Sub-1-Bit LLM Compression via Latent Factorization

Official implementation of LittleBit (NeurIPS 2025) and LittleBit-2 (ICML 2026).

LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment (ICML 2026)

Banseok Lee, Youngmin Kim

LittleBit: Ultra Low-Bit Quantization via Latent Factorization (NeurIPS 2025)

Banseok Lee*, Dongkyu Kim*, Youngcheon You, Youngmin Kim

LittleBit compresses large language models into the sub-1-bit regime by factorizing each dense weight matrix into low-rank latent factors, binarizing those factors, and restoring magnitude information through lightweight learned scales. This enables extreme compression, including the 0.1 bits-per-weight setting, while preserving the original model architecture at inference time.

LittleBit-2 improves this recipe by addressing latent geometry misalignment in the initialization stage. It applies Internal Latent Rotation with Joint Iterative Quantization (Joint-ITQ), aligning the SVD-derived latent factors with the binary hypercube before QAT. LittleBit-2 initialization is available as an opt-in (--use_itq) and produces no additional inference overhead.

Sub-1-bit compression: Designed for 1.0 to 0.1 bits per weight.

LittleBit-2 opt-in: Enable Joint-ITQ initialization with --use_itq for improved latent geometry alignment.

No inference-time change: LittleBit-2 modifies initialization only; the deployed factorized layer remains the same.

QAT-friendly: Supports Quantization-Aware Training with SmoothSign and optional residual factorization.

Supported Models

The codebase currently supports:

conda create -n littlebit python=3.12 conda activate littlebit

Install CUDA toolkit. Adjust the CUDA version if needed.

conda install nvidia/label/cuda-12.4.1::cuda-toolkit -c nvidia/label/cuda-12.4.1

Install PyTorch.

pip install torch==2.8.0+cu124 torchvision==0.23.0+cu124 torchaudio==2.8.0+cu124 --index-url https://download.pytorch.org/whl/cu124

Install dependencies.

pip install -r requirements.txt

Important

For reproducing the paper results, use transformers 4.51.x. Newer transformers releases may change model internals or evaluation behavior.

pip install "transformers==4.51.*"

Usage

Training

Train a model with Quantization-Aware Training. By default, LittleBitLinear uses the original SVD-only initialization. To enable LittleBit-2 (Joint-ITQ), pass --use_itq True.

CUDA_VISIBLE_DEVICES=0 python -m main
--model_id meta-llama/Llama-2-7b-hf
--dataset c4_wiki
--save_dir ./outputs/Llama-2-7b-LittleBit-2
--num_train_epochs 5.0
--per_device_train_batch_size 4
--lr 4e-05
--warmup_ratio 0.02
--report wandb
--quant_func SmoothSign
--quant_mod LittleBitLinear
--residual True
--eff_bit 1.0
--kv_factor 1.0
--min_split_dim 8
--l2l_loss_scale 10.0

Opt-in to LittleBit-2 initialization

--use_itq True

Multi-GPU with DeepSpeed

deepspeed --num_gpus=4 main.py
--model_id meta-llama/Llama-2-7b-hf
--dataset c4_wiki
--save_dir ./outputs/Llama-2-7b-LittleBit-2
--ds_config_path configs/zero3.json
--num_train_epochs 5.0
--per_device_train_batch_size 4
--lr 4e-05
--report wandb
--quant_func SmoothSign
--quant_mod LittleBitLinear
--residual True
--eff_bit 1.0
--kv_factor 1.0
--min_split_dim 8

Evaluation

Evaluate a local checkpoint or a model hosted on the Hugging Face Hub.

From a local directory

CUDA_VISIBLE_DEVICES=0 python eval.py
--model_id ./outputs/Llama-2-7b-LittleBit-2
--seqlen 2048
--ppl_task wikitext2,c4
--zeroshot_task boolq,piqa,hellaswag,winogrande,arc_easy,arc_challenge,openbookqa

From the Hugging Face Hub

CUDA_VISIBLE_DEVICES=0 python eval.py
--model_id username/littlebit-llama-7b-0.1bpw
--seqlen 2048
--ppl_task wikitext2

Legacy Checkpoints

Older checkpoints may not include littlebit_config.json. In that case, pass the quantization arguments explicitly:

CUDA_VISIBLE_DEVICES=0 python eval.py
--model_id ./outputs/Legacy-Llama-2-7b
--quant_func SmoothSign
--quant_mod LittleBitLinear
--split_dim 1024

Parameter loading priority:

If you find this work useful, please cite:

@inproceedings{lee2026littlebit2, title={LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment}, author={Lee, Banseok and Kim, Youngmin}, booktitle={Proceedings of the 43rd International Conference on Machine Learning}, year={2026} }

@inproceedings{lee2025littlebit, title={LittleBit: Ultra Low-Bit Quantization via Latent Factorization}, author={Lee, Banseok and Kim, Dongkyu and You, Youngcheon and Kim, Youngmin}, booktitle={Advances in Neural Information Processing Systems}, year={2025} }

License

This project is licensed under the CC BY-NC 4.0 license.