### **Beam-Search Decoding in Text Generation**

**Beam-search decoding** is a more advanced strategy than greedy search. Instead of only keeping the most probable token at each step, beam search keeps track of multiple hypotheses (or candidate sequences) simultaneously. At each step, beam search explores several possible continuations and selects the hypothesis that has the highest overall probability across the entire sequence, rather than selecting just the most probable next token.

This approach allows the model to consider a wider range of potential sequences, including those that may start with lower-probability tokens but end up forming a more coherent or meaningful sequence overall. It is particularly useful when the output requires careful consideration of the entire sequence, such as in machine translation or text generation.

### **Key Parameters for Beam-Search Decoding:**
- **`num_beams`**: This parameter determines the number of hypotheses (beams) to keep track of. A larger number of beams increases the computational cost but can lead to more accurate or diverse outputs.
- **`max_new_tokens`**: Specifies the maximum number of tokens to generate beyond the prompt.
  
By setting **`num_beams`** to a value greater than 1, you enable beam-search decoding, which retains multiple candidate sequences and selects the best overall one.

### **How Beam Search Works:**
- **Greedy Search**: Chooses the token with the highest probability at each step, leading to deterministic but often repetitive sequences.
- **Beam Search**: Expands the search space by maintaining multiple hypotheses at each step, leading to potentially more diverse and accurate sequences.

### **Example Code for Beam-Search Decoding:**

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Define the checkpoint and tokenizer
checkpoint = "openai-community/gpt2-medium"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)

# Define the prompt and prepare inputs
prompt = "It is astonishing how one can"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate text using beam-search decoding
outputs = model.generate(**inputs, num_beams=5, max_new_tokens=50)

# Decode the generated text
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(generated_text)
```

### **Explanation:**
- **`num_beams=5`**: Specifies that beam search should maintain 5 hypotheses at each step, allowing the model to explore multiple possible continuations of the prompt.
- **`max_new_tokens=50`**: Limits the generated text to 50 tokens beyond the prompt.
- **Prompt**: `"It is astonishing how one can"` is the input for the model to generate a continuation.

### **Generated Output:**
```plaintext
It is astonishing how one can have such a profound impact on the lives of so many people in such a short period of time.
He added: "I am very proud of the work I have been able to do in the last few years."
"I have"
```

### **When to Use Beam-Search Decoding:**
- **Higher-Quality Text**: Beam search is particularly useful when you need high-quality, coherent text generation. It is capable of producing more accurate and fluent outputs than greedy search, especially for complex tasks.
- **Diverse Options**: When you want to ensure the model generates multiple possible continuations, beam search helps by considering multiple hypotheses.
- **Balancing Efficiency and Quality**: Beam search is a good choice for generating text where you want a good balance between efficiency (compared to exhaustive search) and quality.

### **Advantages of Beam-Search Decoding:**
- **Higher-Quality Sequences**: Beam search is better at generating coherent and high-quality sequences because it doesn’t rely on greedy choices at each step.
- **Exploration of Multiple Hypotheses**: It allows exploration of several potential sequences, improving the chance of finding an optimal output.

Beam-search decoding helps ensure that the generated text maintains fluency and coherence, especially when the text needs to reflect more complex relationships or structure, making it a preferred strategy for many NLP tasks.