diff --git a/CHANGELOG.md b/CHANGELOG.md
index d3aa6caec..25c2b253f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,8 +20,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added MMLU downstream evaluation tasks, with prompt variations.
 - Added support for PyTorch v2.2.
 - Added ability to show logs from all ranks
-
-
+- Added option for QKV clipping.
 
 ### Changed
 
diff --git a/olmo/config.py b/olmo/config.py
index c0f26b08b..66e176a7e 100644
--- a/olmo/config.py
+++ b/olmo/config.py
@@ -243,6 +243,11 @@ class ModelConfig(BaseConfig):
     The number of self-attention heads.
     """
 
+    clip_qkv: Optional[float] = None
+    """
+    Clip QKV to this value when set.
+    """
+
     n_layers: int = 12
     """
     The number of layers/blocks.
diff --git a/olmo/model.py b/olmo/model.py
index a11eceb71..bd7be3097 100644
--- a/olmo/model.py
+++ b/olmo/model.py
@@ -452,6 +452,10 @@ def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache):
             )
             self.q_norm = LayerNormBase.build(config, elementwise_affine=config.attention_layer_norm_with_affine)
 
+        # Make sure QKV clip coefficient is positive, otherwise it's not well-defined.
+        if config.clip_qkv is not None:
+            assert config.clip_qkv > 0
+
         # Activation function.
         self.act = Activation.build(config)
         assert (self.act.output_multiplier * self.hidden_size) % 1 == 0
@@ -680,11 +684,14 @@ def forward(
         #  - for multi-query attn q: (batch_size, seq_len, d_model)
         #                      k, v: (batch_size, seq_len, d_model // n_heads)
         if self._activation_checkpoint_fn is not None:
-            q, k, v = self.att_proj(self._activation_checkpoint_fn(self.attn_norm, x)).split(
-                self.fused_dims, dim=-1
-            )
+            qkv = self.att_proj(self._activation_checkpoint_fn(self.attn_norm, x))
         else:
-            q, k, v = self.att_proj(self.attn_norm(x)).split(self.fused_dims, dim=-1)
+            qkv = self.att_proj(self.attn_norm(x))
+
+        if self.config.clip_qkv is not None:
+            qkv.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+
+        q, k, v = qkv.split(self.fused_dims, dim=-1)
 
         # Get attention scores.
         if self._activation_checkpoint_fn is not None:
@@ -780,6 +787,11 @@ def forward(
         else:
             q, k, v, ff = self.fused_attn_ff_proj(self.norm(x)).split(self.fused_dims, dim=-1)
 
+        if self.config.clip_qkv is not None:
+            q.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+            k.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+            v.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+
         # Get attention scores.
         # shape: (B, T, C)
         if self._activation_checkpoint_fn is not None:
@@ -896,6 +908,11 @@ def forward(
         k = self.k_proj(x_normed)
         v = self.v_proj(x_normed)
 
+        if self.config.clip_qkv is not None:
+            q.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+            k.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+            v.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+
         # Get attention scores.
         att, cache = self.attention(q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache)