Preparing Input for LLaMA: Converting Tokens into Model-Ready Format

To use LLaMA (or any large language model), the text data must be converted into a format that the model can process effectively. This involves converting tokens into numerical indices that represent each token’s position in the model’s vocabulary. Here’s how you can prepare input for LLaMA:

Steps to Convert Tokens for LLaMA

1. Tokenization:
   • First, the raw text is split into tokens (words, subwords, or characters). For example, the sentence:
     “Natural language processing is fun!”
     might be tokenized into:
     ["Natural", "language", "processing", "is", "fun", "!"]
2. Mapping Tokens to Indices:
   • Each token is assigned a unique numerical index based on its position in the model’s vocabulary.
     Example:
     • “Natural” → 345
     • “language” → 678
     • “processing” → 1234
     • “is” → 67
     • “fun” → 901
     • “!” → 450

   These indices correspond to the model’s predefined vocabulary, which is built during training.

3. Padding and Truncation:
   • LLaMA expects fixed-length input. If the input sequence is too short, it’s padded with special tokens (like <PAD>). If it’s too long, it’s truncated.
   • Example:
     If the model has a maximum sequence length of 10 tokens, and the tokenized sentence is 6 tokens long, it will be padded to match the length.
4. Adding Special Tokens:
   • LLaMA models may require special tokens like [CLS] for classification tasks or [SEP] for separating different parts of the input.
5. Format the Input for the Model:
   • The final input for the model is a tensor (numerical array) of token indices, ready for feeding into the model.

Code Example: Preparing Input for LLaMA

Here’s a simple Python example using the Hugging Face transformers library to prepare text for LLaMA:

Output (numerical indices for each token):

Conclusion

To prepare input for LLaMA, you:

1. Tokenize the text into manageable units.
2. Convert those tokens into numerical indices based on the model’s vocabulary.
3. Pad or truncate sequences as needed.
4. Format the data into tensors for the model to process.