diff --git a/README.md b/README.md
index 582f1154a..dbe36b38d 100644
--- a/README.md
+++ b/README.md
@@ -82,7 +82,8 @@ from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
 ```
 
 ```python
-flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False, window_size=(-1, -1)):
+flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False,
+                          window_size=(-1, -1), alibi_slopes=None):
 """dropout_p should be set to 0.0 during evaluation
 If Q, K, V are already stacked into 1 tensor, this function will be faster than
 calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
@@ -96,13 +97,16 @@ Arguments:
         Default to 1 / sqrt(headdim).
     causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
     window_size: (left, right). If not (-1, -1), implements sliding window local attention.
+    alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|) is added to
+        the attention score of query i and key j.
 Return:
     out: (batch_size, seqlen, nheads, headdim).
 """
 ```
 
 ```python
-flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False, window_size=(-1, -1)):
+flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False,
+                window_size=(-1, -1), alibi_slopes=None):
 """dropout_p should be set to 0.0 during evaluation
 Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
 than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
@@ -121,6 +125,9 @@ Arguments:
         Default to 1 / sqrt(headdim).
     causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
     window_size: (left, right). If not (-1, -1), implements sliding window local attention.
+    alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
+        (-alibi_slope * |i + seqlen_k - seqlen_q - j|)
+        is added to the attention score of query i and key j.
 Return:
     out: (batch_size, seqlen, nheads, headdim).
 """
@@ -141,6 +148,7 @@ def flash_attn_with_kvcache(
     causal=False,
     window_size=(-1, -1),  # -1 means infinite context window
     rotary_interleaved=True,
+    alibi_slopes=None,
 ):
     """
     If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from
@@ -183,10 +191,9 @@ def flash_attn_with_kvcache(
             If True, rotary embedding will combine dimensions 0 & 1, 2 & 3, etc. If False,
             rotary embedding will combine dimensions 0 & rotary_dim / 2, 1 & rotary_dim / 2 + 1
             (i.e. GPT-NeoX style).
-        num_splits: int. If > 1, split the key/value into this many chunks along the sequence.
-           If num_splits == 1, we don't split the key/value. If num_splits == 0, we use a heuristic
-           to automatically determine the number of splits.
-           Don't change this unless you know what you are doing.
+        alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
+            (-alibi_slope * |i + seqlen_k - seqlen_q - j|)
+            is added to the attention score of query i and key j.
 
     Return:
         out: (batch_size, seqlen, nheads, headdim).
@@ -262,6 +269,10 @@ Implement sliding window attention (i.e., local attention). Thanks to [Mistral
 AI](https://mistral.ai/) and in particular Timothée Lacroix for this
 contribution. Sliding window was used in the [Mistral 7B](https://mistral.ai/news/announcing-mistral-7b/) model.
 
+### 2.4: ALiBi (attention with linear bias)
+
+Implement ALiBi (Press et el., 2021). Thanks to Sanghun Cho from Kakao Brain for this contribution.
+
 ## Performance
 
 We present expected speedup (combined forward + backward pass) and memory savings from using FlashAttention against PyTorch standard attention, depending on sequence length, on different GPUs (speedup depends on memory bandwidth - we see more speedup on slower GPU memory).