Update README.md

README.md

@@ -14,7 +14,7 @@ tags:
 
 PermuFormer is a small Llama-style causal language model trained on symbolic permutation tasks from algebraic combinatorics. It is intended as a specialist base model for permutation representation, reasoning, and finetuning experiments rather than as a general natural-language assistant.
 
-The model operates on a compact whitespace-tokenized vocabulary for permutations. Prompts are formulaic equations: the left side specifies a permutation task and generation begins after the `=` token.
+The model operates on a compact word-level vocabulary for permutation syntax. Training examples are stored as pre-tokenized lists of tokens; at inference time, the Hugging Face tokenizer can also consume equivalent whitespace-separated strings. Prompts are formulaic equations: the left side specifies a permutation task and generation begins after the `=` token.
 
 ## Model Details
 
@@ -78,7 +78,7 @@ print(tokenizer.decode(output_ids[0], skip_special_tokens=False))
 
 ### Prompt Format
 
-All tokens are separated by spaces. Multi-digit integers, delimiters, and task names are individual tokens. A typical example starts with `<|endoftext|>`, then a size token such as `n7`, then the task expression, then `=`.
+Training data is represented as lists of token strings. When writing prompts as plain text, separate every token with spaces. Multi-digit integers, delimiters, and task names are individual tokens. A typical example starts with `<|endoftext|>`, then a size token such as `n7`, then the task expression, then `=`.
 
 Translation example: