Chat Templates and Parsing

llamadart routes chat rendering/parsing through template handlers aligned to llama.cpp behavior.

Parity model#

llamadart reimplements llama.cpp-style template detection, rendering, workarounds, grammar wiring, and parse behavior in Dart. This is why engine.create(...) and engine.chatTemplate(...) can keep consistent behavior across native and web backends.

Template rendering is powered by dinja, the Dart Jinja runtime used by llamadart for llama.cpp-compatible template execution.

For internals and pipeline details, see 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.
• templateNow: deterministic time injection for tests.
• sourceLangCode / targetLangCode: TranslateGemma style metadata.
• responseFormat: structured-output schema hints.

engine.create(...) accepts responseFormat for strict structured output. Use {'type': 'json_object'} or {'type': 'json_schema', 'json_schema': {'schema': <JSON schema>}}. Application code can build those maps with LlamaStructuredOutput, or call engine.createStructuredJson(...) to collect streamed content and validate the final JSON before decoding it into an app type. Streaming UI code can still pass responseFormat: output.responseFormat and finish with await stream.parseStructuredJson(output) after the stream completes. Grammar-capable backends use those hints for strict output. LiteRT-LM native and web fail early for strict response formats because the current public runtime APIs do not expose JSON-schema/Lark constraint wiring.

chatTemplate(...) still accepts the deprecated jsonSchema shortcut for template inspection. Prefer responseFormat for new code; 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.choices.first.delta.thinking carries reasoning text while chunk.choices.first.delta.content 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#

For application code, the supported customization path is customTemplate on engine.chatTemplate(...).

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 = LlamaEngine(LlamaBackend());

  try {
    await engine.loadModel('model.gguf');
    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 an internal extension point used by built-in format implementations.

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.