Expose generation timings from server & update completions.js (#2116)

* use javascript generators as much cleaner API Also add ways to access completion as promise and EventSource * export llama_timings as struct and expose them in server * update readme, update baked includes * llama : uniform variable names + struct init --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
author: Tobias Lütke <tobi@shopify.com> 2023-07-05 16:51:13 -0400
committer: GitHub <noreply@github.com> 2023-07-05 16:51:13 -0400
commit: 31cfbb1013a482e89c72146e2063ac4362becae7 (patch)
tree: b6479860979f90d185a132efe883ae7fb62b07f5
parent: 983b555e9ddb36703cee4d22642afe958de093b7 (diff)
9 files changed, 1926 insertions, 1368 deletions
diff --git a/examples/server/README.md b/examples/server/README.md
index 037412d..c5139c1 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -26,20 +26,17 @@ Command line options:
 
 ## Build
 
-Build llama.cpp with server from repository root with either make or CMake.
+server is build alongside everything else from the root of the project
 
 - Using `make`:
 
   ```bash
-  LLAMA_BUILD_SERVER=1 make
+  make
   ```
 
 - Using `CMake`:
 
   ```bash
-  mkdir build-server
-  cd build-server
-  cmake -DLLAMA_BUILD_SERVER=ON ..
   cmake --build . --config Release
   ```
 
@@ -208,24 +205,30 @@ openai.api_base = "http://<Your api-server IP>:port"
 
 Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
 
-### Extending the Web Front End
+### Extending or building alternative Web Front End
 
-The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. A simple example is below:
+The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
 
-```
+Read the documentation in `/completion.js` to see convenient ways to access llama.
+
+A simple example is below:
+
+```html
 <html>
   <body>
     <pre>
       <script type="module">
-        import { llamaComplete } from '/completion.js'
-
-        llamaComplete({
-            prompt: "### Instruction:\nWrite dad jokes, each one paragraph. You can use html formatting if needed.\n\n### Response:",
-            n_predict: 1024,
-          },
-          null,
-          (chunk) => document.write(chunk.data.content)
-        )
+        import { llama } from '/completion.js'
+
+        const prompt = `### Instruction:
+Write dad jokes, each one paragraph.
+You can use html formatting if needed.
+
+### Response:`
+
+        for await (const chunk of llama(prompt)) {
+          document.write(chunk.data.content)
+        }
       </script>
     </pre>
   </body>
diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp
index 002830c..f399fb1 100644
--- a/examples/server/completion.js.hpp
+++ b/examples/server/completion.js.hpp
@@ -7,187 +7,369 @@ unsigned char completion_js[] = {
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 };
-unsigned int completion_js_len = 2275;
+unsigned int completion_js_len = 4462;
diff --git a/examples/server/deps.sh b/examples/server/deps.sh
index cf99516..1e9fe96 100755
--- a/examples/server/deps.sh
+++ b/examples/server/deps.sh
@@ -4,10 +4,6 @@
 # get the directory of this script file
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 PUBLIC=$DIR/public
-OUTPUT=$DIR/templats.hpp
-
-echo "// Generated file, do not edit" > $OUTPUT
-echo "" > $OUTPUT
 
 echo "download js bundle files"
 curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
index 832e9a3..42707fa 100644
--- a/examples/server/index.html.hpp
+++ b/examples/server/index.html.hpp
@@ -13,138 +13,143 @@ unsigned char index_html[] = {
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+  0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f,
+  0x6e, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x3c, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c,
+  0x70, 0x3e, 0x3c, 0x24, 0x7b, 0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x47, 0x65,
+  0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, 0x6e, 0x66, 0x6f,
+  0x7d, 0x20, 0x2f, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x70, 0x3e,
+  0x50, 0x6f, 0x77, 0x65, 0x72, 0x65, 0x64, 0x20, 0x62, 0x79, 0x20, 0x3c,
+  0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70,
+  0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x69, 0x74, 0x68, 0x75, 0x62, 0x2e, 0x63,
+  0x6f, 0x6d, 0x2f, 0x67, 0x67, 0x65, 0x72, 0x67, 0x61, 0x6e, 0x6f, 0x76,
+  0x2f, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x22, 0x3e,
+  0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c, 0x2f, 0x61,
+  0x3e, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65,
+  0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67,
+  0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x22, 0x3e, 0x67, 0x67, 0x6d, 0x6c,
+  0x2e, 0x61, 0x69, 0x3c, 0x2f, 0x61, 0x3e, 0x2e, 0x3c, 0x2f, 0x70, 0x3e,
+  0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c,
+  0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20,
+  0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64,
+  0x65, 0x72, 0x28, 0x68, 0x28, 0x41, 0x70, 0x70, 0x29, 0x2c, 0x20, 0x64,
+  0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x62, 0x6f, 0x64, 0x79,
+  0x29, 0x3b, 0x0a, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x63, 0x72, 0x69, 0x70,
+  0x74, 0x3e, 0x0a, 0x3c, 0x2f, 0x68, 0x65, 0x61, 0x64, 0x3e, 0x0a, 0x0a,
+  0x3c, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, 0x64,
+  0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a
 };
-unsigned int index_html_len = 10108;
+unsigned int index_html_len = 10752;
diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js
index 4f5005c..a43d5a7 100644
--- a/examples/server/public/completion.js
+++ b/examples/server/public/completion.js
@@ -5,20 +5,29 @@ const paramDefaults = {
   stop: ["</s>"]
 };
 
-/**
- * This function completes the input text using a llama dictionary.
- * @param {object} params - The parameters for the completion request.
- * @param {object} controller - an instance of AbortController if you need one, or null.
- * @param {function} callback - The callback function to call when the completion is done.
- * @returns {string} the completed text as a string. Ideally ignored, and you get at it via the callback.
- */
-export const llamaComplete = async (params, controller, callback) => {
+let generation_settings = null;
+
+
+// Completes the prompt as a generator. Recommended for most use cases.
+//
+// Example:
+//
+//    import { llama } from '/completion.js'
+//
+//    const request = llama("Tell me a joke", {n_predict: 800})
+//    for await (const chunk of request) {
+//      document.write(chunk.data.content)
+//    }
+//
+export async function* llama(prompt, params = {}, config = {}) {
+  let controller = config.controller;
+
   if (!controller) {
     controller = new AbortController();
   }
-  const completionParams = { ...paramDefaults, ...params };
 
-  // we use fetch directly here becasue the built in fetchEventSource does not support POST
+  const completionParams = { ...paramDefaults, ...params, prompt };
+
   const response = await fetch("/completion", {
     method: 'POST',
     body: JSON.stringify(completionParams),
@@ -36,7 +45,6 @@ export const llamaComplete = async (params, controller, callback) => {
   let content = "";
 
   try {
-
     let cont = true;
 
     while (cont) {
@@ -59,18 +67,21 @@ export const llamaComplete = async (params, controller, callback) => {
       result.data = JSON.parse(result.data);
       content += result.data.content;
 
-      // callack
-      if (callback) {
-        cont = callback(result) != false;
-      }
+      // yield
+      yield result;
 
       // if we got a stop token from server, we will break here
       if (result.data.stop) {
+        if (result.data.generation_settings) {
+          generation_settings = result.data.generation_settings;
+        }
         break;
       }
     }
   } catch (e) {
-    console.error("llama error: ", e);
+    if (e.name !== 'AbortError') {
+      console.error("llama error: ", e);
+    }
     throw e;
   }
   finally {
@@ -79,3 +90,79 @@ export const llamaComplete = async (params, controller, callback) => {
 
   return content;
 }
+
+// Call llama, return an event target that you can subcribe to
+//
+// Example:
+//
+//    import { llamaEventTarget } from '/completion.js'
+//
+//    const conn = llamaEventTarget(prompt)
+//    conn.addEventListener("message", (chunk) => {
+//      document.write(chunk.detail.content)
+//    })
+//
+export const llamaEventTarget = (prompt, params = {}, config = {}) => {
+  const eventTarget = new EventTarget();
+  (async () => {
+    let content = "";
+    for await (const chunk of llama(prompt, params, config)) {
+      if (chunk.data) {
+        content += chunk.data.content;
+        eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
+      }
+      if (chunk.data.generation_settings) {
+        eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
+      }
+      if (chunk.data.timings) {
+        eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
+      }
+    }
+    eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
+  })();
+  return eventTarget;
+}
+
+// Call llama, return a promise that resolves to the completed text. This does not support streaming
+//
+// Example:
+//
+//     llamaPromise(prompt).then((content) => {
+//       document.write(content)
+//     })
+//
+//     or
+//
+//     const content = await llamaPromise(prompt)
+//     document.write(content)
+//
+export const llamaPromise = (prompt, params = {}, config = {}) => {
+  return new Promise(async (resolve, reject) => {
+    let content = "";
+    try {
+      for await (const chunk of llama(prompt, params, config)) {
+        content += chunk.data.content;
+      }
+      resolve(content);
+    } catch (error) {
+      reject(error);
+    }
+  });
+};
+
+/**
+ * (deprecated)
+ */
+export const llamaComplete = async (params, controller, callback) => {
+  for await (const chunk of llama(params.prompt, params, { controller })) {
+    callback(chunk);
+  }
+}
+
+// Get the model info from the server. This is useful for getting the context window and so on.
+export const llamaModelInfo = async () => {
+  if (!generation_settings) {
+    generation_settings = await fetch("/model.json").then(r => r.json());
+  }
+  return generation_settings;
+}
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 6393e2e..8ace0b0 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -6,7 +6,6 @@
   <title>llama.cpp - chat</title>
 
   <style>
-
     body {
       background-color: #fff;
       color: #000;
@@ -22,10 +21,6 @@
       height: 100%;
     }
 
-    header, footer {
-      text-align: center;
-    }
-
     main {
       margin: 3px;
       display: flex;
@@ -99,6 +94,15 @@
       margin: 0.5em 0;
       display: block;
     }
+
+    header, footer {
+      text-align: center;
+    }
+
+    footer {
+      font-size: 80%;
+      color: #888;
+    }
   </style>
 
   <script type="module">
@@ -106,10 +110,10 @@
       html, h, signal, effect, computed, render, useSignal, useEffect, useRef
     } from '/index.js';
 
-    import { llamaComplete } from '/completion.js';
+    import { llama } from '/completion.js';
 
     const session = signal({
-      prompt: "This is a conversation between user and llama, a friendly chatbot. respond in markdown.",
+      prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
       template: "{{prompt}}\n\n{{history}}\n{{char}}:",
       historyTemplate: "{{name}}: {{message}}",
       transcript: [],
@@ -118,15 +122,6 @@
       user: "User",
     })
 
-    const transcriptUpdate = (transcript) => {
-      session.value = {
-        ...session.value,
-        transcript
-      }
-    }
-
-    const chatStarted = computed(() => session.value.transcript.length > 0)
-
     const params = signal({
       n_predict: 400,
       temperature: 0.7,
@@ -136,8 +131,18 @@
       top_p: 0.5,
     })
 
+    const llamaStats = signal(null)
     const controller = signal(null)
+
     const generating = computed(() => controller.value == null )
+    const chatStarted = computed(() => session.value.transcript.length > 0)
+
+    const transcriptUpdate = (transcript) => {
+      session.value = {
+        ...session.value,
+        transcript
+      }
+    }
 
     // simple template replace
     const template = (str, extraSettings) => {
@@ -158,7 +163,7 @@
 
       transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
 
-      const payload = template(session.value.template, {
+      const prompt = template(session.value.template, {
         message: msg,
         history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
       });
@@ -168,22 +173,26 @@
 
       const llamaParams = {
         ...params.value,
-        prompt: payload,
         stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
       }
 
-      await llamaComplete(llamaParams, controller.value, (message) => {
-        const data = message.data;
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+        const data = chunk.data;
         currentMessage += data.content;
+
         // remove leading whitespace
         currentMessage = currentMessage.replace(/^\s+/, "")
 
         transcriptUpdate([...history, ["{{char}}", currentMessage]])
 
         if (data.stop) {
-          console.log("-->", data, ' response was:', currentMessage, 'transcript state:', session.value.transcript);
+          console.log("Completion finished: '", currentMessage, "', summary: ", data);
         }
-      })
+
+        if (data.timings) {
+          llamaStats.value = data.timings;
+        }
+      }
 
       controller.value = null;
     }
@@ -219,13 +228,12 @@
       return html`
         <form onsubmit=${submit}>
           <div>
-          <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
-
+            <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
           </div>
           <div class="right">
-          <button type="submit" disabled=${!generating.value} >Send</button>
-          <button onclick=${stop} disabled=${generating}>Stop</button>
-          <button onclick=${reset}>Reset</button>
+            <button type="submit" disabled=${!generating.value} >Send</button>
+            <button onclick=${stop} disabled=${generating}>Stop</button>
+            <button onclick=${reset}>Reset</button>
           </div>
         </form>
       `
@@ -243,7 +251,7 @@
       }, [messages])
 
       const chatLine = ([user, msg]) => {
-        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdown} text=${template(msg)} /></p>`
+        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
       };
 
       return html`
@@ -313,39 +321,52 @@
         </form>
       `
     }
-const Markdown = (params) => {
-  const md = params.text
-    .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
-    .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
-    .replace(/__(.*?)__/g, '<strong>$1</strong>')
-    .replace(/\*(.*?)\*/g, '<em>$1</em>')
-    .replace(/_(.*?)_/g, '<em>$1</em>')
-    .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
-    .replace(/`(.*?)`/g, '<code>$1</code>')
-    .replace(/\n/gim, '<br />');
-  return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
-};
+    // poor mans markdown replacement
+    const Markdownish = (params) => {
+      const md = params.text
+        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
+        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*/g, '<em>$1</em>')
+        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
+        .replace(/`(.*?)`/g, '<code>$1</code>')
+        .replace(/\n/gim, '<br />');
+      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
+    };
+
+    const ModelGenerationInfo = (params) => {
+      if (!llamaStats.value) {
+        return html`<span/>`
+      }
+      return html`
+        <span>
+          ${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
+        </span>
+      `
+    }
 
     function App(props) {
 
       return html`
-      <div id="container">
-        <header>
-          <h1>llama.cpp</h1>
-        </header>
-
-        <main id="content">
-          <${chatStarted.value ? ChatLog : ConfigForm} />
-        </main>
-
-        <footer id="write">
-          <${MessageInput} />
-        </footer>
-
-        <footer>
-          <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a></p>
-        </footer>
-      </div>
+        <div id="container">
+          <header>
+            <h1>llama.cpp</h1>
+          </header>
+
+          <main id="content">
+            <${chatStarted.value ? ChatLog : ConfigForm} />
+          </main>
+
+          <section id="write">
+            <${MessageInput} />
+          </section>
+
+          <footer>
+            <p><${ModelGenerationInfo} /></p>
+            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+          </footer>
+        </div>
       `;
     }
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index a835c39..2cbfc00 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -22,7 +22,8 @@
 using namespace httplib;
 using json = nlohmann::json;
 
-struct server_params {
+struct server_params
+{
     std::string hostname = "127.0.0.1";
     std::string public_path = "examples/server/public";
     int32_t port = 8080;
@@ -31,8 +32,10 @@ struct server_params {
 };
 
 // completion token output with probabilities
-struct completion_token_output {
-    struct token_prob {
+struct completion_token_output
+{
+    struct token_prob
+    {
         llama_token tok;
         float prob;
     };
@@ -41,30 +44,40 @@ struct completion_token_output {
     llama_token tok;
 };
 
-static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
+static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
+{
     size_t i;
-    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
+    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++)
+    {
+    }
     return i;
 }
 
-enum stop_type {
+enum stop_type
+{
     STOP_FULL,
     STOP_PARTIAL,
 };
 
-static bool ends_with(const std::string & str, const std::string & suffix) {
+static bool ends_with(const std::string &str, const std::string &suffix)
+{
     return str.size() >= suffix.size() &&
-        0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
+           0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
 }
 
-static size_t find_partial_stop_string(const std::string & stop,
-                                       const std::string & text) {
-    if (!text.empty() && !stop.empty()) {
+static size_t find_partial_stop_string(const std::string &stop,
+                                       const std::string &text)
+{
+    if (!text.empty() && !stop.empty())
+    {
         const char text_last_char = text.back();
-        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
-            if (stop[char_index] == text_last_char) {
+        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--)
+        {
+            if (stop[char_index] == text_last_char)
+            {
                 const std::string current_partial = stop.substr(0, char_index + 1);
-                if (ends_with(text, current_partial)) {
+                if (ends_with(text, current_partial))
+                {
                     return text.size() - char_index - 1;
                 }
             }
@@ -73,26 +86,30 @@ static size_t find_partial_stop_string(const std::string & stop,
     return std::string::npos;
 }
 
-template<class Iter>
-static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
+template <class Iter>
+static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
+{
     std::string ret;
-    for (; begin != end; ++begin) {
+    for (; begin != end; ++begin)
+    {
         ret += llama_token_to_str(ctx, *begin);
     }
     return ret;
 }
 
-static void server_log(const char * level, const char * function, int line,
-                       const char * message, const nlohmann::ordered_json & extra) {
-    nlohmann::ordered_json log {
-        { "timestamp", time(nullptr) },
-        { "level", level },
-        { "function", function },
-        { "line", line },
-        { "message", message },
+static void server_log(const char *level, const char *function, int line,
+                       const char *message, const nlohmann::ordered_json &extra)
+{
+    nlohmann::ordered_json log{
+        {"timestamp", time(nullptr)},
+        {"level", level},
+        {"function", function},
+        {"line", line},
+        {"message", message},
     };
 
-    if (!extra.empty()) {
+    if (!extra.empty())
+    {
         log.merge_patch(extra);
     }
 
@@ -102,32 +119,37 @@ static void server_log(const char * level, const char * function, int line,
 }
 
 // format incomplete utf-8 multibyte character for output
-static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
+static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
+{
     std::string out = token == -1 ? "" : llama_token_to_str(ctx, token);
     // if first bit is 1, meaning it's a partial character
-    if (out.size() > 0 && (out[0] & 0x80) == 0x80) {
+    if (out.size() > 0 && (out[0] & 0x80) == 0x80)
+    {
         std::stringstream ss;
-        ss<< std::hex << (out[0] & 0xff);
-        std::string res ( ss.str() );
+        ss << std::hex << (out[0] & 0xff);
+        std::string res(ss.str());
         out = "byte: \\x" + res;
     }
     return out;
 }
 
 // convert a vector of completion_token_output to json
-static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> probs) {
+static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> probs)
+{
     json out = json::array();
-    for (const auto & prob : probs) {
+    for (const auto &prob : probs)
+    {
         json probs_for_token = json::array();
-        for (const auto & p : prob.probs) {
+        for (const auto &p : prob.probs)
+        {
             std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
-            probs_for_token.push_back(json {
-                { "tok_str", tok_str },
-                { "prob", p.prob },
+            probs_for_token.push_back(json{
+                {"tok_str", tok_str},
+                {"prob", p.prob},
             });
         }
         std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
-        out.push_back(json {
+        out.push_back(json{
             {"content", tok_str},
             {"probs", probs_for_token},
         });
@@ -138,21 +160,24 @@ static json probs_vector_to_json(const llama_context * ctx, const std::vector<co
 static bool server_verbose = false;
 
 #if SERVER_VERBOSE != 1
-#  define LOG_VERBOSE(MSG, ...)
+#define LOG_VERBOSE(MSG, ...)
 #else
-#  define LOG_VERBOSE(MSG, ...)                                          \
-    do {                                                                 \
-        if (server_verbose) {                                            \
+#define LOG_VERBOSE(MSG, ...)                                            \
+    do                                                                   \
+    {                                                                    \
+        if (server_verbose)                                              \
+        {                                                                \
             server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
         }                                                                \
-    } while(0)
+    } while (0)
 #endif
 
 #define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__)
 #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
 #define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
 
-struct llama_server_context {
+struct llama_server_context
+{
     bool stream = false;
     bool has_next_token = false;
     std::string generated_text;
@@ -166,8 +191,8 @@ struct llama_server_context {
     std::vector<llama_token> embd;
     std::vector<llama_token> last_n_tokens;
 
-    llama_model * model = nullptr;
-    llama_context * ctx = nullptr;
+    llama_model *model = nullptr;
+    llama_context *ctx = nullptr;
     gpt_params params;
 
     bool truncated = false;
@@ -179,22 +204,27 @@ struct llama_server_context {
 
     std::mutex mutex;
 
-    std::unique_lock<std::mutex> lock() {
+    std::unique_lock<std::mutex> lock()
+    {
         return std::unique_lock<std::mutex>(mutex);
     }
 
-    ~llama_server_context() {
-        if (ctx) {
+    ~llama_server_context()
+    {
+        if (ctx)
+        {
             llama_free(ctx);
             ctx = nullptr;
         }
-        if (model) {
+        if (model)
+        {
             llama_free_model(model);
             model = nullptr;
         }
     }
 
-    void rewind() {
+    void rewind()
+    {
         params.antiprompt.clear();
         num_prompt_tokens = 0;
         num_tokens_predicted = 0;
@@ -212,11 +242,13 @@ struct llama_server_context {
         n_past = 0;
     }
 
-    bool loadModel(const gpt_params & params_) {
+    bool loadModel(const gpt_params &params_)
+    {
         params = params_;
         std::tie(model, ctx) = llama_init_from_gpt_params(params);
-        if (model == nullptr) {
-            LOG_ERROR("unable to load model", { { "model", params_.model } });
+        if (model == nullptr)
+        {
+            LOG_ERROR("unable to load model", {{"model", params_.model}});
             return false;
         }
 
@@ -225,18 +257,21 @@ struct llama_server_context {
         return true;
     }
 
-    void loadPrompt() {
+    void loadPrompt()
+    {
         params.prompt.insert(0, 1, ' '); // always add a first space
         std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
         num_prompt_tokens = prompt_tokens.size();
 
-        if (params.n_keep < 0) {
+        if (params.n_keep < 0)
+        {
             params.n_keep = (int)num_prompt_tokens;
         }
         params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
 
         // if input prompt is too big, truncate like normal
-        if (num_prompt_tokens>= (size_t)params.n_ctx) {
+        if (num_prompt_tokens >= (size_t)params.n_ctx)
+        {
             const int n_left = (params.n_ctx - params.n_keep) / 2;
             std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
             const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
@@ -244,15 +279,17 @@ struct llama_server_context {
             std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin());
 
             LOG_VERBOSE("input truncated", {
-                { "n_ctx", params.n_ctx },
-                { "n_keep", params.n_keep },
-                { "n_left", n_left },
-                { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
-            });
+                                               {"n_ctx", params.n_ctx},
+                                               {"n_keep", params.n_keep},
+                                               {"n_left", n_left},
+                                               {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
+                                           });
 
             truncated = true;
             prompt_tokens = new_tokens;
-        } else {
+        }
+        else
+        {
             const size_t ps = num_prompt_tokens;
             std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
             std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
@@ -261,31 +298,35 @@ struct llama_server_context {
         // compare the evaluated prompt with the new prompt
         n_past = common_part(embd, prompt_tokens);
         embd = prompt_tokens;
-        if (n_past == num_prompt_tokens) {
+        if (n_past == num_prompt_tokens)
+        {
             // we have to evaluate at least 1 token to generate logits.
             n_past--;
         }
 
         LOG_VERBOSE("prompt ingested", {
-            { "n_past", n_past },
-            { "cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past) },
-            { "to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
-        });
+                                           {"n_past", n_past},
+                                           {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)},
+                                           {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
+                                       });
 
         has_next_token = true;
     }
 
-    void beginCompletion() {
+    void beginCompletion()
+    {
         // number of tokens to keep when resetting context
         n_remain = params.n_predict;
         llama_set_rng_seed(ctx, params.seed);
     }
 
-    completion_token_output nextToken() {
+    completion_token_output nextToken()
+    {
         completion_token_output result;
         result.tok = -1;
 
-        if (embd.size() >= (size_t)params.n_ctx) {
+        if (embd.size() >= (size_t)params.n_ctx)
+        {
             // Reset context
             const int n_left = (params.n_ctx - params.n_keep) / 2;
 
@@ -295,32 +336,36 @@ struct llama_server_context {
             n_past = params.n_keep;
             truncated = true;
             LOG_VERBOSE("input truncated", {
-                { "n_ctx", params.n_ctx },
-                { "n_keep", params.n_keep },
-                { "n_left", n_left },
-                { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
-            });
+                                               {"n_ctx", params.n_ctx},
+                                               {"n_keep", params.n_keep},
+                                               {"n_left", n_left},
+                                               {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
+                                           });
         }
 
-        while (n_past < embd.size()) {
+        while (n_past < embd.size())
+        {
             int n_eval = (int)embd.size() - n_past;
-            if (n_eval > params.n_batch) {
+            if (n_eval > params.n_batch)
+            {
                 n_eval = params.n_batch;
             }
-            if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads)) {
+            if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads))
+            {
                 LOG_ERROR("failed to eval", {
-                    { "n_eval", n_eval },
-                    { "n_past", n_past },
-                    { "n_threads", params.n_threads },
-                    { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
-                });
+                                                {"n_eval", n_eval},
+                                                {"n_past", n_past},
+                                                {"n_threads", params.n_threads},
+                                                {"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
+                                            });
                 has_next_token = false;
                 return result;
             }
             n_past += n_eval;
         }
 
-        if (params.n_predict == 0) {
+        if (params.n_predict == 0)
+        {
             has_next_token = false;
             result.tok = llama_token_eos();
             return result;
@@ -343,52 +388,64 @@ struct llama_server_context {
         const int32_t n_probs = params.n_probs;
 
         {
-            auto * logits = llama_get_logits(ctx);
+            auto *logits = llama_get_logits(ctx);
             auto n_vocab = llama_n_vocab(ctx);
 
             // Apply params.logit_bias map
-            for (const auto & it : params.logit_bias) {
+            for (const auto &it : params.logit_bias)
+            {
                 logits[it.first] += it.second;
             }
 
             std::vector<llama_token_data> candidates;
             candidates.reserve(n_vocab);
-            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-                candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
+            for (llama_token token_id = 0; token_id < n_vocab; token_id++)
+            {
+                candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
             }
 
-            llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+            llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
 
             // Apply penalties
             float nl_logit = logits[llama_token_nl()];
             auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx);
             llama_sample_repetition_penalty(ctx, &candidates_p,
-                last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-                last_n_repeat, repeat_penalty);
+                                            last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+                                            last_n_repeat, repeat_penalty);
             llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
-                last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-                last_n_repeat, alpha_frequency, alpha_presence);
-            if (!penalize_nl) {
+                                                          last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+                                                          last_n_repeat, alpha_frequency, alpha_presence);
+            if (!penalize_nl)
+            {
                 logits[llama_token_nl()] = nl_logit;
             }
 
-            if (temp <= 0) {
+            if (temp <= 0)
+            {
                 // Greedy sampling
                 result.tok = llama_sample_token_greedy(ctx, &candidates_p);
-                if (n_probs > 0) {
+                if (n_probs > 0)
+                {
                     llama_sample_softmax(ctx, &candidates_p);
                 }
-            } else {
-                if (mirostat == 1) {
+            }
+            else
+            {
+                if (mirostat == 1)
+                {
                     static float mirostat_mu = 2.0f * mirostat_tau;
                     const int mirostat_m = 100;
                     llama_sample_temperature(ctx, &candidates_p, temp);
                     result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
-                } else if (mirostat == 2) {
+                }
+                else if (mirostat == 2)
+                {
                     static float mirostat_mu = 2.0f * mirostat_tau;
                     llama_sample_temperature(ctx, &candidates_p, temp);
                     result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
-                } else {
+                }
+                else
+                {
                     // Temperature sampling
                     size_t min_keep = std::max(1, n_probs);
                     llama_sample_top_k(ctx, &candidates_p, top_k, min_keep);
@@ -400,7 +457,8 @@ struct llama_server_context {
                 }
             }
 
-            for (size_t i = 0; i < std::min(candidates_p.size, (size_t) n_probs); ++i) {
+            for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
+            {
                 result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
             }
             last_n_tokens.erase(last_n_tokens.begin());
@@ -413,8 +471,9 @@ struct llama_server_context {
         // decrement remaining sampling budget
         --n_remain;
 
-        if (!embd.empty() && embd.back() == llama_token_eos()) {
-            //stopping_word = llama_token_to_str(ctx, embd.back());
+        if (!embd.empty() && embd.back() == llama_token_eos())
+        {
+            // stopping_word = llama_token_to_str(ctx, embd.back());
             has_next_token = false;
             stopped_eos = true;
             LOG_VERBOSE("eos token found", {});
@@ -425,22 +484,28 @@ struct llama_server_context {
         return result;
     }
 
-    size_t findStoppingStrings(const std::string & text, const size_t last_token_size,
-                               const stop_type type) {
+    size_t findStoppingStrings(const std::string &text, const size_t last_token_size,
+                               const stop_type type)
+    {
         size_t stop_pos = std::string::npos;
-        for (const std::string & word : params.antiprompt) {
+        for (const std::string &word : params.antiprompt)
+        {
             size_t pos;
-            if (type == STOP_FULL) {
+            if (type == STOP_FULL)
+            {
                 const size_t tmp = word.size() + last_token_size;
                 const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
                 pos = text.find(word, from_pos);
             }
-            else {
+            else
+            {
                 pos = find_partial_stop_string(word, text);
             }
             if (pos != std::string::npos &&
-                (stop_pos == std::string::npos || pos < stop_pos)) {
-                if (type == STOP_FULL) {
+                (stop_pos == std::string::npos || pos < stop_pos))
+            {
+                if (type == STOP_FULL)
+                {
                     stopping_word = word;
                     stopped_word = true;
                     has_next_token = false;
@@ -451,74 +516,91 @@ struct llama_server_context {
         return stop_pos;
     }
 
-    completion_token_output doCompletion() {
+    completion_token_output doCompletion()
+    {
         const completion_token_output token_with_probs = nextToken();
 
         const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
         generated_text += token_text;
 
-        if (params.n_probs > 0) {
+        if (params.n_probs > 0)
+        {
             generated_token_probs.push_back(token_with_probs);
         }
 
-        if (multibyte_pending > 0) {
+        if (multibyte_pending > 0)
+        {
             multibyte_pending -= token_text.size();
-        } else if (token_text.size() == 1) {
+        }
+        else if (token_text.size() == 1)
+        {
             const char c = token_text[0];
             // 2-byte characters: 110xxxxx 10xxxxxx
-            if ((c & 0xE0) == 0xC0) {
+            if ((c & 0xE0) == 0xC0)
+            {
                 multibyte_pending = 1;
-            // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
-            } else if ((c & 0xF0) == 0xE0) {
+                // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
+            }
+            else if ((c & 0xF0) == 0xE0)
+            {
                 multibyte_pending = 2;
-            // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
-            } else if ((c & 0xF8) == 0xF0) {
+                // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
+            }
+            else if ((c & 0xF8) == 0xF0)
+            {
                 multibyte_pending = 3;
-            } else {
+            }
+            else
+            {
                 multibyte_pending = 0;
             }
         }
 
-        if (multibyte_pending > 0 && !has_next_token) {
+        if (multibyte_pending > 0 && !has_next_token)
+        {
             has_next_token = true;
             n_remain++;
         }
 
-        if (!has_next_token && n_remain == 0) {
+        if (!has_next_token && n_remain == 0)
+        {
             stopped_limit = true;
         }
 
         LOG_VERBOSE("next token", {
-            { "token", token_with_probs.tok },
-            { "token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok) },
-            { "has_next_token", has_next_token },
-            { "n_remain", n_remain },
-            { "num_tokens_predicted", num_tokens_predicted },
-            { "stopped_eos", stopped_eos },
-            { "stopped_word", stopped_word },
-            { "stopped_limit", stopped_limit },
-            { "stopping_word", stopping_word },
-        });
+                                      {"token", token_with_probs.tok},
+                                      {"token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok)},
+                                      {"has_next_token", has_next_token},
+                                      {"n_remain", n_remain},
+                                      {"num_tokens_predicted", num_tokens_predicted},
+                                      {"stopped_eos", stopped_eos},
+                                      {"stopped_word", stopped_word},
+                                      {"stopped_limit", stopped_limit},
+                                      {"stopping_word", stopping_word},
+                                  });
 
         return token_with_probs;
     }
 
-    std::vector<float> getEmbedding() {
+    std::vector<float> getEmbedding()
+    {
         static const int n_embd = llama_n_embd(ctx);
-        if (!params.embedding) {
+        if (!params.embedding)
+        {
             LOG_WARNING("embedding disabled", {
-                { "params.embedding", params.embedding },
-            });
+                                                  {"params.embedding", params.embedding},
+                                              });
             return std::vector<float>(n_embd, 0.0f);
         }
-        const float * data = llama_get_embeddings(ctx);
+        const float *data = llama_get_embeddings(ctx);
         std::vector<float> embedding(data, data + n_embd);
         return embedding;
     }
 };
 
-static void server_print_usage(const char * argv0, const gpt_params & params,
-                               const server_params & sparams) {
+static void server_print_usage(const char *argv0, const gpt_params &params,
+                               const server_params &sparams)
+{
     fprintf(stderr, "usage: %s [options]\n", argv0);
     fprintf(stderr, "\n");
     fprintf(stderr, "options:\n");
@@ -529,10 +611,12 @@ static void server_print_usage(const char * argv0, const gpt_params & params,
     fprintf(stderr, "  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     fprintf(stderr, "  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
     fprintf(stderr, "                        not recommended: doubles context memory required and no measurable increase in quality\n");
-    if (llama_mlock_supported()) {
+    if (llama_mlock_supported())
+    {
         fprintf(stderr, "  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
     }
-    if (llama_mmap_supported()) {
+    if (llama_mmap_supported())
+    {
         fprintf(stderr, "  --no-mmap             do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
     }
 #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
@@ -558,78 +642,113 @@ static void server_print_usage(const char * argv0, const gpt_params & params,
     fprintf(stderr, "\n");
 }
 
-static void server_params_parse(int argc, char ** argv, server_params & sparams,
-                                gpt_params & params) {
+static void server_params_parse(int argc, char **argv, server_params &sparams,
+                                gpt_params &params)
+{
     gpt_params default_params;
     server_params default_sparams;
     std::string arg;
     bool invalid_param = false;
 
-    for (int i = 1; i < argc; i++) {
+    for (int i = 1; i < argc; i++)
+    {
         arg = argv[i];
-        if (arg == "--port") {
-            if (++i >= argc) {
+        if (arg == "--port")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             sparams.port = std::stoi(argv[i]);
-        } else if (arg == "--host") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--host")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             sparams.hostname = argv[i];
-        } else if (arg == "--path") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--path")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             sparams.public_path = argv[i];
-        } else if (arg == "--timeout" || arg == "-to") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--timeout" || arg == "-to")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             sparams.read_timeout = std::stoi(argv[i]);
             sparams.write_timeout = std::stoi(argv[i]);
-        } else if (arg == "-m" || arg == "--model") {
-            if (++i >= argc) {
+        }
+        else if (arg == "-m" || arg == "--model")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.model = argv[i];
-        } else if (arg == "-a" || arg == "--alias") {
-            if (++i >= argc) {
+        }
+        else if (arg == "-a" || arg == "--alias")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.model_alias = argv[i];
-        } else if (arg == "-h" || arg == "--help") {
+        }
+        else if (arg == "-h" || arg == "--help")
+        {
             server_print_usage(argv[0], default_params, default_sparams);
             exit(0);
-        } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") {
-            if (++i >= argc) {
+        }
+        else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.n_ctx = std::stoi(argv[i]);
-        } else if (arg == "--memory-f32" || arg == "--memory_f32") {
+        }
+        else if (arg == "--memory-f32" || arg == "--memory_f32")
+        {
             params.memory_f16 = false;
-        } else if (arg == "--threads" || arg == "-t") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--threads" || arg == "-t")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.n_threads = std::stoi(argv[i]);
-        } else if (arg == "-b" || arg == "--batch-size") {
-            if (++i >= argc) {
+        }
+        else if (arg == "-b" || arg == "--batch-size")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.n_batch = std::stoi(argv[i]);
             params.n_batch = std::min(512, params.n_batch);
-        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
@@ -637,11 +756,14 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
             params.n_gpu_layers = std::stoi(argv[i]);
 #else
             LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
-                        "See main README.md for information on enabling GPU BLAS support", { { "n_gpu_layers", params.n_gpu_layers } });
+                        "See main README.md for information on enabling GPU BLAS support",
+                        {{"n_gpu_layers", params.n_gpu_layers}});
 #endif
         }
-        else if (arg == "--tensor-split" || arg == "-ts") {
-            if (++i >= argc) {
+        else if (arg == "--tensor-split" || arg == "-ts")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
@@ -649,16 +771,19 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
             std::string arg_next = argv[i];
 
             // split string by , and /
-            const std::regex regex{ R"([,/]+)" };
-            std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
-            std::vector<std::string> split_arg{ it, {} };
+            const std::regex regex{R"([,/]+)"};
+            std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
+            std::vector<std::string> split_arg{it, {}};
             GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
 
-            for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) {
-                if (i_device < split_arg.size()) {
+            for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device)
+            {
+                if (i_device < split_arg.size())
+                {
                     params.tensor_split[i_device] = std::stof(split_arg[i_device]);
                 }
-                else {
+                else
+                {
                     params.tensor_split[i_device] = 0.0f;
                 }
             }
@@ -674,8 +799,10 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
             fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
 #endif // GGML_USE_CUBLAS
         }
-        else if (arg == "--main-gpu" || arg == "-mg") {
-            if (++i >= argc) {
+        else if (arg == "--main-gpu" || arg == "-mg")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
@@ -684,125 +811,173 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
 #else
             LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
 #endif
-        } else if (arg == "--lora") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--lora")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.lora_adapter = argv[i];
             params.use_mmap = false;
-        } else if (arg == "--lora-base") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--lora-base")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.lora_base = argv[i];
-        } else if (arg == "-v" || arg == "--verbose") {
+        }
+        else if (arg == "-v" || arg == "--verbose")
+        {
 #if SERVER_VERBOSE != 1
             LOG_WARNING("server.cpp is not built with verbose logging.", {});
 #else
             server_verbose = true;
 #endif
-        } else if (arg == "--mlock") {
+        }
+        else if (arg == "--mlock")
+        {
             params.use_mlock = true;
-        } else if (arg == "--no-mmap") {
+        }
+        else if (arg == "--no-mmap")
+        {
             params.use_mmap = false;
-        } else if (arg == "--embedding") {
+        }
+        else if (arg == "--embedding")
+        {
             params.embedding = true;
-        } else {
+        }
+        else
+        {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             server_print_usage(argv[0], default_params, default_sparams);
             exit(1);
         }
     }
 
-    if (invalid_param) {
+    if (invalid_param)
+    {
         fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
         server_print_usage(argv[0], default_params, default_sparams);
         exit(1);
     }
 }
 
-static json format_generation_settings(llama_server_context & llama) {
+static json format_generation_settings(llama_server_context &llama)
+{
     const auto eos_bias = llama.params.logit_bias.find(llama_token_eos());
     const bool ignore_eos = eos_bias != llama.params.logit_bias.end() &&
-        eos_bias->second < 0.0f && std::isinf(eos_bias->second);
-
-    return json {
-        { "seed", llama.params.seed },
-        { "temp", llama.params.temp },
-        { "top_k", llama.params.top_k },
-        { "top_p", llama.params.top_p },
-        { "tfs_z", llama.params.tfs_z },
-        { "typical_p", llama.params.typical_p },
-        { "repeat_last_n", llama.params.repeat_last_n },
-        { "repeat_penalty", llama.params.repeat_penalty },
-        { "presence_penalty", llama.params.presence_penalty },
-        { "frequency_penalty", llama.params.frequency_penalty },
-        { "mirostat", llama.params.mirostat },
-        { "mirostat_tau", llama.params.mirostat_tau },
-        { "mirostat_eta", llama.params.mirostat_eta },
-        { "penalize_nl", llama.params.penalize_nl },
-        { "stop", llama.params.antiprompt },
-        { "n_predict", llama.params.n_predict },
-        { "n_keep", llama.params.n_keep },
-        { "ignore_eos", ignore_eos },
-        { "stream", llama.stream },
-        { "logit_bias", llama.params.logit_bias },
-        { "n_probs", llama.params.n_probs },
+                            eos_bias->second < 0.0f && std::isinf(eos_bias->second);
+
+    return json{
+        {"n_ctx", llama.params.n_ctx},
+        {"model", llama.params.model_alias},
+        {"seed", llama.params.seed},
+        {"temp", llama.params.temp},
+        {"top_k", llama.params.top_k},
+        {"top_p", llama.params.top_p},
+        {"tfs_z", llama.params.tfs_z},
+        {"typical_p", llama.params.typical_p},
+        {"repeat_last_n", llama.params.repeat_last_n},
+        {"repeat_penalty", llama.params.repeat_penalty},
+        {"presence_penalty", llama.params.presence_penalty},
+        {"frequency_penalty", llama.params.frequency_penalty},
+        {"mirostat", llama.params.mirostat},
+        {"mirostat_tau", llama.params.mirostat_tau},
+        {"mirostat_eta", llama.params.mirostat_eta},
+        {"penalize_nl", llama.params.penalize_nl},
+        {"stop", llama.params.antiprompt},
+        {"n_predict", llama.params.n_predict},
+        {"n_keep", llama.params.n_keep},
+        {"ignore_eos", ignore_eos},
+        {"stream", llama.stream},
+        {"logit_bias", llama.params.logit_bias},
+        {"n_probs", llama.params.n_probs},
+    };
+}
+
+static json format_embedding_response(llama_server_context &llama)
+{
+    return json{
+        {"embedding", llama.getEmbedding()},
     };
 }
 
-static json format_embedding_response(llama_server_context & llama) {
-    return json {
-        { "embedding", llama.getEmbedding() },
+static json format_timings(llama_server_context &llama)
+{
+    const auto timings = llama_get_timings(llama.ctx);
+
+    assert(timings.n_eval == llama.num_tokens_predicted);
+
+    return json{
+        {"prompt_n", timings.n_eval},
+        {"prompt_ms", timings.t_p_eval_ms},
+        {"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval},
+        {"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval},
+
+        {"predicted_n", timings.n_eval},
+        {"predicted_ms", timings.t_eval_ms},
+        {"predicted_per_token_ms", timings.t_eval_ms / timings.n_eval},
+        {"predicted_per_second", 1e3 / timings.t_eval_ms * timings.n_eval},
     };
 }
 
-static json format_final_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) {
-
-    json res = json {
-        { "content", content },
-        { "stop", true },
-        { "model", llama.params.model_alias },
-        { "tokens_predicted", llama.num_tokens_predicted },
-        { "tokens_evaluated", llama.num_prompt_tokens },
-        { "generation_settings", format_generation_settings(llama) },
-        { "prompt", llama.params.prompt },
-        { "truncated", llama.truncated },
-        { "stopped_eos", llama.stopped_eos },
-        { "stopped_word", llama.stopped_word },
-        { "stopped_limit", llama.stopped_limit },
-        { "stopping_word", llama.stopping_word },
+static json format_final_response(llama_server_context &llama, const std::string &content, const std::vector<completion_token_output> &probs)
+{
+
+    json res = json{
+        {"content", content},
+        {"stop", true},
+        {"model", llama.params.model_alias},
+        {"tokens_predicted", llama.num_tokens_predicted},
+        {"tokens_evaluated", llama.num_prompt_tokens},
+        {"generation_settings", format_generation_settings(llama)},
+        {"prompt", llama.params.prompt},
+        {"truncated", llama.truncated},
+        {"stopped_eos", llama.stopped_eos},
+        {"stopped_word", llama.stopped_word},
+        {"stopped_limit", llama.stopped_limit},
+        {"stopping_word", llama.stopping_word},
+        {"tokens_cached", llama.n_past},
+        {"tokens_predicted", llama.num_tokens_predicted},
+        {"timings", format_timings(llama)},
     };
 
-    if (llama.params.n_probs > 0) {
+    if (llama.params.n_probs > 0)
+    {
         res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
     }
 
     return res;
 }
 
-static json format_partial_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) {
-    json res = json {
-        { "content", content },
-        { "stop", false },
+static json format_partial_response(llama_server_context &llama, const std::string &content, const std::vector<completion_token_output> &probs)
+{
+    json res = json{
+        {"content", content},
+        {"stop", false},
     };
 
-    if (llama.params.n_probs > 0) {
+    if (llama.params.n_probs > 0)
+    {
         res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
     }
 
     return res;
 }
 
-static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
-    return json {
-        { "tokens", tokens }
-    };
+static json format_tokenizer_response(const std::vector<llama_token> &tokens)
+{
+    return json{
+        {"tokens", tokens}};
 }
 
-static void parse_options_completion(const json & body, llama_server_context & llama) {
+static void parse_options_completion(const json &body, llama_server_context &llama)
+{
     gpt_params default_params;
 
     llama.stream = body.value("stream", false);
@@ -826,20 +1001,28 @@ static void parse_options_completion(const json & body, llama_server_context & l
     llama.params.n_probs = body.value("n_probs", default_params.n_probs);
 
     llama.params.logit_bias.clear();
-    if (body.value("ignore_eos", false)) {
+    if (body.value("ignore_eos", false))
+    {
         llama.params.logit_bias[llama_token_eos()] = -INFINITY;
     }
 
-    const auto & logit_bias = body.find("logit_bias");
-    if (logit_bias != body.end() && logit_bias->is_array()) {
+    const auto &logit_bias = body.find("logit_bias");
+    if (logit_bias != body.end() && logit_bias->is_array())
+    {
         const int n_vocab = llama_n_vocab(llama.ctx);
-        for (const auto & el : *logit_bias) {
-            if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) {
+        for (const auto &el : *logit_bias)
+        {
+            if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
+            {
                 llama_token tok = el[0].get<llama_token>();
-                if (tok >= 0 && tok < n_vocab) {
-                    if (el[1].is_number()) {
+                if (tok >= 0 && tok < n_vocab)
+                {
+                    if (el[1].is_number())
+                    {
                         llama.params.logit_bias[tok] = el[1].get<float>();
-                    } else if (el[1].is_boolean() && !el[1].get<bool>()) {
+                    }
+                    else if (el[1].is_boolean() && !el[1].get<bool>())
+                    {
                         llama.params.logit_bias[tok] = -INFINITY;
                     }
                 }
@@ -848,10 +1031,13 @@ static void parse_options_completion(const json & body, llama_server_context & l
     }
 
     llama.params.antiprompt.clear();
-    const auto & stop = body.find("stop");
-    if (stop != body.end() && stop->is_array()) {
-        for (const auto & word : *stop) {
-            if (!word.empty()) {
+    const auto &stop = body.find("stop");
+    if (stop != body.end() && stop->is_array())
+    {
+        for (const auto &word : *stop)
+        {
+            if (!word.empty())
+            {
                 llama.params.antiprompt.push_back(word);
             }
         }
@@ -860,25 +1046,25 @@ static void parse_options_completion(const json & body, llama_server_context & l
     LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama));
 }
 
-
-static void log_server_request(const Request & req, const Response & res) {
+static void log_server_request(const Request &req, const Response &res)
+{
     LOG_INFO("request", {
-        { "remote_addr", req.remote_addr },
-        { "remote_port", req.remote_port },
-        { "status", res.status },
-        { "method", req.method },
-        { "path", req.path },
-        { "params", req.params },
-    });
+                            {"remote_addr", req.remote_addr},
+                            {"remote_port", req.remote_port},
+                            {"status", res.status},
+                            {"method", req.method},
+                            {"path", req.path},
+                            {"params", req.params},
+                        });
 
     LOG_VERBOSE("request", {
-        { "request", req.body },
-        { "response", res.body },
-    });
+                               {"request", req.body},
+                               {"response", res.body},
+                           });
 }
 
-
-int main(int argc, char ** argv) {
+int main(int argc, char **argv)
+{
     // own arguments required by this example
     gpt_params params;
     server_params sparams;
@@ -888,54 +1074,53 @@ int main(int argc, char ** argv) {
 
     server_params_parse(argc, argv, sparams, params);
 
-    if (params.model_alias == "unknown") {
+    if (params.model_alias == "unknown")
+    {
         params.model_alias = params.model;
     }
 
     llama_init_backend(params.numa);
 
-    LOG_INFO("build info", {
-        { "build", BUILD_NUMBER },
-        { "commit", BUILD_COMMIT }
-    });
+    LOG_INFO("build info", {{"build", BUILD_NUMBER},
+                            {"commit", BUILD_COMMIT}});
     LOG_INFO("system info", {
-        { "n_threads", params.n_threads },
-        { "total_threads", std::thread::hardware_concurrency() },
-        { "system_info", llama_print_system_info() },
-    });
+                                {"n_threads", params.n_threads},
+                                {"total_threads", std::thread::hardware_concurrency()},
+                                {"system_info", llama_print_system_info()},
+                            });
 
     // load the model
-    if (!llama.loadModel(params)) {
+    if (!llama.loadModel(params))
+    {
         return 1;
     }
 
     Server svr;
 
-    svr.set_default_headers({
-        { "Server", "llama.cpp" },
-        { "Access-Control-Allow-Origin", "*" },
-        { "Access-Control-Allow-Headers", "content-type" }
-    });
-
-    // this is only called if no index.js is found in the public --path
-    svr.Get("/index.js", [](const Request &, Response & res) {
-        res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
-        return false;
-    });
+    svr.set_default_headers({{"Server", "llama.cpp"},
+                             {"Access-Control-Allow-Origin", "*"},
+                             {"Access-Control-Allow-Headers", "content-type"}});
 
     // this is only called if no index.html is found in the public --path
-    svr.Get("/", [](const Request &, Response & res) {
+    svr.Get("/", [](const Request &, Response &res)
+            {
         res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
-        return false;
-    });
+        return false; });
+
+    // this is only called if no index.js is found in the public --path
+    svr.Get("/index.js", [](const Request &, Response &res)
+            {
+        res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
+        return false; });
 
     // this is only called if no index.html is found in the public --path
-    svr.Get("/completion.js", [](const Request &, Response & res) {
+    svr.Get("/completion.js", [](const Request &, Response &res)
+            {
         res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
-        return false;
-    });
+        return false; });
 
-    svr.Post("/completion", [&llama](const Request & req, Response & res) {
+    svr.Post("/completion", [&llama](const Request &req, Response &res)
+             {
         auto lock = llama.lock();
 
         llama.rewind();
@@ -1040,24 +1225,28 @@ int main(int argc, char ** argv) {
                 return true;
             };
             res.set_chunked_content_provider("text/event-stream", chunked_content_provider);
-        }
-    });
+        } });
+
+    svr.Get("/model.json", [&llama](const Request &, Response &res)
+            {
+        const json data = format_generation_settings(llama);
+        return res.set_content(data.dump(), "application/json"); });
 
-    svr.Options(R"(/.*)", [](const Request &, Response & res) {
-        return res.set_content("", "application/json");
-    });
+    svr.Options(R"(/.*)", [](const Request &, Response &res)
+                { return res.set_content("", "application/json"); });
 
-    svr.Post("/tokenize", [&llama](const Request & req, Response & res) {
+    svr.Post("/tokenize", [&llama](const Request &req, Response &res)
+             {
         auto lock = llama.lock();
 
         const json body = json::parse(req.body);
         const std::string content = body.value("content", "");
         const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
         const json data = format_tokenizer_response(tokens);
-        return res.set_content(data.dump(), "application/json");
-    });
+        return res.set_content(data.dump(), "application/json"); });
 
-    svr.Post("/embedding", [&llama](const Request & req, Response & res) {
+    svr.Post("/embedding", [&llama](const Request &req, Response &res)
+             {
         auto lock = llama.lock();
 
         const json body = json::parse(req.body);
@@ -1071,12 +1260,12 @@ int main(int argc, char ** argv) {
         llama.doCompletion();
 
         const json data = format_embedding_response(llama);
-        return res.set_content(data.dump(), "application/json");
-    });
+        return res.set_content(data.dump(), "application/json"); });
 
     svr.set_logger(log_server_request);
 
-    svr.set_exception_handler([](const Request &, Response & res, std::exception_ptr ep) {
+    svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep)
+                              {
         const auto * fmt = "500 Internal Server Error\n%s";
         char buf[BUFSIZ];
         try {
@@ -1087,20 +1276,19 @@ int main(int argc, char ** argv) {
             snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
         }
         res.set_content(buf, "text/plain");
-        res.status = 500;
-    });
+        res.status = 500; });
 
-    svr.set_error_handler([](const Request &, Response & res) {
+    svr.set_error_handler([](const Request &, Response &res)
+                          {
         res.set_content("File Not Found", "text/plain");
-        res.status = 404;
-    });
-
+        res.status = 404; });
 
     // set timeouts and change hostname and port
     svr.set_read_timeout(sparams.read_timeout);
     svr.set_write_timeout(sparams.write_timeout);
 
-    if (!svr.bind_to_port(sparams.hostname, sparams.port)) {
+    if (!svr.bind_to_port(sparams.hostname, sparams.port))
+    {
         fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
         return 1;
     }
@@ -1112,11 +1300,12 @@ int main(int argc, char ** argv) {
     fprintf(stdout, "\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
 
     LOG_INFO("HTTP server listening", {
-        { "hostname", sparams.hostname },
-        { "port", sparams.port },
-    });
+                                          {"hostname", sparams.hostname},
+                                          {"port", sparams.port},
+                                      });
 
-    if (!svr.listen_after_bind()) {
+    if (!svr.listen_after_bind())
+    {
         return 1;
     }
 
diff --git a/llama.cpp b/llama.cpp
index 7a866cb..02afdeb 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -3480,23 +3480,35 @@ llama_token llama_token_nl() {
     return 13;
 }
 
+struct llama_timings llama_get_timings(struct llama_context * ctx) {
+    struct llama_timings result = {
+        /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
+        /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
+        /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
+        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
+        /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
+        /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,
+
+        /*.n_sample =*/ std::max(1, ctx->n_sample),
+        /*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
+        /*.n_eval   =*/ std::max(1, ctx->n_eval),
+    };
 
-void llama_print_timings(struct llama_context * ctx) {
-    const int64_t t_end_us = ggml_time_us();
+    return result;
+}
 
-    const int32_t n_sample = std::max(1, ctx->n_sample);
-    const int32_t n_eval   = std::max(1, ctx->n_eval);
-    const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
+void llama_print_timings(struct llama_context * ctx) {
+    const llama_timings timings = llama_get_timings(ctx);
 
     fprintf(stderr, "\n");
-    fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
+    fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, timings.t_load_ms);
     fprintf(stderr, "%s:      sample time = %8.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample, 1e6 / ctx->t_sample_us * n_sample);
+            __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
     fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval, 1e6 / ctx->t_p_eval_us * n_p_eval);
+            __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
     fprintf(stderr, "%s:        eval time = %8.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_eval_us,   n_eval,   1e-3 * ctx->t_eval_us   / n_eval,   1e6 / ctx->t_eval_us   * n_eval);
-    fprintf(stderr, "%s:       total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
+            __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
+    fprintf(stderr, "%s:       total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
 }
 
 void llama_reset_timings(struct llama_context * ctx) {
diff --git a/llama.h b/llama.h
index 5bb1964..c1e7dab 100644
--- a/llama.h
+++ b/llama.h
@@ -134,6 +134,20 @@ extern "C" {
         bool quantize_output_tensor; // quantize output.weight
     } llama_model_quantize_params;
 
+    // performance timing information
+    struct llama_timings {
+        double t_start_ms;
+        double t_end_ms;
+        double t_load_ms;
+        double t_sample_ms;
+        double t_p_eval_ms;
+        double t_eval_ms;
+
+        int32_t n_sample;
+        int32_t n_p_eval;
+        int32_t n_eval;
+    };
+
     LLAMA_API struct llama_context_params llama_context_default_params();
     LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
 
@@ -331,6 +345,7 @@ extern "C" {
     LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
 
     // Performance information
+    LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
     LLAMA_API void llama_print_timings(struct llama_context * ctx);
     LLAMA_API void llama_reset_timings(struct llama_context * ctx);
author	Tobias Lütke <tobi@shopify.com>	2023-07-05 16:51:13 -0400
committer	GitHub <noreply@github.com>	2023-07-05 16:51:13 -0400
commit	31cfbb1013a482e89c72146e2063ac4362becae7 (patch)
tree	b6479860979f90d185a132efe883ae7fb62b07f5
parent	983b555e9ddb36703cee4d22642afe958de093b7 (diff)