✨ Add example script for generating options with ollama.generate()

examples/generate-with-options.py

@@ -0,0 +1,151 @@
+"""
+Example demonstrating how to use options with ollama.generate() to control model behavior.
+
+This example shows how different options affect:
+- Creativity and randomness (temperature, top_p)
+- Response length (num_predict)
+- Reproducibility (seed)
+- Stop conditions (stop sequences)
+- Repetition control (repeat_penalty)
+"""
+
+import ollama
+from ollama import Options
+
+
+def main():
+    model = 'llama3.2'
+    base_prompt = 'Write a creative opening line for a sci-fi novel about space exploration'
+
+    print("=== Ollama Generate Options Examples ===\n")
+
+    # Example 1: Creative, longer response
+    print("1. Creative Response (high temperature, diverse sampling)")
+    print("-" * 55)
+
+    creative_options = Options(
+        temperature=1.0,        # High creativity
+        top_p=0.9,             # Allow diverse word choices
+        num_predict=80,        # Longer response
+        repeat_penalty=1.1,    # Avoid repetition
+        top_k=40              # Consider top 40 tokens
+    )
+
+    try:
+        response = ollama.generate(
+            model=model,
+            prompt=base_prompt,
+            options=creative_options
+        )
+        print(f"Response: {response['response']}")
+        print(f"Tokens generated: {response.get('eval_count', 'N/A')}")
+    except Exception as e:
+        print(f"Error: {e}")
+
+    print("\n" + "="*60 + "\n")
+
+    # Example 2: Focused, deterministic response
+    print("2. Focused Response (low temperature, reproducible)")
+    print("-" * 55)
+
+    focused_options = {
+        'temperature': 0.3,     # Low creativity, more focused
+        'top_p': 0.5,          # More conservative word choices
+        'num_predict': 40,     # Shorter response
+        'seed': 42,            # Reproducible results
+        'repeat_penalty': 1.05  # Light repetition penalty
+    }
+
+    try:
+        response = ollama.generate(
+            model=model,
+            prompt=base_prompt,
+            options=focused_options
+        )
+        print(f"Response: {response['response']}")
+        print(f"Tokens generated: {response.get('eval_count', 'N/A')}")
+        print("Note: Run this again with the same seed for identical results!")
+    except Exception as e:
+        print(f"Error: {e}")
+
+    print("\n" + "="*60 + "\n")
+
+    # Example 3: Controlled response with stop sequences
+    print("3. Controlled Response (with stop sequences)")
+    print("-" * 55)
+
+    list_prompt = "List the top 5 benefits of renewable energy sources:"
+
+    controlled_options = Options(
+        temperature=0.7,
+        num_predict=200,
+        stop=['\n6.', 'In conclusion', 'However', 'Additionally'],  # Stop at these phrases
+        repeat_penalty=1.1
+    )
+
+    try:
+        response = ollama.generate(
+            model=model,
+            prompt=list_prompt,
+            options=controlled_options
+        )
+        print(f"Prompt: {list_prompt}")
+        print(f"Response: {response['response']}")
+        print("Note: Generation stopped early due to stop sequences")
+    except Exception as e:
+        print(f"Error: {e}")
+
+    print("\n" + "="*60 + "\n")
+
+    # Example 4: Comparison of different temperatures
+    print("4. Temperature Comparison")
+    print("-" * 55)
+
+    temperatures = [0.1, 0.7, 1.2]
+    comparison_prompt = "Describe the color blue in one sentence"
+
+    for temp in temperatures:
+        print(f"\nTemperature {temp}:")
+
+        try:
+            response = ollama.generate(
+                model=model,
+                prompt=comparison_prompt,
+                options={
+                    'temperature': temp,
+                    'num_predict': 30,
+                    'seed': 123  # Same seed for fair comparison
+                }
+            )
+            print(f"  {response['response'].strip()}")
+        except Exception as e:
+            print(f"  Error: {e}")
+
+    print("\n" + "="*60 + "\n")
+
+    # Example 5: Context window demonstration (simplified)
+    print("5. Context Window Control (simplified)")
+    print("-" * 55)
+
+    prompt = "Given this background: Solar power uses sunlight, wind power uses wind, hydroelectric uses water, geothermal uses Earth's heat. Which is best for a desert?"
+    options = {
+        'temperature': 0.5,
+        'num_ctx': 512,
+        'num_predict': 50
+    }
+    try:
+        response = ollama.generate(
+            model=model,
+            prompt=prompt,
+            options=options
+        )
+        print(f"Prompt: {prompt}")
+        print(f"Response: {response['response']}")
+    except Exception as e:
+        print(f"Error: {e}")
+    print("\n" + "="*60)
+    print("Examples completed! Try modifying the options to see different behaviors.")
+
+
+if __name__ == '__main__':
+    main()