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_itqfor 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.
The codebase currently supports:
- OPT
- Llama and Llama 2/3
- Phi-4
- Qwen2.5 and QwQ
- Gemma 2 and Gemma 3
- Qwen3
We recommend Python 3.12.
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.txtImportant
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.*"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.
Single GPU
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 TrueMulti-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 8Evaluate 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 wikitext2Older 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 1024Parameter loading priority:
- Explicit CLI arguments
- littlebit_config.jsonin the model directory
- config.jsonfallback for older checkpoints
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}
}This project is licensed under the CC BY-NC 4.0 license.