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# How to reduce repetition with `frequency_penalty`

> Helps you control how often the model repeats words or phrases

The `frequency_penalty` parameter helps you **control how often the model repeats words or phrases**.

* **Positive values** → penalize tokens that have already appeared → model is **less likely to repeat itself**.
* **Negative values** → encourage more repetition (rarely used).

### How it works:

* After each token is generated, the model adjusts the probability of generating the **same tokens again**, based on how often they have already been used.
* This helps you create **more varied and natural-sounding text**.

---

### Parameter details:

| Parameter           | Type   | Range       | Default |
| ------------------- | ------ | ----------- | ------- |
| `frequency_penalty` | Double | -2.0 to 2.0 | 0.0     |

---

### When to use `frequency_penalty`:

| Scenario                                 | Suggested Value |
| ---------------------------------------- | --------------- |
| Model is repeating itself too much       | 0.5 to 1.0      |
| Long-form content, essays, articles      | 0.5 to 1.0      |
| Dialogue-heavy apps (avoid echoing user) | 0.5 to 1.0      |
| Want creative, varied phrasing           | 0.5 to 1.5      |

First, install the SDK:

```bash
pip install -Uqq sarvamai
```

Then use the following Python code:

```python
from sarvamai import SarvamAI

# Initialize the SarvamAI client with your API key
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")

# Example: Apply frequency_penalty to avoid repetition
response = client.chat.completions(
    model="sarvam-105b",
    messages=[
        {"role": "system", "content": "You are a travel blogger assistant."},
        {"role": "user", "content": "Write a paragraph about the beaches of Goa."}
    ],
    frequency_penalty=1.0  # Reduce repetitive phrases
)

# Receive assistant's reply as output
print(response.choices[0].message.content)
```

> Helps you control how often the model repeats words or phrases