Published
•
23
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized
Diffusion Model
Paper
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2402.17412
•
Published
•
21
Teaching Large Language Models to Reason with Reinforcement Learning
Paper
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2403.04642
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Published
•
46
Yi: Open Foundation Models by 01.AI
Paper
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2403.04652
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Published
•
62
Scaling Laws of RoPE-based Extrapolation
Paper
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2310.05209
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Published
•
6
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper
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2310.09263
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Published
•
39
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper
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2403.13372
•
Published
•
62
The Unreasonable Ineffectiveness of the Deeper Layers
Paper
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2403.17887
•
Published
•
78