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Collections including paper arxiv:2412.09871

CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data

Paper • 2404.15653 • Published Apr 24, 2024 • 26
MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24, 2024 • 12
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Paper • 2405.12130 • Published May 20, 2024 • 47
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

Paper • 2405.12981 • Published May 21, 2024 • 28

Papers - Llama 3

How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study

Paper • 2404.14047 • Published Apr 22, 2024 • 45
Reasoning in Large Language Models: A Geometric Perspective

Paper • 2407.02678 • Published Jul 2, 2024 • 1
Natural Language Reinforcement Learning

Paper • 2411.14251 • Published Nov 21, 2024 • 28
Byte Latent Transformer: Patches Scale Better Than Tokens

Paper • 2412.09871 • Published about 1 month ago • 86

LM Architectures

Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Paper • 2404.08801 • Published Apr 12, 2024 • 64
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Paper • 2404.07839 • Published Apr 11, 2024 • 43
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

Paper • 2404.05892 • Published Apr 8, 2024 • 33
Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 139

Papers - Encodings - No Positional Encodings - NoPE

The Impact of Positional Encoding on Length Generalization in Transformers

Paper • 2305.19466 • Published May 31, 2023 • 2
Transformers Can Do Arithmetic with the Right Embeddings

Paper • 2405.17399 • Published May 27, 2024 • 52
Teaching Transformers Causal Reasoning through Axiomatic Training

Paper • 2407.07612 • Published Jul 10, 2024 • 2
Round and Round We Go! What makes Rotary Positional Encodings useful?

Paper • 2410.06205 • Published Oct 8, 2024 • 1

Papers - Encodings - Rotary - RoPE

The Impact of Positional Encoding on Length Generalization in Transformers

Paper • 2305.19466 • Published May 31, 2023 • 2
Qwen2 Technical Report

Paper • 2407.10671 • Published Jul 15, 2024 • 160
Round and Round We Go! What makes Rotary Positional Encodings useful?

Paper • 2410.06205 • Published Oct 8, 2024 • 1
ThunderKittens: Simple, Fast, and Adorable AI Kernels

Paper • 2410.20399 • Published Oct 27, 2024 • 1

Papers - Llama 2

Instruction Tuning with Human Curriculum

Paper • 2310.09518 • Published Oct 14, 2023 • 3
A Thorough Examination of Decoding Methods in the Era of LLMs

Paper • 2402.06925 • Published Feb 10, 2024 • 1
Distilling System 2 into System 1

Paper • 2407.06023 • Published Jul 8, 2024 • 3
Byte Latent Transformer: Patches Scale Better Than Tokens

Paper • 2412.09871 • Published about 1 month ago • 86

Papers - Tokenizers - LLaMA Byte Pair Encoding (BPE)

Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Paper • 2306.02707 • Published Jun 5, 2023 • 46
Byte Latent Transformer: Patches Scale Better Than Tokens

Paper • 2412.09871 • Published about 1 month ago • 86

Papers - Multilingual - Benchmarks

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 20
ByT5: Towards a token-free future with pre-trained byte-to-byte models

Paper • 2105.13626 • Published May 28, 2021 • 3
Byte Latent Transformer: Patches Scale Better Than Tokens

Paper • 2412.09871 • Published about 1 month ago • 86

Papers - Multilingual - Encoders - BPE

Poro 34B and the Blessing of Multilinguality

Paper • 2404.01856 • Published Apr 2, 2024 • 13
Byte Latent Transformer: Patches Scale Better Than Tokens

Paper • 2412.09871 • Published about 1 month ago • 86

Papers - Embeddings

Gecko: Versatile Text Embeddings Distilled from Large Language Models

Paper • 2403.20327 • Published Mar 29, 2024 • 47
Round and Round We Go! What makes Rotary Positional Encodings useful?

Paper • 2410.06205 • Published Oct 8, 2024 • 1
Byte Latent Transformer: Patches Scale Better Than Tokens

Paper • 2412.09871 • Published about 1 month ago • 86
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models

Paper • 2410.20771 • Published Oct 28, 2024 • 3

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