diff --git a/gemma/gm/text/__init__.py b/gemma/gm/text/__init__.py
index c6f0df80..620b6cb3 100644
--- a/gemma/gm/text/__init__.py
+++ b/gemma/gm/text/__init__.py
@@ -33,12 +33,12 @@
   from gemma.gm.text._tool_sampler import ToolSampler
 
   # Sampling methods
-  # TODO(mblondel): Add nucleus sampling
   from gemma.gm.text._sampling import SamplingMethod
   from gemma.gm.text._sampling import Greedy
   from gemma.gm.text._sampling import RandomSampling
   from gemma.gm.text._sampling import TopkSampling
   from gemma.gm.text._sampling import TopPSampling
+  from gemma.gm.text._sampling import MinPSampling
 
   # Other utils
   # from gemma.gm.text import _template as template
diff --git a/gemma/gm/text/_sampling.py b/gemma/gm/text/_sampling.py
index 9202d563..f1cc97e7 100644
--- a/gemma/gm/text/_sampling.py
+++ b/gemma/gm/text/_sampling.py
@@ -116,3 +116,41 @@ def get_next_tokens(self, logits: Float['... V'], rng: PRNGKey) -> Int['...']:
 
     return jax.random.categorical(rng, logits, axis=-1)
 
+
+@dataclasses.dataclass(frozen=True, kw_only=True)
+class MinPSampling(SamplingMethod):
+  """Min-p sampling.
+
+  Min-p sampling keeps all tokens whose probability is at least
+  `min_p` times the probability of the most likely token. Unlike top-p
+  which uses an absolute cumulative threshold, min-p adapts the number
+  of candidate tokens based on model confidence: when the model is
+  confident (one token dominates), fewer alternatives are kept; when
+  uncertain, more tokens remain in the candidate set.
+
+  Reference: https://arxiv.org/abs/2407.01082
+  """
+
+  min_p: float = 0.1
+  temperature: float = 1.0
+
+  @typechecked
+  def get_next_tokens(self, logits: Float['... V'], rng: PRNGKey) -> Int['...']:
+    # temperature scaling
+    logits = logits / self.temperature
+
+    if self.min_p > 0.0:
+      probs = jax.nn.softmax(logits, axis=-1)
+
+      # Compute the threshold: min_p * max probability in each batch.
+      max_prob = jnp.max(probs, axis=-1, keepdims=True)
+      threshold = self.min_p * max_prob
+
+      # Mask out tokens below the threshold.
+      logits = jnp.where(
+          probs < threshold,
+          jnp.finfo(logits.dtype).min,
+          logits,
+      )
+
+    return jax.random.categorical(rng, logits, axis=-1)
diff --git a/gemma/gm/text/_sampling_test.py b/gemma/gm/text/_sampling_test.py
index 286a49d6..1a9fc953 100644
--- a/gemma/gm/text/_sampling_test.py
+++ b/gemma/gm/text/_sampling_test.py
@@ -79,3 +79,39 @@ def test_top1_sampling_matches_greedy_sampling():
   tokens_top1 = top1_sampling.get_next_tokens(logits, rng)
   np.testing.assert_array_equal(tokens_greedy, tokens_top1)
 
+
+def test_minp_sampling():
+  sampling = gm.text.MinPSampling(min_p=0.1)
+  rng = jax.random.PRNGKey(0)
+  batch_size = 2
+  vocab_size = 5
+  logits = jax.random.normal(rng, shape=(batch_size, vocab_size))
+  tokens = sampling.get_next_tokens(logits, rng)
+  assert tokens.shape == (batch_size,)
+
+
+def test_minp_sampling_with_skewed_logits():
+  """With highly skewed logits, min-p should only keep the dominant token."""
+  sampling = gm.text.MinPSampling(min_p=0.5)
+  rng = jax.random.PRNGKey(0)
+  # Token 0 has probability ~0.99, others are negligible.
+  logits = jax.numpy.array([
+      [10.0, 0.0, 0.0, 0.0, 0.0],
+  ])
+  tokens = sampling.get_next_tokens(logits, rng)
+  # With min_p=0.5, threshold = 0.5 * 0.99 ≈ 0.49.
+  # Only token 0 (prob ~0.99) exceeds the threshold.
+  np.testing.assert_array_equal(tokens, [0])
+
+
+def test_minp_zero_disables_filtering():
+  """With min_p=0, all tokens should be candidates (no filtering)."""
+  sampling_off = gm.text.MinPSampling(min_p=0.0, temperature=1.0)
+  sampling_rand = gm.text.RandomSampling(temperature=1.0)
+  rng = jax.random.PRNGKey(42)
+  logits = jax.random.normal(rng, shape=(100, 5))
+  # With min_p=0, MinPSampling should behave like RandomSampling.
+  tokens_minp = sampling_off.get_next_tokens(logits, rng)
+  tokens_rand = sampling_rand.get_next_tokens(logits, rng)
+  np.testing.assert_array_equal(tokens_minp, tokens_rand)
+