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1. github.com › microsoft › BitNetmicrosoft/BitNet: Official inference framework for 1-bit LLMs -...

   github.com › microsoft › BitNet

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   bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support fast and lossless inference of 1.58-bit models on CPU (with NPU and GPU support coming next).

2. huggingface.co › blog › 1_58_llm_extreme_quantizationFine-tuning LLMs to 1.58bit: extreme quantization made easy -...

   huggingface.co › blog › 1_58_llm_extreme_quantization

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   18 wrz 2024 · BitNet is an architecture introduced by Microsoft Research that uses extreme quantization, representing each parameter with only three values: -1, 0, and 1. This results in a model that uses just 1.58 bits per parameter, significantly reducing computational and memory requirements.

3. github.com › AlarioAI › BitNetAlarioAI/bitnet: Train and evaluate 1.58 bits Neural Networks -...

   github.com › AlarioAI › BitNet

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   This repository not only provides PyTorch implementations for training and evaluating 1.58-bit neural networks but also includes a unique integration where the experiments conducted automatically update a LaTeX-generated paper.

4. huggingface.co › 1bitLLM › bitnet_b1_58-large1bitLLM/bitnet_b1_58-large - Hugging Face

   huggingface.co › 1bitLLM › bitnet_b1_58-large

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   This is a reproduction of the BitNet b1.58 paper. The models are trained with RedPajama dataset for 100B tokens. The hypers, as well as two-stage LR and weight decay, are implemented as suggested in their following paper. All models are open-source in the repo.

5. huggingface.co › papers › 2402The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

   huggingface.co › papers › 2402

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   28 lut 2024 · Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}.

6. arxiv.org › pdf › 2407BitNet b1.58 Reloaded: State-of-the-art Performance Also on...

   arxiv.org › pdf › 2407

   Recently proposed methods for 1-bit and 1.58-bit quantiza-tion aware training investigate the performance and behavior of these methods in the context of large language models, finding state-of-the-art performance for models with more than 3B parameters.

7. arxiv.org › abs › 2407BitNet b1.58 Reloaded: State-of-the-art Performance Also on...

   arxiv.org › abs › 2407

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   24 cze 2024 · In this work, we investigate 1.58-bit quantization for small language and vision models ranging from 100K to 48M parameters. We introduce a variant of BitNet b1.58, which allows to rely on the median rather than the mean in the quantization process.