Cut Your Losses in Large-Vocabulary Language Models
Paper
•
2411.09009
•
Published
•
40
Ankit Sharma
nezubn
AI & ML interests
engineering • systems • ml
Recent Activity
liked
a model
5 days ago
bartowski/Llama-3.1_OpenScholar-8B-GGUF
upvoted
a
paper
13 days ago
Cut Your Losses in Large-Vocabulary Language Models
upvoted
a
paper
about 1 month ago
Teach Multimodal LLMs to Comprehend Electrocardiographic Images
Organizations
nezubn's activity
upvoted
a
paper
13 days ago
upvoted
a
paper
about 1 month ago
upvoted
2
articles
4 months ago
Article
Optimizing your LLM in production
•
15
Article
Getting Started With Embeddings
•
38
upvoted
an
article
5 months ago
Article
quanto: a pytorch quantization toolkit
•
31
upvoted
2
papers
6 months ago
upvoted
a
paper
7 months ago
upvoted
an
article
8 months ago
Article
Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
•
94
upvoted
8
papers
8 months ago
Understanding LLMs: A Comprehensive Overview from Training to Inference
Paper
•
2401.02038
•
Published
•
62
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper
•
2404.02258
•
Published
•
104
Advancing LLM Reasoning Generalists with Preference Trees
Paper
•
2404.02078
•
Published
•
44
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
Paper
•
2403.20041
•
Published
•
34
Jamba: A Hybrid Transformer-Mamba Language Model
Paper
•
2403.19887
•
Published
•
104
Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction
Paper
•
2403.18795
•
Published
•
18
Long-form factuality in large language models
Paper
•
2403.18802
•
Published
•
24
The case for 4-bit precision: k-bit Inference Scaling Laws
Paper
•
2212.09720
•
Published
•
3
upvoted
2
papers
9 months ago
upvoted
a
paper
10 months ago