Chat templates and output parsing

llamadart reimplements llama.cpp's template detection, rendering and parsing in Dart, so engine.create and engine.chatTemplate behave the same on native and web. Templates run on dinja, a Dart Jinja runtime; the pipeline is described in Template engine internals.

Core API#

Use engine.chatTemplate(...) when you need:

• prompt preview,
• grammar and stop-sequence inspection,
• format-aware rendering diagnostics.

final result = await engine.chatTemplate(
  messages,
  tools: tools,
  toolChoice: ToolChoice.auto,
  parallelToolCalls: false,
  customTemplate: null,
  chatTemplateKwargs: const {'use_builtin_tools': true},
);

print(result.prompt);
print(result.format);

Useful parameters#

• customTemplate: per-call template override.
• chatTemplateKwargs: additional template globals, as llama.cpp's chat_template_kwargs. TranslateGemma reads its language codes from {'source_lang_code': 'en', 'target_lang_code': 'ko'}, defaulting to en-GB; the sourceLangCode and targetLangCode parameters are deprecated.
• templateNow: deterministic time injection for tests.
• responseFormat: structured-output constraint; unrecognised shapes throw LlamaUnsupportedException.

Structured output (responseFormat, LlamaStructuredOutput, parseStructuredJson) is covered in Generation and Streaming. chatTemplate(...) still accepts the deprecated jsonSchema shortcut; if both are passed, responseFormat wins.

When to inspect template output#

Inspect template output when debugging:

• tool-call shape mismatches,
• stop-sequence behavior,
• model-specific reasoning/content boundaries,
• template routing differences after upgrades.

Built-in format coverage#

Built-in handlers include newer formats such as Gemma 4. In practice that means llamadart can detect and parse:

• <|turn> ... <turn|> turn framing,
• <|think|> thinking enablement in the system prompt,
• <|channel>thought ... <channel|> reasoning output,
• <|tool_call>call:name{args}<tool_call|> tool-call envelopes.

Gemma 4 thought-channel output is parsed incrementally during streaming, so chunk.thinking carries reasoning text while chunk.text remains reserved for final answer content.

Tencent Hunyuan V3 templates are also detected directly, including their namespaced reasoning tags and parallel <tool_call:opensource> envelopes.

LiteRT-LM template registry#

GGUF models expose tokenizer.chat_template metadata directly through the llama.cpp backend. Native .litertlm bundles do not currently expose their embedded template through the LiteRT-LM FFI, so llamadart uses a filename-keyed registry for supported Gemma and Qwen LiteRT-LM families.

Native LiteRT-LM engine.create(...) uses LiteRT-LM's Conversation APIs for eligible text-only chat requests so system messages, history, tools, and extra context stay structured inside the runtime. The Dart template registry is still used for template metadata, streamed output parsing, engine.chatTemplate(...), web LiteRT-LM, and fallback prompt rendering when a request cannot use the native conversation path.

Use ModelParams.chatTemplate when loading a .litertlm bundle whose family is not in the registry or whose filename has been changed. The maintained registry coverage, smoke commands, and contribution notes live in doc/litert_lm_templates.md.

Custom template overrides#

To replace a model's template for every request, such as a GGUF file whose embedded template is missing or broken, load it with ModelParams(chatTemplate: ...). engine.create and engine.chatTemplate then render with that template instead of tokenizer.chat_template and its tool_use variant, and detect the tool-call and reasoning format from it, as llama.cpp's --chat-template-file does. Null or empty keeps the model's template. The value is Jinja source: unlike llama.cpp's --chat-template, a name such as chatml is not mapped to a built-in template. engine.getMetadata() still reports the GGUF tokenizer.chat_template.

To preview a different template for one call, pass customTemplate to engine.chatTemplate(...); it takes precedence over ModelParams.chatTemplate. The low-level WebGPU LlamaBackend.applyChatTemplate cannot render either override and throws LlamaUnsupportedException.

import 'package:llamadart/llamadart.dart';

const String customTemplate = '''
{% for message in messages %}
{{ message['role'] }}: {{ message['content'] }}
{% endfor %}
Assistant:
''';

Future<void> main() async {
  final LlamaEngine engine = await LlamaEngine.load(
    LlamaModel(ModelSource.path('model.gguf')),
  );

  try {
    final messages = [
      LlamaChatMessage.fromText(
        role: LlamaChatRole.user,
        text: 'Explain local inference in one sentence.',
      ),
    ];

    final rendered = await engine.chatTemplate(
      messages,
      customTemplate: customTemplate,
      addAssistant: true,
    );

    print(rendered.prompt);
    print(rendered.stopSequences);
  } finally {
    await engine.dispose();
  }
}

About custom handlers#

ChatTemplateHandler is exported so handler types are visible, but handlers are selected internally by ChatFormat; apps cannot register their own.

There is currently no public API to register custom handlers globally from application code. If you need first-class support for a new template format, open an issue with a minimal reproducible template and sample outputs.