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Chat templates are the rule layer by which inference frameworks convert multi-turn conversations, system prompts, and tool call sequences into model input tokens, usually distributed as Jinja2 templates together with the model weights.
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10/23/2026
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UnverifiedModels labeled with Instruct or Chat are instruction-tuned conversational versions, while those without labels are Base foundation models71% similarUnverifiedIn llama.cpp, Jinja templates are used to assemble the conversation history into the input format expected by different models (such as Qwen, Llama, Mistral); preparing the corresponding template for different models is more reliable than relying on the built-in default template.68% similarUnverifiedUsing system prompts to constrain the model's output style and character personality boundaries is a technical approach to implementing character dialogue.67% similarUnverifiedThe chat split refers to the subset of a dataset specifically organized in multi-turn dialogue format, where samples are usually shorter, with a natural tone, and close to real interaction; other common splits alongside it include math, code, stem, etc.66% similarUnverifiedThe total token count for a single conversation is the sum of input (context, instructions, history) and output (the model's generated response)65% similar
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