Merge branch 'main' into remove_unused_code

1 files changed, 8 insertions, 6 deletions

diff --git a/README.md b/README.md
index 0ae3afa..eac64a5 100644
--- a/README.md
+++ b/README.md
@@ -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},