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Collections including paper arxiv:2402.04177

Empowering SLMs

Collection of resources focusing on making Small Language Models (SLMs) better at various tasks

OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework

Paper • 2404.14619 • Published Apr 22, 2024 • 127
Scaling Laws for Downstream Task Performance of Large Language Models

Paper • 2402.04177 • Published Feb 6, 2024 • 18
Orca 2: Teaching Small Language Models How to Reason

Paper • 2311.11045 • Published Nov 18, 2023 • 73
Orca-Math: Unlocking the potential of SLMs in Grade School Math

Paper • 2402.14830 • Published Feb 16, 2024 • 24

Scaling Laws for Forgetting When Fine-Tuning Large Language Models

Paper • 2401.05605 • Published Jan 11, 2024
Scaling Laws for Downstream Task Performance of Large Language Models

Paper • 2402.04177 • Published Feb 6, 2024 • 18
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks

Paper • 2312.15614 • Published Dec 25, 2023

Scaling Laws for Downstream Task Performance of Large Language Models

Paper • 2402.04177 • Published Feb 6, 2024 • 18
A Tale of Tails: Model Collapse as a Change of Scaling Laws

Paper • 2402.07043 • Published Feb 10, 2024 • 15
Scaling Laws for Fine-Grained Mixture of Experts

Paper • 2402.07871 • Published Feb 12, 2024 • 14
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Paper • 2402.17193 • Published Feb 27, 2024 • 24

20s LLM Toolbox

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Paper • 2402.04291 • Published Feb 6, 2024 • 49
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6, 2024 • 115
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6, 2024 • 31
Scaling Laws for Downstream Task Performance of Large Language Models

Paper • 2402.04177 • Published Feb 6, 2024 • 18

Scaling Laws for Downstream Task Performance of Large Language Models

Paper • 2402.04177 • Published Feb 6, 2024 • 18
Offline Actor-Critic Reinforcement Learning Scales to Large Models

Paper • 2402.05546 • Published Feb 8, 2024 • 5
SaulLM-7B: A pioneering Large Language Model for Law

Paper • 2403.03883 • Published Mar 6, 2024 • 79
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27, 2024 • 609

Efficient-Continuous Training

LLaMA Pro: Progressive LLaMA with Block Expansion

Paper • 2401.02415 • Published Jan 4, 2024 • 53
Scaling Laws for Downstream Task Performance of Large Language Models

Paper • 2402.04177 • Published Feb 6, 2024 • 18

Approximating Two-Layer Feedforward Networks for Efficient Transformers

Paper • 2310.10837 • Published Oct 16, 2023 • 11
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 97
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 27
LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 14

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Paper • 2309.14509 • Published Sep 25, 2023 • 18
LLM Augmented LLMs: Expanding Capabilities through Composition

Paper • 2401.02412 • Published Jan 4, 2024 • 37
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11, 2024 • 49
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16, 2024 • 23

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