feat(neuron): render the model's chat_template with chat_template_kwargs

Closes #9.

Replaces the hardcoded `format_qwen3_prompt` ChatML glue with
`minijinja`-driven rendering of the model's own `chat_template`
from `tokenizer_config.json`. The request's `chat_template_kwargs`
flow into the Jinja context so model-specific levers
(Qwen3's `enable_thinking: false`, etc.) actually take effect.

## Implementation

- New `harness::chat_template` module with three entry points:
  - `load_chat_template_alongside(tokenizer_json_path)` — probes
    `tokenizer_config.json` in the same hf-hub snapshot directory.
    Supports both the canonical string-form `chat_template` and
    the array-form some tokenizers ship (multi-template models).
  - `render_chat_template(template, messages, tools, kwargs)` —
    renders via `minijinja`. Messages flatten into the
    `[{role, content}]` shape HF templates iterate, with
    per-message extras (`tool_calls`, `tool_call_id`) preserved.
    `tools` and `kwargs` add into the Jinja context so templates
    that reference them work without us interpreting their shape.
  - `chat_templates_enabled()` reads `NEURON_USE_CHAT_TEMPLATE`
    (default true). Falsy values force the fallback path
    everywhere — a kill switch for emergency rollback without a
    rebuild.

- `LoadedModel.chat_template: Option<String>` and the TP
  equivalent are populated once at load time. `None` (no
  tokenizer_config.json, parse error, missing field) routes the
  fallback path silently; logs go through `tracing::debug`/`warn`
  per condition.

- New `build_prompt_for_request(chat_template, request)` wraps
  the decision: when both the template is present AND the kill
  switch is off, render with kwargs from `request.extra` (looks
  up `chat_template_kwargs` and `tools` lazily). On render error
  → warn + fallback to `format_qwen3_prompt`. Wired into all four
  current prompt-build sites (single-GPU stream + non-stream, TP
  stream + non-stream).

## Dependency

`minijinja = "2"` with the `builtins`, `json`, and `serde`
features. Pure-Rust Jinja2 implementation, ~80KB compiled. Used
internally by HF's `tokenizers-rs` for its own chat templating;
the API surface we touch (`Environment::add_template` +
`Template::render(serde_value)`) is stable.

## Validation strategy

I can't byte-compare the new path's output against
`format_qwen3_prompt` for live models without GPU (CI doesn't
have one). The fallback path and kill switch are the mitigations
— a deploy can flip `NEURON_USE_CHAT_TEMPLATE=false` in the
neuron service env if the chat template renders surprisingly on
Qwen3-8B in production. The legacy formatter stays the
fail-closed default.

## Scope cuts (documented in module header)

- Tool-definition lifting from helexa-acp's system-prompt
  injection into the chat_template's native tools block is
  deferred. Today the request's `tools` array threads into the
  Jinja context, but helexa-acp continues to inject Hermes-format
  tool descriptions into the system prompt for backwards-compat
  with non-cortex endpoints.

## Tests

9 unit tests in `chat_template`: kill-switch matrix (truthy /
falsy / unset), template loading (string form, array form,
missing file, unparseable JSON, missing field), rendering
(basic conversation threading, kwargs forwarding, message-extras
threading for tool_calls).

215 workspace tests pass; clippy + fmt clean across all workspace
features (default).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

crates/neuron/Cargo.toml

@@ -76,6 +76,15 @@ cudarc = { version = "0.19", optional = true, default-features = false, features
 half = { version = "2.5", optional = true }
 tokenizers = { version = "0.22", default-features = false, features = ["onig"] }
 hf-hub = { version = "0.4", features = ["tokio"] }
+# Jinja-compatible template renderer for the model's
+# `tokenizer_config.json::chat_template`. Hugging Face's chat
+# templates use a strict subset of Jinja2 that minijinja supports
+# out of the box. ~80KB compiled; pure Rust, no async surface.
+# Features: `builtins` for the `is defined` / `default` filters HF
+# templates use; `json` for `tojson` (some Qwen3 templates emit
+# tool definitions via tojson); `serde` so we can hand it a
+# serde_json::Value as the context.
+minijinja = { version = "2", features = ["builtins", "json", "serde"] }
 # Direct dep on `safetensors` (re-exported by candle but its `TensorView`
 # / `slice::IndexOp` types are public-but-not-re-exported). Used by the
 # tp `fused_load` module to read per-rank slices of fused QKV tensors

crates/neuron/src/harness/candle.rs

@@ -167,6 +167,15 @@ pub struct LoadedModel {
     /// through as plain text in that case and the consumer parses
     /// the markers itself if it knows how.
     pub tool_call_tokens: Option<ToolCallTokenPair>,
+    /// Raw Jinja `chat_template` string loaded from this model's
+    /// `tokenizer_config.json` at load time. `None` when the file
+    /// is absent / unparseable / lacks the field. When `Some`,
+    /// the prompt-build path renders it through `minijinja` with
+    /// `chat_template_kwargs` from the request body; when `None`,
+    /// the hardcoded Qwen3 ChatML fallback (`format_qwen3_prompt`)
+    /// is used. The `NEURON_USE_CHAT_TEMPLATE=false` env var
+    /// forces the fallback path even when `Some`.
+    pub chat_template: Option<String>,
 }
 
 impl LoadedModel {
@@ -229,6 +238,8 @@ pub struct TpLoadedModel {
     pub reasoning_tokens: Option<ReasoningTokenPair>,
     /// Same shape as [`LoadedModel::tool_call_tokens`].
     pub tool_call_tokens: Option<ToolCallTokenPair>,
+    /// Same shape as [`LoadedModel::chat_template`].
+    pub chat_template: Option<String>,
 }
 
 #[cfg(feature = "cuda")]
@@ -1397,7 +1408,7 @@ impl CandleHarness {
         let _inference_guard = loaded.inference_lock.lock().await;
 
         let result = async {
-            let prompt = format_qwen3_prompt(&request.messages);
+            let prompt = build_prompt_for_request(loaded.chat_template.as_deref(), &request);
 
             let encoding = loaded
                 .tokenizer
@@ -1702,7 +1713,7 @@ impl CandleHarness {
             }
         };
 
-        let prompt = format_qwen3_prompt(&request.messages);
+        let prompt = build_prompt_for_request(loaded.chat_template.as_deref(), &request);
         let encoding = loaded
             .tokenizer
             .encode(prompt.as_str(), true)
@@ -2081,6 +2092,19 @@ impl Harness for CandleHarness {
                 "tool-call markers detected — streaming will emit structured ToolCall events"
             );
         }
+        // Probe `tokenizer_config.json` in the same snapshot dir.
+        // When present and non-empty, the inference path renders
+        // this Jinja template with the request's
+        // `chat_template_kwargs` instead of using the hardcoded
+        // ChatML formatter. Best-effort: missing or unparseable
+        // configs silently fall through to the legacy path.
+        let chat_template = super::chat_template::load_chat_template_alongside(&tokenizer_path);
+        if chat_template.is_some() {
+            tracing::info!(
+                model = %spec.model_id,
+                "chat_template loaded from tokenizer_config.json — prompt assembly will use the model's own template"
+            );
+        }
 
         let loaded = Arc::new(LoadedModel {
             model_id: spec.model_id.clone(),
@@ -2095,6 +2119,7 @@ impl Harness for CandleHarness {
             inference_lock: tokio::sync::Mutex::new(()),
             reasoning_tokens,
             tool_call_tokens,
+            chat_template,
         });
 
         let mut models = self.models.write().await;
@@ -2288,6 +2313,13 @@ impl CandleHarness {
                 "TP load: tool-call markers detected"
             );
         }
+        let chat_template = super::chat_template::load_chat_template_alongside(&tokenizer_path);
+        if chat_template.is_some() {
+            tracing::info!(
+                model = %spec.model_id,
+                "TP load: chat_template loaded from tokenizer_config.json"
+            );
+        }
 
         let tp_loaded = StdArc::new(TpLoadedModel {
             model_id: spec.model_id.clone(),
@@ -2303,6 +2335,7 @@ impl CandleHarness {
             worker: leader_worker,
             reasoning_tokens,
             tool_call_tokens,
+            chat_template,
         });
 
         let mut models = self.models.write().await;
@@ -2429,7 +2462,7 @@ impl CandleHarness {
             return Err(poisoned_error(&request.model));
         }
 
-        let prompt = format_qwen3_prompt(&request.messages);
+        let prompt = build_prompt_for_request(tp.chat_template.as_deref(), &request);
         let encoding = tp
             .tokenizer
             .encode(prompt.as_str(), true)
@@ -2893,7 +2926,7 @@ async fn chat_completion_tp_inner(
     let req_start = std::time::Instant::now();
     let model_id = request.model.clone();
 
-    let prompt = format_qwen3_prompt(&request.messages);
+    let prompt = build_prompt_for_request(tp.chat_template.as_deref(), &request);
     let encoding = tp
         .tokenizer
         .encode(prompt.as_str(), true)
@@ -3242,6 +3275,66 @@ pub enum InferenceError {
     Other(#[from] anyhow::Error),
 }
 
+/// Build the model's prompt from a [`ChatCompletionRequest`].
+///
+/// Prefers the model's own `chat_template` when one was loaded
+/// from `tokenizer_config.json` at startup and the
+/// `NEURON_USE_CHAT_TEMPLATE` kill switch isn't tripped. The
+/// request's `chat_template_kwargs` (e.g.
+/// `{"enable_thinking": false}` on Qwen3) and `tools` array flow
+/// into the template's Jinja context so model-specific behaviour
+/// like reasoning-suppression-at-generation works.
+///
+/// Falls back to [`format_qwen3_prompt`] (the legacy hardcoded
+/// ChatML glue) on any of:
+///
+/// - no `chat_template` loaded for this model (older quantised
+///   variants, fallback-only models)
+/// - the env kill switch is set to a falsy value
+/// - the template rendered to an error (caller can flip the env
+///   var to force fallback while debugging the template)
+///
+/// Failures are logged at `warn` so an operator running with
+/// `RUST_LOG=neuron=debug` sees which path each request took.
+fn build_prompt_for_request(
+    chat_template: Option<&str>,
+    request: &ChatCompletionRequest,
+) -> String {
+    if !super::chat_template::chat_templates_enabled() {
+        return format_qwen3_prompt(&request.messages);
+    }
+    let Some(tmpl) = chat_template else {
+        return format_qwen3_prompt(&request.messages);
+    };
+
+    // Pull `chat_template_kwargs` and `tools` from the request's
+    // catch-all `extra` field. Both are optional; absent fields
+    // become `Value::Null`, which the renderer skips inserting
+    // into the Jinja context.
+    let kwargs = request
+        .extra
+        .get("chat_template_kwargs")
+        .cloned()
+        .unwrap_or(serde_json::Value::Null);
+    let tools = request
+        .extra
+        .get("tools")
+        .cloned()
+        .unwrap_or(serde_json::Value::Null);
+
+    match super::chat_template::render_chat_template(tmpl, &request.messages, &tools, &kwargs) {
+        Ok(prompt) => prompt,
+        Err(e) => {
+            tracing::warn!(
+                model = %request.model,
+                error = %format!("{e:#}"),
+                "chat_template render failed; falling back to format_qwen3_prompt"
+            );
+            format_qwen3_prompt(&request.messages)
+        }
+    }
+}
+
 /// Apply the Qwen3 chat template:
 ///
 /// ```text

crates/neuron/src/harness/chat_template.rs

@@ -0,0 +1,392 @@
+//! Chat-template rendering for the model-supplied Jinja templates
+//! HuggingFace tokenizers ship in `tokenizer_config.json`.
+//!
+//! ## Background
+//!
+//! Every modern open-weight model bundles a `chat_template` field
+//! in its `tokenizer_config.json` — a Jinja2 template string that
+//! converts a sequence of `{role, content}` messages into the
+//! exact prompt the model was trained on. Examples:
+//!
+//! - Qwen3-Coder: `<|im_start|>{role}\n{content}<|im_end|>\n…`
+//!   with conditional `enable_thinking` handling that injects an
+//!   empty `<think>\n\n</think>` block when set false.
+//! - DeepSeek-R1: similar im_start framing with different special-
+//!   token names.
+//! - Mistral / Magistral: a `[INST]` / `[/INST]` framing.
+//! - Claude / Llama: another shape again.
+//!
+//! Rendering the model's own template is the only way to get the
+//! *exact* prompt format the model was trained on plus the
+//! model-specific kwargs (`enable_thinking`, `tools`, …) without
+//! hardcoding per-model logic. The alternative — neuron's previous
+//! `format_qwen3_prompt` — was a hardcoded Qwen3 ChatML glue that
+//! ignored kwargs entirely.
+//!
+//! ## Scope
+//!
+//! This module is request-side only: it builds the prompt string
+//! the tokenizer ingests before inference. The reasoning- and
+//! tool-call-marker token routing (issues #6, #8) is response-side
+//! and stays in `wire::openai_chat` / the streaming inference
+//! loops.
+//!
+//! ## Fallback
+//!
+//! When the model's `tokenizer_config.json` is missing, doesn't
+//! parse, lacks a `chat_template`, or renders an error, the caller
+//! falls back to `format_qwen3_prompt`. The
+//! `NEURON_USE_CHAT_TEMPLATE=false` env var is a global kill
+//! switch — if a deploy goes sideways and the renderer is to
+//! blame, an operator can flip the env and restart neuron without
+//! shipping a new build.
+
+use anyhow::{Context, Result};
+use cortex_core::openai::{ChatMessage, MessageContent};
+use minijinja::Environment;
+use serde_json::Value;
+use std::path::Path;
+
+/// Environment variable that, when set to `false`/`0`/`no`,
+/// forces every model to skip its `chat_template` and fall back
+/// to `format_qwen3_prompt`. Default (unset) is "use chat
+/// templates where available".
+pub const KILL_SWITCH_ENV: &str = "NEURON_USE_CHAT_TEMPLATE";
+
+/// Read the global kill switch. `true` means chat templates are
+/// enabled; `false` forces the fallback path everywhere.
+pub fn chat_templates_enabled() -> bool {
+    match std::env::var(KILL_SWITCH_ENV).ok().as_deref() {
+        Some(s) => !matches!(
+            s.trim().to_ascii_lowercase().as_str(),
+            "false" | "0" | "no" | "off"
+        ),
+        None => true,
+    }
+}
+
+/// Convenience: probe for `tokenizer_config.json` in the same
+/// directory the tokenizer was loaded from. Both files come from
+/// the same HuggingFace snapshot in the hf-hub cache, so the
+/// sibling path is reliable.
+pub fn load_chat_template_alongside(tokenizer_json_path: &Path) -> Option<String> {
+    let parent = tokenizer_json_path.parent()?;
+    let config_path = parent.join("tokenizer_config.json");
+    load_chat_template_from(&config_path)
+}
+
+/// Best-effort load of `chat_template` from a HuggingFace
+/// `tokenizer_config.json`. Returns `None` when the file is
+/// absent, doesn't parse, or lacks the `chat_template` field —
+/// in all of those cases the caller falls back to
+/// `format_qwen3_prompt`. Warnings are logged so an operator can
+/// see why the fallback fired.
+pub fn load_chat_template_from(path: &Path) -> Option<String> {
+    let text = match std::fs::read_to_string(path) {
+        Ok(t) => t,
+        Err(e) => {
+            tracing::debug!(
+                path = %path.display(),
+                error = %e,
+                "chat_template: tokenizer_config.json absent or unreadable; falling back"
+            );
+            return None;
+        }
+    };
+    let value: Value = match serde_json::from_str(&text) {
+        Ok(v) => v,
+        Err(e) => {
+            tracing::warn!(
+                path = %path.display(),
+                error = %e,
+                "chat_template: tokenizer_config.json failed to parse; falling back"
+            );
+            return None;
+        }
+    };
+    // Some tokenizer_config.json files carry `chat_template` as an
+    // array of `{name, template}` objects (multi-template models —
+    // tool-use variant, default variant). For now we pick the first
+    // entry; future iterations could honour a name hint.
+    match value.get("chat_template") {
+        Some(Value::String(s)) => Some(s.clone()),
+        Some(Value::Array(arr)) => {
+            for entry in arr {
+                if let Some(t) = entry.get("template").and_then(|v| v.as_str()) {
+                    return Some(t.to_string());
+                }
+            }
+            tracing::warn!(
+                path = %path.display(),
+                "chat_template: array form had no usable template entry; falling back"
+            );
+            None
+        }
+        _ => None,
+    }
+}
+
+/// Render the chat template into the prompt the model expects.
+///
+/// `template` is the raw Jinja string from `tokenizer_config.json`.
+/// `messages` is the conversation in order. `kwargs` is the
+/// `chat_template_kwargs` object the client supplied on the
+/// request (or `Value::Null` when absent). The function expands
+/// the kwargs into the Jinja context alongside the standard
+/// `messages` and `add_generation_prompt` variables HF templates
+/// expect.
+///
+/// `tools` is the request's `tools` array (or `Value::Null`).
+/// Some chat templates iterate it to emit native tool definitions
+/// (Qwen3-Coder's tool-use template, Mistral's [TOOL_DEFINITIONS]
+/// frame). We forward whatever the client sent without
+/// interpretation.
+pub fn render_chat_template(
+    template: &str,
+    messages: &[ChatMessage],
+    tools: &Value,
+    kwargs: &Value,
+) -> Result<String> {
+    let mut env = Environment::new();
+    // Compile the template against a fixed name so error messages
+    // surface "chat_template" rather than `<template>`.
+    env.add_template("chat_template", template)
+        .context("compile chat_template")?;
+    let tmpl = env.get_template("chat_template").unwrap();
+
+    // Convert our internal ChatMessage shape into the
+    // `[{role, content}]` shape HF templates iterate. Text content
+    // becomes a string; Parts becomes an array of content blocks.
+    // The HF templates handle both shapes via `content is string`
+    // checks or content-array iteration.
+    let messages_json: Vec<Value> = messages
+        .iter()
+        .map(|m| {
+            let content_value = match &m.content {
+                MessageContent::Text(s) => Value::String(s.clone()),
+                MessageContent::Parts(parts) => Value::Array(parts.clone()),
+            };
+            let mut obj = serde_json::Map::new();
+            obj.insert("role".into(), Value::String(m.role.clone()));
+            obj.insert("content".into(), content_value);
+            // Forward extras (e.g. tool_calls on assistant turns,
+            // tool_call_id on tool result turns). HF templates that
+            // need them read e.g. `message.tool_calls`.
+            if let Value::Object(extras) = &m.extra {
+                for (k, v) in extras {
+                    obj.insert(k.clone(), v.clone());
+                }
+            }
+            Value::Object(obj)
+        })
+        .collect();
+
+    // Build the kwargs context. Add base bindings the template
+    // expects (`messages`, `add_generation_prompt`, `tools`) plus
+    // anything the caller passed in `chat_template_kwargs`. Caller
+    // kwargs override the defaults so `add_generation_prompt: false`
+    // from the request actually wins.
+    let mut ctx_map = serde_json::Map::new();
+    ctx_map.insert("messages".into(), Value::Array(messages_json));
+    ctx_map.insert("add_generation_prompt".into(), Value::Bool(true));
+    if !tools.is_null() {
+        ctx_map.insert("tools".into(), tools.clone());
+    }
+    if let Value::Object(kwargs_obj) = kwargs {
+        for (k, v) in kwargs_obj {
+            ctx_map.insert(k.clone(), v.clone());
+        }
+    }
+    // `Template::render` takes any Serialize value; serde_json's
+    // `Value` implements it natively, so we pass the assembled
+    // context object directly without going through the
+    // `context!` macro (which expects minijinja-native values).
+    tmpl.render(Value::Object(ctx_map))
+        .context("render chat_template")
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use serde_json::json;
+
+    fn user_msg(text: &str) -> ChatMessage {
+        ChatMessage {
+            role: "user".into(),
+            content: MessageContent::Text(text.into()),
+            extra: Value::Object(Default::default()),
+        }
+    }
+
+    fn assistant_msg(text: &str) -> ChatMessage {
+        ChatMessage {
+            role: "assistant".into(),
+            content: MessageContent::Text(text.into()),
+            extra: Value::Object(Default::default()),
+        }
+    }
+
+    /// Minimal Qwen3-style template — enough surface to confirm
+    /// our renderer threads role + content correctly without
+    /// loading a real model's tokenizer_config.json.
+    const QWEN3_LIKE: &str = "{%- for message in messages -%}\
+<|im_start|>{{ message.role }}\n{{ message.content }}<|im_end|>\n\
+{%- endfor -%}\
+{%- if add_generation_prompt -%}<|im_start|>assistant\n{%- endif -%}";
+
+    #[test]
+    fn renders_basic_conversation() {
+        let prompt = render_chat_template(
+            QWEN3_LIKE,
+            &[user_msg("hello"), assistant_msg("hi"), user_msg("bye")],
+            &Value::Null,
+            &Value::Null,
+        )
+        .unwrap();
+        // Structural assertions — the exact whitespace produced
+        // by a given template is a Jinja-trim concern that varies
+        // per real chat_template. What matters is that every
+        // turn's role + content thread through in order, and that
+        // the generation cue lands at the end.
+        assert!(
+            prompt.contains("<|im_start|>user\nhello<|im_end|>"),
+            "first user turn missing: {prompt}"
+        );
+        assert!(
+            prompt.contains("<|im_start|>assistant\nhi<|im_end|>"),
+            "assistant turn missing: {prompt}"
+        );
+        assert!(
+            prompt.contains("<|im_start|>user\nbye<|im_end|>"),
+            "second user turn missing: {prompt}"
+        );
+        assert!(
+            prompt.ends_with("<|im_start|>assistant")
+                || prompt.ends_with("<|im_start|>assistant\n"),
+            "generation cue missing at end: {prompt}"
+        );
+    }
+
+    #[test]
+    fn kwargs_are_threaded_into_template_context() {
+        // Replica of Qwen3's enable_thinking branch in
+        // simplified form. When the kwarg is false, the model's
+        // template injects an empty `<think>...</think>` block
+        // before the generation cue — pre-filling the model's
+        // reasoning slot with "no thinking" so the model emits
+        // the answer directly.
+        let template = "{%- if enable_thinking is defined and enable_thinking is false -%}\
+NO_THINK\
+{%- else -%}\
+THINK_OK\
+{%- endif -%}";
+        let r_disabled = render_chat_template(
+            template,
+            &[],
+            &Value::Null,
+            &json!({ "enable_thinking": false }),
+        )
+        .unwrap();
+        assert_eq!(r_disabled, "NO_THINK");
+        let r_default = render_chat_template(template, &[], &Value::Null, &Value::Null).unwrap();
+        assert_eq!(r_default, "THINK_OK");
+    }
+
+    #[test]
+    fn missing_template_field_returns_none() {
+        let tmp = std::env::temp_dir().join("neuron-test-tokenizer-missing-field.json");
+        std::fs::write(&tmp, r#"{"some_other_field": 1}"#).unwrap();
+        assert!(load_chat_template_from(&tmp).is_none());
+        let _ = std::fs::remove_file(tmp);
+    }
+
+    #[test]
+    fn load_template_from_string_field() {
+        let tmp = std::env::temp_dir().join("neuron-test-tokenizer-string.json");
+        std::fs::write(
+            &tmp,
+            r#"{"chat_template": "hello {{ messages[0].content }}"}"#,
+        )
+        .unwrap();
+        let t = load_chat_template_from(&tmp).expect("template loaded");
+        assert!(t.contains("messages[0].content"));
+        let _ = std::fs::remove_file(tmp);
+    }
+
+    #[test]
+    fn load_template_from_array_form() {
+        // Some HF models ship `chat_template` as `[{name, template}, ...]`.
+        let tmp = std::env::temp_dir().join("neuron-test-tokenizer-array.json");
+        std::fs::write(
+            &tmp,
+            r#"{"chat_template": [{"name": "default", "template": "ARR"}]}"#,
+        )
+        .unwrap();
+        let t = load_chat_template_from(&tmp).expect("template loaded");
+        assert_eq!(t, "ARR");
+        let _ = std::fs::remove_file(tmp);
+    }
+
+    #[test]
+    fn missing_file_returns_none_quietly() {
+        let absent = std::path::PathBuf::from("/definitely/not/a/real/path.json");
+        assert!(load_chat_template_from(&absent).is_none());
+    }
+
+    #[test]
+    fn unparseable_returns_none() {
+        let tmp = std::env::temp_dir().join("neuron-test-tokenizer-garbage.json");
+        std::fs::write(&tmp, b"{not valid json").unwrap();
+        assert!(load_chat_template_from(&tmp).is_none());
+        let _ = std::fs::remove_file(tmp);
+    }
+
+    #[test]
+    fn kill_switch_recognises_truthy_falsy_values() {
+        // Test against the actual env var so callers see the
+        // same behaviour as production. Serialise via a
+        // mutex — see path_util.rs for the pattern.
+        use std::sync::Mutex;
+        static LOCK: Mutex<()> = Mutex::new(());
+        let _g = LOCK.lock().unwrap();
+        let prior = std::env::var(KILL_SWITCH_ENV).ok();
+        unsafe {
+            std::env::remove_var(KILL_SWITCH_ENV);
+        }
+        assert!(chat_templates_enabled());
+        for value in ["false", "0", "no", "off", "FALSE", "  no  "] {
+            unsafe { std::env::set_var(KILL_SWITCH_ENV, value) };
+            assert!(!chat_templates_enabled(), "value {value:?} should disable");
+        }
+        for value in ["true", "1", "yes", ""] {
+            unsafe { std::env::set_var(KILL_SWITCH_ENV, value) };
+            assert!(chat_templates_enabled(), "value {value:?} should enable");
+        }
+        unsafe {
+            match prior {
+                Some(p) => std::env::set_var(KILL_SWITCH_ENV, p),
+                None => std::env::remove_var(KILL_SWITCH_ENV),
+            }
+        }
+    }
+
+    #[test]
+    fn message_extras_thread_through_for_tool_calls() {
+        // HF templates read assistant.tool_calls and tool
+        // turns' tool_call_id. Confirm our extras flatten into
+        // the message object the template iterates.
+        let mut extras = serde_json::Map::new();
+        extras.insert(
+            "tool_calls".into(),
+            json!([{"id": "t1", "function": {"name": "x", "arguments": "{}"}}]),
+        );
+        let msg = ChatMessage {
+            role: "assistant".into(),
+            content: MessageContent::Text(String::new()),
+            extra: Value::Object(extras),
+        };
+        let template = "{{ messages[0].tool_calls[0].id }}";
+        let rendered = render_chat_template(template, &[msg], &Value::Null, &Value::Null).unwrap();
+        assert_eq!(rendered, "t1");
+    }
+}

crates/neuron/src/harness/mod.rs

@@ -2,6 +2,7 @@
 
 pub mod arch;
 pub mod candle;
+pub mod chat_template;
 pub mod device_worker;
 pub mod tp;