Renderer

Introduction

dynamo-renderer turns OpenAI-style chat requests into model-ready prompt strings. It is the encode side of inference serving: messages + tools + generation settings in, a fully-rendered prompt out. It is standalone, so an external OpenAI frontend can reuse Dynamo's prompt formatting without pulling in the Dynamo runtime.

It renders HuggingFace chat_template jinja2 (via minijinja + minijinja-contrib pycompat) and also ships native Rust formatters for model families whose prompt protocol cannot be represented faithfully by the published template. The crate is a bridge between OpenAI request types (dynamo-protocols) and the template engine; Kimi K3 additionally returns segment-aware prompts so structural XTML tokens and ordinary user text remain distinct through tokenization.

Features

• HF chat templates: faithful apply_chat_template rendering, including tool-use and generation-prompt handling.
• Native DeepSeek formatters: Rust formatters for V4.1 / V4 / V3.2 families (under deepseek).
• Native Inkling formatter: exact text, image, audio, reasoning, and tool-use framing for inkling_mm_model (under inkling). Media blocks contain only the marker token; the backend multimodal processor expands per-patch or per-frame placeholders later.
• Native Kimi K3 formatter: XTML rendering with explicit trusted-control and ordinary-text segment boundaries (under kimi_k3).
• Bring-your-own request type: implement OAIChatLikeRequest for any request type, or use the ready-made impl for dynamo-protocols' OpenAI chat request.
• Self-contained: no async runtime or networking; segment-aware prompts interoperate with dynamo-tokenizers.

Quick Start

use dynamo_renderer::{ChatTemplate, ContextMixins, PromptFormatter};
use dynamo_protocols::types::CreateChatCompletionRequest;

// `config` is parsed from a model's `tokenizer_config.json`.
let config: ChatTemplate = serde_json::from_str(tokenizer_config_json)?;
let PromptFormatter::OAI(formatter) =
    PromptFormatter::from_parts(config, ContextMixins::default(), /* exclude_tools_when_tool_choice_none */ false)?
else {
    unreachable!("from_parts always builds an OAI formatter")
};

// Any type implementing `OAIChatLikeRequest` can be rendered; the standard
// OpenAI chat request works out of the box.
let request: CreateChatCompletionRequest = serde_json::from_str(request_json)?;
let prompt: String = formatter.render(&request)?;

DeepSeek V4.1

The V4.1 formatter supports text messages, tool history, mid-conversation system messages, and numeric reasoning effort. It rejects media content and explicit tool namespace fields; qualified function names are preserved. Generation headers follow the reference encoder and cannot be disabled with add_generation_prompt. OpenAI effort names match the reference encoder: low is 50, high is 75, and max is 100; the default is 75. The xhigh alias is not supported. Template arguments accept the same names or an integer from 1 to 100. Top-level effort takes precedence over template effort. Set reasoning_effort to none or the template argument thinking to false to disable thinking.

Reasoning-effort names and the default follow the model's Python reference encoder at revision dba1be0a. Prompt fixtures pass explicit numeric effort to the low-level encoder.

Relationship to other crates

• dynamo-protocols — OpenAI/wire request types this crate renders from.
• dynamo-tokenizers — tokenization (the next step after rendering); re-exported here for convenience.
• dynamo-parsers — the decode side (parsing model output back into reasoning / tool calls).

Ownership

This crate is developed and published from frontend-crates. Dynamo consumes the published crate rather than carrying a source mirror.