
---

**Default Text Generation Configuration**  
In text generation tasks, the decoding strategy is defined by the model's generation configuration. This configuration governs how the model generates text by controlling factors like the maximum output length, token selection, and stopping criteria.

### Default Generation Behavior
When you use a pre-trained model for inference, either directly or within a pipeline, the model automatically applies a default generation configuration through its **`generate()`** method. This default configuration is set under the hood and can be modified later by passing a custom configuration.

#### Default Configuration Includes:
- **Maximum Length**: The default generation configuration limits the combined input and output to a maximum of 20 tokens to avoid resource limitations.
- **Decoding Strategy**: The default decoding strategy is **greedy search**, where the model picks the most probable next token at each step. This works well for shorter outputs, but it can produce repetitive results for longer sequences.
  
### Inspecting the Generation Configuration
When loading a model explicitly, you can inspect the model's generation configuration. Here's how you can access it:

```python
from transformers import AutoModelForCausalLM

# Load pre-trained model
model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")

# Access the generation configuration
print(model.generation_config)
```

**Output Example**:
```python
GenerationConfig {
  "bos_token_id": 50256,
  "eos_token_id": 50256
}
```

In this output, `bos_token_id` (beginning of sequence) and `eos_token_id` (end of sequence) are displayed, but other default values (like max output length, etc.) are not listed unless they differ from the standard configuration.

### Decoding Strategies
The default decoding strategy used by models is **greedy search**. This method selects the token with the highest probability at each step, ensuring deterministic and coherent output. However, this strategy can result in repetitive text when generating longer sequences.

For tasks that require longer or more varied outputs, you may want to modify the decoding strategy, such as by using **sampling**, **top-k sampling**, or **beam search**, to reduce repetition and generate more natural text.

### Example Default Behavior:
If you generate text using the model, the default strategy will be applied unless you explicitly specify custom settings:

```python
# Generating text with the default configuration
input_text = "Once upon a time"
inputs = model.tokenizer(input_text, return_tensors="pt")

# Generate output using the default configuration
generated_output = model.generate(inputs["input_ids"], max_length=50)

# Decode the output
print(model.tokenizer.decode(generated_output[0], skip_special_tokens=True))
```

### Conclusion
By default, the generation configuration of a pre-trained model uses a **greedy search** strategy and limits the output length to prevent excessive resource usage. Although this works well for smaller text generation tasks, you may need to adjust the configuration for more complex outputs. Customizing decoding strategies and adjusting model configurations helps improve text quality for longer sequences and diverse applications.

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