HIGGS Collection Models prequantized with [HIGGS](https://arxiv.org/abs/2411.17525) zero-shot quantization. Requires the latest `transformers` to run. • 17 items • Updated 10 days ago • 4
HIGGS Collection Models prequantized with [HIGGS](https://arxiv.org/abs/2411.17525) zero-shot quantization. Requires the latest `transformers` to run. • 17 items • Updated 10 days ago • 4
HIGGS Collection Models prequantized with [HIGGS](https://arxiv.org/abs/2411.17525) zero-shot quantization. Requires the latest `transformers` to run. • 17 items • Updated 10 days ago • 4
Sparse Finetuning for Inference Acceleration of Large Language Models Paper • 2310.06927 • Published Oct 10, 2023 • 14
Towards End-to-end 4-Bit Inference on Generative Large Language Models Paper • 2310.09259 • Published Oct 13, 2023 • 1
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression Paper • 2306.03078 • Published Jun 5, 2023 • 3
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation Paper • 2401.04679 • Published Jan 9, 2024 • 2
Extreme Compression of Large Language Models via Additive Quantization Paper • 2401.06118 • Published Jan 11, 2024 • 12
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization Paper • 2308.02060 • Published Aug 3, 2023 • 1