1-Bit LLM: The Most Efficient LLM Possible?

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I've been planning for a bitnet video for the longest time, and with the release of bitnet b1.58 2B4T gave me the perfect chance to brief you on the history of 1-bit LLM! Fun fact, the major bitnet research is mostly done by the same researchers.

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Quantifying the Capabilities of LLMs across Scale and Precision
[Paper] https://arxiv.org/abs/2405.03146v2

BitNet: Scaling 1-bit Transformers for Large Language Models
[Paper] https://arxiv.org/abs/2310.11453v1

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
[Paper] https://arxiv.org/abs/2402.17764v1

BitNet a4.8: 4-bit Activations for 1-bit LLMs
[Paper] https://arxiv.org/abs/2411.04965v1

Efficient Construction of Model Family through Progressive Training Using Model Expansion
[Paper] https://arxiv.org/abs/2504.00623v1

BitNet b1.58 2B4T Technical Report
[Paper] https://arxiv.org/abs/2504.12285
[Web Demo] https://bitnet-demo.azurewebsites.net/
[HuggingFace] https://huggingface.co/microsoft/bitn...
[Code] https://github.com/microsoft/BitNet

[Additional Recs]
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
https://arxiv.org/abs/2407.00088v2

FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
https://arxiv.org/abs/2407.07093v1

Matmul or No Matmul in the Era of 1-bit LLMs
https://arxiv.org/abs/2408.11939v2

1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs
https://arxiv.org/abs/2410.16144v2

Bitnet.cpp: Efficient Edge Inference for Ternary LLMs
https://arxiv.org/abs/2502.11880v1

Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?
https://arxiv.org/abs/2502.11895v1

(NEW!) BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs
https://arxiv.org/abs/2504.18415

(NEW!) BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation
https://arxiv.org/abs/2506.07530


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This video is supported by the kind Patrons & YouTube Members:
🙏Nous Research, Chris LeDoux, Ben Shaener, DX Research Group, Poof N' Inu, Andrew Lescelius, Deagan, Robert Zawiasa, Ryszard Warzocha, Tobe2d, Louis Muk, Akkusativ, Kevin Tai, Mark Buckler, NO U, Tony Jimenez, Ângelo Fonseca, jiye, Anushka, Asad Dhamani, Binnie Yiu, Calvin Yan, Clayton Ford, Diego Silva, Etrotta, Gonzalo Fidalgo, Handenon, Hector, Jake Disco very, Michael Brenner, Nilly K, OlegWock, Daddy Wen, Shuhong Chen, Sid_Cipher, Stefan Lorenz, Sup, tantan assawade, Thipok Tham, Thomas Di Martino, Thomas Lin, Richárd Nagyfi, Paperboy, mika, Leo, Berhane-Meskel, Kadhai Pesalam, mayssam, Bill Mangrum, nyaa


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