diff --git a/min_p_sampling_approx.py b/min_p_sampling_approx.py
new file mode 100644
index 000000000..6c49c5ff3
--- /dev/null
+++ b/min_p_sampling_approx.py
@@ -0,0 +1,147 @@
+import google.generativeai as genai
+import collections
+import random
+import math
+from google.generativeai.types import GenerationConfig, GenerateContentResponse
+
+
+def _normalize_probs(prob_map):
+    total_prob = sum(prob_map.values())
+    if total_prob == 0:
+        num_tokens = len(prob_map)
+        if num_tokens == 0:
+            return {}
+        equal_prob = 1.0 / num_tokens
+        return {token: equal_prob for token in prob_map}
+
+    return {token: prob / total_prob for token, prob in prob_map.items()}
+
+
+def generate_content_min_p_approx(
+    model: genai.GenerativeModel,
+    prompt: str,
+    max_new_tokens: int = 50,
+    p_base: float = 0.1,
+    temperature: float = 1.0,
+    num_samples: int = 100,
+    fallback_to_most_frequent: bool = True
+) -> str:
+
+    if not (0.0 < p_base <= 1.0):
+        raise ValueError("p_base must be between 0.0 (exclusive) and 1.0 (inclusive)")
+
+    generated_sequence = prompt
+    current_prompt = prompt
+
+    print(f"Starting min_p approximation (p_base={p_base}, temp={temperature}, samples={num_samples})...")
+
+    for i in range(max_new_tokens):
+        print(f"  Step {i+1}/{max_new_tokens}: Generating candidates...")
+        try:
+            estimation_config = GenerationConfig(
+                candidate_count=num_samples,
+                max_output_tokens=1,
+                temperature=temperature
+            )
+
+            response: GenerateContentResponse = model.generate_content(
+                current_prompt,
+                generation_config=estimation_config,
+            )
+
+            if not response.candidates:
+                 print("  Warning: No candidates generated by the API. Stopping generation.")
+                 break
+
+            token_counts = collections.Counter()
+            valid_candidates = 0
+            for candidate in response.candidates:
+                if candidate.content and candidate.content.parts:
+                    try:
+                         token_text = candidate.content.parts[0].text
+                         if token_text:
+                            token_counts[token_text] += 1
+                            valid_candidates += 1
+                    except (IndexError, AttributeError, KeyError):
+                        print(f"  Warning: Skipping malformed candidate: {candidate}")
+                        continue
+                else:
+                     print(f"  Warning: Skipping empty or blocked candidate: {candidate.finish_reason}, {candidate.safety_ratings}")
+
+
+            if valid_candidates == 0:
+                print("  Error: No valid tokens found among candidates. Stopping generation.")
+                break
+
+            estimated_probs = {token: count / valid_candidates for token, count in token_counts.items()}
+
+            if not estimated_probs:
+                 print("  Error: Estimated probabilities are empty. Stopping generation.")
+                 break
+
+            p_max = max(estimated_probs.values())
+            p_threshold = p_base * p_max
+
+            filtered_probs = {
+                token: prob for token, prob in estimated_probs.items() if prob >= p_threshold
+            }
+
+            chosen_token = None
+            if not filtered_probs:
+                print("  Warning: Min_p filter resulted in empty set.")
+                if fallback_to_most_frequent:
+                    chosen_token = token_counts.most_common(1)[0][0]
+                    print(f"    Falling back to most frequent token: '{chosen_token}'")
+                else:
+                    print("  Error: No tokens passed min_p filter and no fallback. Stopping.")
+                    break
+            else:
+                normalized_filtered_probs = _normalize_probs(filtered_probs)
+
+                tokens = list(normalized_filtered_probs.keys())
+                probabilities = list(normalized_filtered_probs.values())
+                chosen_token = random.choices(tokens, weights=probabilities, k=1)[0]
+
+            if chosen_token is None:
+                 print("  Error: Could not select a token. Stopping generation.")
+                 break
+
+            generated_sequence += chosen_token
+            current_prompt += chosen_token
+
+        except Exception as e:
+            print(f"  Error during generation step {i+1}: {e}")
+            break
+
+    print("Finished min_p approximation.")
+    return generated_sequence
+
+if __name__ == "__main__":
+    try:
+
+        model = genai.GenerativeModel('gemini-1.5-flash-latest')
+
+        initial_prompt = "Write a short story about a curious cat exploring a futuristic city."
+
+
+        p_base_val = 0.1
+        temp_val = 1.5
+        samples_per_step = 100
+        tokens_to_generate = 100
+
+
+        generated_text = generate_content_min_p_approx(
+            model,
+            initial_prompt,
+            max_new_tokens=tokens_to_generate,
+            p_base=p_base_val,
+            temperature=temp_val,
+            num_samples=samples_per_step
+        )
+
+        print("\n--- Generated Text ---")
+        print(generated_text)
+
+    except Exception as e:
+        print(f"An error occurred during example usage: {e}")
+        print("Please ensure you have configured your API key and have access to the specified model.")
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