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authorDmitry Baranchuk <dmitrybaranchuk@gmail.com>2022-09-10 19:33:21 -0700
committerGitHub <noreply@github.com>2022-09-10 19:33:21 -0700
commit843ad0631c65eabc7f64e80906ecf5482cc1a036 (patch)
tree07ab541ec59ab3474a711c155daa118fc0ae6864 /README.md
parent8d34d36f150b0fd4914cdb56d4e3bda34c029ccc (diff)
parent2e630b55f51d454f3bd723dffda68a07ef93190c (diff)
Merge pull request #1 from TimDettmers/main
Update main branch
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@@ -23,12 +23,12 @@ Resources:
1. Comment out torch.nn.Linear: ``#linear = torch.nn.Linear(...)``
2. Add bnb 8-bit linear light module: ``linear = bnb.nn.Linear8bitLt(...)`` (base arguments stay the same)
3. There are two modes:
- - Mixed 8-bit training with 16-bit main weights. Pass the argument ``use_fp16_weights=True`` (default)
- - Int8 inference. Pass the argument ``use_fp16_weights=False``
+ - Mixed 8-bit training with 16-bit main weights. Pass the argument ``has_fp16_weights=True`` (default)
+ - Int8 inference. Pass the argument ``has_fp16_weights=False``
4. To use the full LLM.int8() method, use the ``threshold=k`` argument. We recommend ``k=6.0``.
```python
# LLM.int8()
-linear = bnb.nn.Linear8bitLt(dim1, dim2, bias=True, use_fp16_weights=False, threshold=6.0)
+linear = bnb.nn.Linear8bitLt(dim1, dim2, bias=True, has_fp16_weights=False, threshold=6.0)
# inputs need to be fp16
out = linear(x.to(torch.float16))
```
@@ -115,7 +115,8 @@ We thank Fabio Cannizzo for his work on [FastBinarySearch](https://github.com/fa
## How to cite us
If you found this library and found LLM.int8() useful, please consider citing our work:
-```
+
+```bibtex
@article{dettmers2022llmint8,
title={LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale},
author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},
@@ -124,8 +125,9 @@ If you found this library and found LLM.int8() useful, please consider citing ou
}
```
-For 8-bit optimizers or quantization routines please consider citing the following work.
-```
+For 8-bit optimizers or quantization routines, please consider citing the following work:
+
+```bibtex
@article{dettmers2022optimizers,
title={8-bit Optimizers via Block-wise Quantization},
author={Dettmers, Tim and Lewis, Mike and Shleifer, Sam and Zettlemoyer, Luke},