diff --git a/server/text_generation_server/models/vectorized_causal_lm.py b/server/text_generation_server/models/vectorized_causal_lm.py
index 44118347..5a11ef2b 100644
--- a/server/text_generation_server/models/vectorized_causal_lm.py
+++ b/server/text_generation_server/models/vectorized_causal_lm.py
@@ -216,9 +216,13 @@ class VectorizedNextTokenChooser:
         else:
             self.typical_p=None
 
-        self.do_sample = any(do_sample)
-        if self.do_sample and not all(do_sample):
-            raise NotImplementedError("Mixed greedy and probabilistic sampling not supported")
+        num_do_sample=sum(do_sample)
+        self.do_sample = num_do_sample>0
+        if self.do_sample and num_do_sample<self.batch_size:
+            # Mixed greedy and probabilistic sampling. Compute both and pick the right one.
+            self.do_sample_v=torch.tensor(do_sample, dtype=torch.bool, device=device)
+        else:
+            self.do_sample_v=None
 
     def _standardize(self, values, default):
         if isinstance(values, list):
@@ -289,7 +293,10 @@ class VectorizedNextTokenChooser:
         logprobs = torch.log_softmax(scores, dim=-1)
 
         if self.do_sample:
-            raise NotImplementedError()
+            probs = torch.nn.functional.softmax(scores, -1)
+            next_token_ids = torch.multinomial(probs, num_samples=1)
+            if self.do_sample_v is not None:
+                next_token_ids=torch.where(self.do_sample_v, next_token_ids,torch.argmax(scores, dim=-1))
         else:
             next_token_ids = torch.argmax(scores, dim=-1)