From e4e13db8123170e14683aa454739e2bfcff4a6e0 Mon Sep 17 00:00:00 2001
From: David Silin <DSilin17@gmail.com>
Date: Wed, 17 Aug 2022 14:39:10 -0500
Subject: [PATCH] fix param name

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 0ae3afa..1f94317 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))
 ```