Microsoft hat gerade sein 1-Bit-LLM-Inference-Framework bitnet.cpp als Open Source veröffentlicht - und das ist ein ziemlicher Gamechanger.
Damit lassen sich Modelle mit bis zu 100B Parametern lokal auf der CPU ausführen - ohne GPUs.
Microsoft hat gerade sein 1-Bit-LLM-Inference-Framework bitnet.cpp als Open Source veröffentlicht - und das ist ein ziemlicher Gamechanger.
Damit lassen sich Modelle mit bis zu 100B Parametern lokal auf der CPU ausführen - ohne GPUs.
BitNet v2 trained from scratch with 8-bit activations matches BitNet b1.58 while BitNet v2 achieves minimal degradation when trained with native 4-bit activations, significantly reducing memory footprint and computational cost for batched inference
BitNet v2 trained from scratch with 8-bit activations matches BitNet b1.58 while BitNet v2 achieves minimal degradation when trained with native 4-bit activations, significantly reducing memory footprint and computational cost for batched inference
BitNet achieves performance on par with leading full-precision LLMs — and it’s blazingly fast⚡️⚡️uses much lower memory🎉
Everything is open-sourced, per them.
BitNet achieves performance on par with leading full-precision LLMs — and it’s blazingly fast⚡️⚡️uses much lower memory🎉
Everything is open-sourced, per them.
A lightweight pipeline that fine-tunes off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit precision (i.e., ternary weights {-1, 0, 1}) for specific downstream tasks,
A lightweight pipeline that fine-tunes off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit precision (i.e., ternary weights {-1, 0, 1}) for specific downstream tasks,
🔥 Training weights (bf16): huggingface.co/microsoft/bi...
🧰 Inference code: github.com/microsoft/bi...
Demo: bitnet-demo.azurewebsites.net
🔥 Training weights (bf16): huggingface.co/microsoft/bi...
🧰 Inference code: github.com/microsoft/bi...
Demo: bitnet-demo.azurewebsites.net
List of previous papers:
BitNet (b1): arxiv.org/abs/2310.11453
BitNet b1.58: arxiv.org/abs/2402.17764
BitNet b1.58 Tips and Code: github.com/microsoft/un...
BitNet a4.8: arxiv.org/abs/2411.04965
List of previous papers:
BitNet (b1): arxiv.org/abs/2310.11453
BitNet b1.58: arxiv.org/abs/2402.17764
BitNet b1.58 Tips and Code: github.com/microsoft/un...
BitNet a4.8: arxiv.org/abs/2411.04965
2B BitNet a4.8 trained with 2T tokens achieves 50.30% acc on MMLU, almost no degradation to BitNet b1.58.
arxiv.org/abs/2411.04965
2B BitNet a4.8 trained with 2T tokens achieves 50.30% acc on MMLU, almost no degradation to BitNet b1.58.
arxiv.org/abs/2411.04965
HTTP before Host: header?
Gopher?
HTTP before Host: header?
Gopher?
github.com/microsoft/Bi...
github.com/microsoft/Bi...
In all your life online, what’s the greatest social media you’ve ever used?
6.17x faster inference
82.2% less energy on CPUs
Supports Llama3, Falcon3, and BitNet models
You can now run 100B parameter models on your local CPU without GPUs.
6.17x faster inference
82.2% less energy on CPUs
Supports Llama3, Falcon3, and BitNet models
You can now run 100B parameter models on your local CPU without GPUs.
* BitNet
* mxfp4
* fp8
* bf16
if you forget, just look at the number. it means something, i think, usually
* BitNet
* mxfp4
* fp8
* bf16
if you forget, just look at the number. it means something, i think, usually