# Source: https://huggingface.co/docs/bitsandbytes/v0.49.1/explanations/resources.md

# Papers, related resources & how to cite

The below academic work is ordered in reverse chronological order.

## [SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression (Jun 2023)](https://arxiv.org/abs/2306.03078)

Authors: Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh

- [Twitter summary thread](https://twitter.com/Tim_Dettmers/status/1666076553665744896)

```
@article{dettmers2023spqr,
  title={SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression},
  author={Dettmers, Tim and Svirschevski, Ruslan and Egiazarian, Vage and Kuznedelev, Denis and Frantar, Elias and Ashkboos, Saleh and Borzunov, Alexander and Hoefler, Torsten and Alistarh, Dan},
  journal={arXiv preprint arXiv:2306.03078},
  year={2023}
}
```

## [QLoRA: Efficient Finetuning of Quantized LLMs (May 2023)](https://arxiv.org/abs/2305.14314)
Authors: Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer

- [Video](https://www.youtube.com/watch?v=y9PHWGOa8HA&ab_channel=LondonMachineLearningMeetup)
- [Twitter summary thread](https://twitter.com/Tim_Dettmers/status/1661379354507476994)

```
@article{dettmers2023qlora,
  title={Qlora: Efficient finetuning of quantized llms},
  author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:2305.14314},
  year={2023}
}
```

## [The case for 4-bit precision: k-bit Inference Scaling Laws (Dec 2022)](https://arxiv.org/abs/2212.09720)
Authors: Tim Dettmers, Luke Zettlemoyer

- [Video](https://www.youtube.com/watch?v=odlQa6AE1gY&ab_channel=TheInsideView)
- [Twitter summary thread](https://twitter.com/Tim_Dettmers/status/1605209171758284805)

```
@inproceedings{dettmers2023case,
  title={The case for 4-bit precision: k-bit inference scaling laws},
  author={Dettmers, Tim and Zettlemoyer, Luke},
  booktitle={International Conference on Machine Learning},
  pages={7750--7774},
  year={2023},
  organization={PMLR}
}
```

## [LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale (Nov 2022)](https://arxiv.org/abs/2208.07339) [[llm-int8]]
Authors: Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer

- [LLM.int8() Blog Post](https://huggingface.co/blog/hf-bitsandbytes-integration)
- [LLM.int8() Emergent Features Blog Post](https://timdettmers.com/2022/08/17/llm-int8-and-emergent-features/)
- [Introduction to Weight Quantization](https://towardsdatascience.com/introduction-to-weight-quantization-2494701b9c0c)
- [Poster](https://twitter.com/Tim_Dettmers/status/1598351301942951937)

```
@article{dettmers2022llm,
  title={Llm. int8 (): 8-bit matrix multiplication for transformers at scale},
  author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:2208.07339},
  year={2022}
}
```

## [8-bit Optimizers via Block-wise Quantization (Oct 2021)](https://arxiv.org/abs/2110.02861)
Authors: Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer

- [Video](https://www.youtube.com/watch?v=IxrlHAJtqKE)
- [Twitter summary thread](https://twitter.com/Tim_Dettmers/status/1446472128979562499)

```
@article{DBLP:journals/corr/abs-2110-02861,
  author       = {Tim Dettmers and
                  Mike Lewis and
                  Sam Shleifer and
                  Luke Zettlemoyer},
  title        = {8-bit Optimizers via Block-wise Quantization},
  journal      = {CoRR},
  volume       = {abs/2110.02861},
  year         = {2021},
  url          = {https://arxiv.org/abs/2110.02861},
  eprinttype    = {arXiv},
  eprint       = {2110.02861},
  timestamp    = {Thu, 21 Oct 2021 16:20:08 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2110-02861.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```