Machine Learning / Model Compression
Official implementation of LittleBit and LittleBit-2 for sub-1-bit LLM compression via latent factorization and quantization-aware training.
The page clearly describes an AI research/code project with a concrete purpose: compressing large language models into the sub-1-bit regime. The README gives enough detail for a public listing, including the method, supported models, installation steps, training and evaluation commands, and notes on checkpoint handling.
The snapshot does not show benchmark results, license details beyond the file listing, release status, or examples of trained models available for download. It is also hard to tell from the page alone how stable the code is for newcomers or which exact model sizes have been validated end to end.
Last evaluated just now. Current rank #812. Down 3 spots in the rankings.
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