diff --git a/README.md b/README.md
index d91dc1356..3c90b75bb 100644
--- a/README.md
+++ b/README.md
@@ -195,6 +195,8 @@ from pydantic import BaseModel, constr
 import outlines.models as models
 import outlines.text.generate as generate
 
+import torch
+
 
 class Weapon(str, Enum):
     sword = "sword"
@@ -219,20 +221,38 @@ class Character(BaseModel):
     strength: int
 
 
-model = models.transformers("gpt2")
-sequence = generate.json(model, Character)("Give me a character description")
+model = models.transformers("gpt2", device="cuda")
+
+# Construct guided sequence generator
+generator = generate.json(model, Character, max_tokens=100)
+
+# Draw a sample
+rng = torch.Generator(device="cuda")
+rng.manual_seed(789001)
+
+sequence = generator("Give me a character description", rng=rng)
+print(sequence)
+# {
+#   "name": "clerame",
+#   "age": 7,
+#   "armor": "plate",
+#   "weapon": "mace",
+#   "strength": 4171
+# }
+
+sequence = generator("Give me an interesting character description", rng=rng)
 print(sequence)
 # {
-#   "name": "ranbelt",
-#   "age": 26,
+#   "name": "piggyback",
+#   "age": 23,
 #   "armor": "chainmail",
-#   "weapon": "bow",
-#   "strength": 5
+#   "weapon": "sword",
+#   "strength": 0
 # }
 
 parsed = Character.model_validate_json(sequence)
 print(parsed)
-# name='ranbelt' age=26 armor=<Armor.chainmail: 'chainmail'> weapon=<Weapon.bow: 'bow'> strength=5
+# name='piggyback' age=23 armor=<Armor.chainmail: 'chainmail'> weapon=<Weapon.sword: 'sword'> strength=0
 ```
 
 The method works with union types, optional types, arrays, nested schemas, etc. Some field constraints are [not supported yet](https://github.com/normal-computing/outlines/issues/215), but everything else should work.
diff --git a/outlines/text/generate/sequence.py b/outlines/text/generate/sequence.py
index 77edcfc0a..699f12c5e 100644
--- a/outlines/text/generate/sequence.py
+++ b/outlines/text/generate/sequence.py
@@ -199,6 +199,7 @@ def __call__(
 
         if rng is None:
             rng = torch.Generator(device=self.device)
+            rng.seed()
 
         num_prompt_tokens = token_ids.shape[-1]