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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).
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.
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}.
11 mar 2024 · Existing Quantization Approaches Vs BitNet 1.58bit. Most quantization algorithms present in the market requires a pretrained model in full precision. And one would apply techniques such as Post Training Quantization and Quantization Aware Training for such algorithms to work effectively.
Here is the commands to run the evaluation: pip install lm-eval==0.3.0. python eval_ppl.py --hf_path 1bitLLM/bitnet_b1_58-3B --seqlen 2048. python eval_task.py --hf_path 1bitLLM/bitnet_b1_58-3B \ --batch_size 1 \ --tasks \ --output_path result.json \ --num_fewshot 0 \ --ctx_size 2048. Downloads last month. 10,408. Safetensors. 729M params.
26 mar 2024 · Unlike its predecessor, BitNet b1.58 replaces the conventional nn.Linear layers with BitLinear layers, leveraging 1.58-bit weights and 8-bit activations.
29 lut 2024 · BitNet b1.58 emerges as a solution, utilizing 1-bit ternary parameters to dramatically lighten the load on computational resources while maintaining high model performance. This section will...
