Conversation schemas become exact token sequences

Represent conversations with explicit roles and content under a supported schema. Decide whether system instructions, user turns, assistant responses, and tool interactions are part of the task, and reject records whose roles or ordering violate that contract. A plain concatenation can erase who said what. Conversely, copying one model family's literal control tokens into another family's content can introduce unintended markers. The correct structure follows the selected model and template rather than a universal conversation spelling.

Preserve provenance and quality criteria for the desired assistant responses. Supervised instruction tuning teaches the supplied continuation, including its mistakes, format, and omissions; it does not automatically establish truthfulness or broad instruction-following ability. A synthetic or human-authored example still needs validation against the actual task. Distinguish supervised demonstrations from preference optimization or reinforcement learning stages instead of calling all post-training methods the same objective.

Transformers 4.57.1 documents chat templates as model-specific formatting and warns that separately adding special tokens after a template can duplicate them. In that version, template-and-tokenize together is a supported route; if formatting text first, subsequent tokenization must follow the documented special-token setting. A training example containing a completed assistant answer differs from an inference prompt that opens a new assistant turn. Do not append a generation prefix to completed targets blindly.

An end-of-turn token and an end-of-sequence token can coincide in one model family and differ in another. Their identity and placement affect stopping behavior and later loss masks. Preserve tokenizer files, added-token IDs, template revision, and base checkpoint identity together. A tokenizer with the same vocabulary size can still assign different IDs or use different normalization. Testing decoded text alone may conceal duplicated boundary tokens or changed control-token placement.

Render representative single-turn, multi-turn, empty, and tool-bearing records through the exact preprocessing path. Inspect token IDs, decoded boundaries, attention masks, and target masks. Include content containing strings resembling control markers and follow the template's supported handling; a structured role field does not guarantee the model is immune to adversarial instructions inside content. Confirm deployment uses the same intended conversation format and stopping rules before comparing generations across checkpoints.

The cache lab is only an identity-version contrast. Reset and set both C=4, s=4, predict twelve hits among the initial sixteen address requests, then Step to add one hit. The empty-start fully associative LRU trace caps at 4096 requests and has no tokenizer or chat template. A matching abstract key does not prove two serialized examples are interchangeable. Explain which base, tokenizer, template, and schema revisions a real preprocessing-cache key would need to preserve.