diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
index 5bca2a8ab..2a6b585ae 100644
--- a/examples/server/index.html.hpp
+++ b/examples/server/index.html.hpp
@@ -23,10 +23,32 @@ unsigned char index_html[] = {
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@@ -34,382 +56,424 @@ unsigned char index_html[] = {
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+  0x53, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, 0x2e, 0x76, 0x61, 0x6c, 0x75,
+  0x65, 0x20, 0x3f, 0x20, 0x43, 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, 0x20,
+  0x3a, 0x20, 0x43, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d,
+  0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x3c, 0x2f, 0x6d, 0x61, 0x69, 0x6e, 0x3e, 0x0a, 0x0a,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73,
+  0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x77,
+  0x72, 0x69, 0x74, 0x65, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x4d, 0x65,
+  0x73, 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, 0x70, 0x75, 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, 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, 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 = 13791;
+unsigned int index_html_len = 17647;
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 1db69a6ba..394346e1a 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -10,6 +10,11 @@
     body {
       font-family: system-ui;
       font-size: 90%;
+      max-width: 600px;
+      min-width: 300px;
+      line-height: 1.2;
+      margin: 0 auto;
+      padding: 0 0.5em;
     }
 
     #container {
@@ -35,12 +40,37 @@
       padding: 0.5em;
     }
 
-    body {
-      max-width: 600px;
-      min-width: 300px;
-      line-height: 1.2;
-      margin: 0 auto;
-      padding: 0 0.5em;
+    #chat {
+      display: flex;
+      flex-direction: column;
+    }
+
+    .bubble {
+      border: 1px solid;
+      border-radius: 1.0em;
+      padding: 0.5em;
+      max-width: 75%;
+      margin-top: 0;
+    }
+
+    .user {
+      background-color: #161616;
+      color: #d6d6d6;
+      border-bottom-right-radius: 0;
+      place-self: flex-end;
+    }
+
+    .asst {
+      background-color: #d6d6d6;
+      color: #161616;
+      border-bottom-left-radius: 0;
+      place-self: flex-start;
+    }
+
+    .typing {
+      color: #888;
+      text-align: left;
+      font-size: 120%;
     }
 
     p {
@@ -102,7 +132,6 @@
       padding: 0.5em;
     }
 
-
     textarea {
       padding: 5px;
       flex-grow: 1;
@@ -138,34 +167,35 @@
   <script type="module">
     import {
       html, h, signal, effect, computed, render, useSignal, useEffect, useRef
-    } from '/index.js';
+    } from '/index.js'
 
-    import { llama } 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 simple markdown.",
-      template: "{{prompt}}\n\n{{history}}\n{{char}}:",
-      historyTemplate: "{{name}}: {{message}}",
+      system: "A chat between a curious user and a pirate.",
+      system_cfg: "A chat between a curious user and an artificial intelligence assistant.",
+      message: "{{system}}\n\n### Instruction:\n{{user}}\n\n### Response:\n{{assistant}}",
+      stop: ["###"],
       transcript: [],
       type: "chat",
-      char: "llama",
-      user: "User",
     })
 
     const params = signal({
+      cfg_scale: 4.0,
+      frequency_penalty: 0.0, // 0.0 = disabled
+      mirostat_eta: 0.1, // learning rate
+      mirostat_tau: 5, // target entropy
+      mirostat: 0, // 0/1/2
       n_predict: 400,
-      temperature: 0.7,
+      penalize_nl: true,
+      presence_penalty: 0.0, // 0.0 = disabled
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
+      temperature: 0.7,
+      tfs_z: 1.0, // 1.0 = disabled
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.5, // 1.0 = disabled
-      tfs_z: 1.0, // 1.0 = disabled
       typical_p: 1.0, // 1.0 = disabled
-      presence_penalty: 0.0, // 0.0 = disabled
-      frequency_penalty: 0.0, // 0.0 = disabled
-      mirostat: 0, // 0/1/2
-      mirostat_tau: 5, // target entropy
-      mirostat_eta: 0.1, // learning rate
     })
 
     const llamaStats = signal(null)
@@ -177,88 +207,92 @@
     const transcriptUpdate = (transcript) => {
       session.value = {
         ...session.value,
-        transcript
+        transcript,
       }
     }
 
     // simple template replace
     const template = (str, extraSettings) => {
-      let settings = session.value;
+      let settings = session.value
       if (extraSettings) {
-        settings = { ...settings, ...extraSettings };
+        settings = { ...settings, ...extraSettings }
       }
-      return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
+      return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]))
     }
 
     // send message to server
     const chat = async (msg) => {
       if (controller.value) {
-        console.log('already running...');
-        return;
+        console.log('already running...')
+        return
       }
-      controller.value = new AbortController();
+      controller.value = new AbortController()
 
-      transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
-
-      const prompt = template(session.value.template, {
-        message: msg,
-        history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
-      });
-
-      let currentMessage = '';
       const history = session.value.transcript
+      const system = history.length == 0 ? session.value.system : ""
+      transcriptUpdate([...history, { system, user: msg, assistant: "" }])
+
+      const prompt = session.value.transcript.map(t =>
+        template(session.value.message, t)).join("").trimEnd()
+
+      const cfg_negative_prompt = params.value.cfg_scale > 1 ? session.value.transcript.map(t =>
+        template(session.value.message, { ...t, system: session.value.system_cfg })
+      ).join("").trimEnd() : ""
+
+      let currentMessage = ''
 
       const llamaParams = {
         ...params.value,
-        stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
+        cfg_negative_prompt,
+        stop: session.stop,
       }
 
       for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
-        const data = chunk.data;
-        currentMessage += data.content;
+        const data = chunk.data
+        currentMessage += data.content
 
         // remove leading whitespace
         currentMessage = currentMessage.replace(/^\s+/, "")
 
-        transcriptUpdate([...history, ["{{char}}", currentMessage]])
+        transcriptUpdate([...history, { system, user: msg, assistant: currentMessage }])
 
         if (data.stop) {
-          console.log("Completion finished: '", currentMessage, "', summary: ", data);
+          console.log("Completion finished: '", currentMessage, "', summary: ", data)
         }
 
         if (data.timings) {
-          llamaStats.value = data.timings;
+          llamaStats.value = data.timings
         }
       }
 
-      controller.value = null;
+      controller.value = null
     }
 
     function MessageInput() {
       const message = useSignal("")
 
       const stop = (e) => {
-        e.preventDefault();
+        e.preventDefault()
         if (controller.value) {
-          controller.value.abort();
-          controller.value = null;
+          controller.value.abort()
+          controller.value = null
         }
       }
 
       const reset = (e) => {
-        stop(e);
-        transcriptUpdate([]);
+        stop(e)
+        transcriptUpdate([])
       }
 
       const submit = (e) => {
-        stop(e);
-        chat(message.value);
-        message.value = "";
+        stop(e)
+        chat(message.value)
+        message.value = ""
       }
 
       const enterSubmits = (event) => {
         if (event.which === 13 && !event.shiftKey) {
-          submit(event);
+          submit(event)
         }
       }
 
@@ -277,7 +311,7 @@
     }
 
     const ChatLog = (props) => {
-      const messages = session.value.transcript;
+      const messages = session.value.transcript
       const container = useRef(null)
 
       useEffect(() => {
@@ -287,102 +321,132 @@
         }
       }, [messages])
 
-      const chatLine = ([user, msg]) => {
-        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
-      };
-
       return html`
         <section id="chat" ref=${container}>
-          ${messages.flatMap(chatLine)}
-        </section>`;
+          ${messages.map(({system, user, assistant}) => html`
+            ${system !== "" && html`<p><em><${Markdownish} text=${system} /></em></p>`}
+            <p class="user bubble"><${Markdownish} text=${user} /></p>
+            ${assistant !== "" ?
+              html`<p class="asst bubble"><${Markdownish} text=${assistant} /></p>` :
+              html`<p class="typing">...</p>`}
+          `)}
+        </section>
+      `
+    }
+
+    const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
+    const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
+    const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
+    const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
+    const updateArray = (el) => {
+        const [name, index] = el.target.name.split(".")
+        const newarr = session.value[name].map((v, i) => i == index ? el.target.value : v).filter(x => x !== "")
+        session.value = { ...session.value, [name]: newarr }
+    }
+    const appendArray = () => session.value = { ...session.value, stop: [...session.value.stop, ""] }
+
+    const ParamSlider = ({param, min, max, step, children}) => {
+      return html`
+        <div>
+          <label for="${param}">${children}</label>
+          <input type="range" id="${param}" min="${min}" max="${max}" step="${step}" name="${param}" value="${params.value[param]}" oninput=${updateParamsFloat} />
+          <span>${params.value[param]}</span>
+        </div>
+      `
+    }
+
+    const FloatField = ({label, max, min, name, step, value}) => {
+      return html`
+        <div>
+          <label for="${name}">${label}</label>
+          <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} />
+          <span>${value}</span>
+        </div>
+      `
+    };
+
+    const IntField = ({label, max, min, name, value}) => {
+      return html`
+        <div>
+          <label for="${name}">${label}</label>
+          <input type="range" id="${name}" min="${min}" max="${max}" name="${name}" value="${value}" oninput=${updateParamsInt} />
+          <span>${value}</span>
+        </div>
+      `
     };
 
     const ConfigForm = (props) => {
-      const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
-      const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
-      const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
-      const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
-
-      const FloatField = ({label, max, min, name, step, value}) => {
-        return html`
-          <div>
-            <label for="${name}">${label}</label>
-            <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} />
-            <span>${value}</span>
-          </div>
-        `
-      };
-
-      const IntField = ({label, max, min, name, value}) => {
-        return html`
-          <div>
-            <label for="${name}">${label}</label>
-            <input type="range" id="${name}" min="${min}" max="${max}" name="${name}" value="${value}" oninput=${updateParamsInt} />
-            <span>${value}</span>
-          </div>
-        `
-      };
-
       return html`
         <form>
           <fieldset>
             <div>
-              <label for="prompt">Prompt</label>
-              <textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/>
-            </div>
-          </fieldset>
-
-          <fieldset class="two">
-            <div>
-              <label for="user">User name</label>
-              <input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
+              <label for="system">System prompt</label>
+              <textarea type="text" name="system" value="${session.value.system}" rows=4 oninput=${updateSession}/>
             </div>
 
             <div>
-              <label for="bot">Bot name</label>
-              <input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
-            </div>
-          </fieldset>
-
-          <fieldset>
-            <div>
-              <label for="template">Prompt template</label>
-              <textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
+              <label for="message">Message template</label>
+              <textarea type="text" name="message" value="${session.value.message}" rows=7 oninput=${updateSession}/>
             </div>
 
             <div>
-              <label for="template">Chat history template</label>
-              <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
+              <label for="stop">Stop strings</label>
+              ${session.value.stop.map((stop, i) => html`
+                <p><input type="text" name="stop.${i}" value="${stop}" oninput=${updateArray}/></p>
+              `)}
+              <input type="button" value="+" onclick=${appendArray} />
             </div>
-          </fieldset>
 
-          <fieldset class="two">
-            ${IntField({label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict})}
-            ${FloatField({label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature})}
-            ${FloatField({label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty})}
-            ${IntField({label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n})}
-            ${IntField({label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k})}
-            ${FloatField({label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p})}
-          </fieldset>
-          <details>
-            <summary>More options</summary>
-            <fieldset class="two">
-              ${FloatField({label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z})}
-              ${FloatField({label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p})}
-              ${FloatField({label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty})}
-              ${FloatField({label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty})}
-            </fieldset>
-            <hr />
-            <fieldset class="three">
+            <${ParamSlider} min=1 max=10 step=0.1 param=cfg_scale>CFG scale<//>
+            ${params.value.cfg_scale > 1 && html`
               <div>
-                <label><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> no Mirostat</label>
-                <label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label>
-                <label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label>
+                <label for="system_cfg">CFG System prompt</label>
+                <textarea type="text" name="system_cfg" value="${session.value.system_cfg}" rows=4 oninput=${updateSession}/>
               </div>
-              ${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})}
-              ${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})}
+            `}
+          </fieldset>
+
+          <fieldset class="two">
+            <${ParamSlider} min=1 max=1000 step=1 param=n_predict>Predict N tokens<//>
+            <${ParamSlider} min=0 max=1000 step=1 param=repeat_last_n>Penalize last N tokens<//>
+            ${params.value.repeat_last_n > 0 && html`
+              <${ParamSlider} min=0 max=4 step=0.01 param=repeat_penalty>Penalize repeat sequence<//>
+              <${ParamSlider} min=0 max=4 step=0.01 param=frequency_penalty>Penalize frequent tokens<//>
+              <${ParamSlider} min=0 max=4 step=0.01 param=presence_penalty>Penalize tokens not present in prompt<//>
+            `}
+            <${ParamSlider} min=0 max=2 step=0.01 param=temperature>Temperature<//>
+          </fieldset>
+
+          ${params.value.temperature > 0 && html`
+            <fieldset class="three">
+              <label>
+                <input type=radio name=mirostat checked=${params.value.mirostat == 0} value=0 oninput=${updateParamsFloat} />
+                Temperature
+              </label>
+
+              <label><input type=radio name=mirostat checked=${params.value.mirostat == 1} value=1 oninput=${updateParamsFloat} />
+                Mirostat v1
+              </label>
+
+              <label>
+                <input type=radio name=mirostat checked=${params.value.mirostat == 2} value=2 oninput=${updateParamsFloat} />
+                Mirostat v2
+              </label>
             </fieldset>
-          </details>
+
+            <fieldset class="two">
+              ${params.value.mirostat == 0 && html`
+                <${ParamSlider} min=1 max=1000 step=1 param=top_k>Top K<//>
+                <${ParamSlider} min=0 max=1 step=0.01 param=tfs_z>Tail free sampling<//>
+                <${ParamSlider} min=0 max=1 step=0.01 param=typical_p>Typical P<//>
+                <${ParamSlider} min=0 max=1 step=0.01 param=top_p>Top P<//>
+              `}
+              ${params.value.mirostat > 0 && html`
+                <${ParamSlider} min=0 max=1 step=0.01 param=mirostat_eta>Mirostat eta, learning rate<//>
+                <${ParamSlider} min=0 max=1000 step=1 param=mirostat_tau>Mirostat tau, target entropy<//>
+              `}
+              </fieldset>
+            `}
         </form>
       `
     }
@@ -399,9 +463,9 @@
         .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 }} />`;
-    };
+        .replace(/\n/gim, '<br />')
+      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`
+    }
 
     const ModelGenerationInfo = (params) => {
       if (!llamaStats.value) {
@@ -435,10 +499,10 @@
             <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>
-      `;
+      `
     }
 
-    render(h(App), document.body);
+    render(h(App), document.body)
   </script>
 </head>
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index c0725088f..c7ef4d272 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -21,9 +21,9 @@
 
 using namespace httplib;
 using json = nlohmann::json;
+using ordered_json = nlohmann::ordered_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;
@@ -32,10 +32,8 @@ 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;
     };
@@ -44,40 +42,30 @@ 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;
                 }
             }
@@ -86,45 +74,51 @@ 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},
+#define SERVER_LOG_PRETTY 0
+
+static void server_log(const char * level, const char * function, int line,
+                       const char * message, const ordered_json & extra) {
+
+    #if SERVER_LOG_PRETTY == 1
+    fprintf(stdout, ANSI_COLOR_MAGENTA ANSI_BOLD "[%s] " ANSI_COLOR_RESET " %s@%d:  %s\n", level, function, line, message);
+    for (auto & it : extra.items()) {
+        fprintf(stdout, "%s=" ANSI_COLOR_YELLOW ANSI_BOLD "%s  " ANSI_COLOR_RESET, it.key().c_str(), it.value().dump().c_str());
+    }
+    fprintf(stdout, "\n\n");
+    #else
+    ordered_json log {
+        { "timestamp", time(nullptr) },
+        { "level", level },
+        { "function", function },
+        { "line", line },
+        { "message", message },
     };
 
-    if (!extra.empty())
-    {
+    if (!extra.empty()) {
         log.merge_patch(extra);
     }
 
     const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
     fprintf(stdout, "%.*s\n", (int)str.size(), str.data());
+    #endif
+
     fflush(stdout);
 }
 
 // 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());
@@ -134,24 +128,21 @@ static std::string tokens_to_output_formatted_string(const llama_context *ctx, c
 }
 
 // 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{
-            {"content", tok_str},
-            {"probs", probs_for_token},
+        out.push_back(json {
+            { "content", tok_str },
+            { "probs", probs_for_token },
         });
     }
     return out;
@@ -160,13 +151,11 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector<com
 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)
@@ -176,26 +165,164 @@ static bool server_verbose = false;
 #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
-{
+// helper class to manage prompt loading and truncation
+struct prompt_evaluator {
+    llama_context * ctx;
+    size_t n_ctx = 0;
+    std::vector<llama_token> embd;
+    std::vector<llama_token> last_n_tokens;
+    size_t num_prompt_tokens = 0;
+    size_t repeat_last_n = 0;
+    size_t n_past = 0;
+    size_t n_keep = 0;
+    bool truncated = false;
+
+    void set_context(llama_context * ctx) {
+        this->ctx = ctx;
+        this->n_ctx = llama_n_ctx(ctx);
+    }
+
+    ~prompt_evaluator() {
+        if (ctx) {
+            llama_free(ctx);
+            ctx = nullptr;
+        }
+    }
+
+    void rewind() {
+        num_prompt_tokens = 0;
+        //num_tokens_predicted = 0;
+        truncated = false;
+        //n_remain = 0;
+        n_past = 0;
+    }
+
+    void load_prompt(std::string &prompt, int keep, size_t n_last) {
+        prompt.insert(0, 1, ' '); // always add a first space
+        std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, prompt, true);
+        num_prompt_tokens = prompt_tokens.size();
+
+        if (keep < 0) {
+            keep = (int)num_prompt_tokens;
+        }
+        n_keep = std::min(n_ctx - 4, (size_t)keep);
+
+        // if input prompt is too big, truncate like normal
+        if (num_prompt_tokens >= n_ctx) {
+            const size_t n_left = (n_ctx - n_keep) / 2;
+            std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + n_keep);
+            const size_t erased_blocks = (num_prompt_tokens - n_keep - n_left - 1) / n_left;
+            new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + n_keep + erased_blocks * n_left, prompt_tokens.end());
+
+            LOG_VERBOSE("input truncated", {
+                { "n_ctx", n_ctx },
+                { "n_keep", n_keep },
+                { "n_left", n_left },
+                { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
+            });
+
+            truncated = true;
+            prompt_tokens = new_tokens;
+        }
+
+        // fill the last n tokens from the input even if context is truncated
+        repeat_last_n = n_last;
+        last_n_tokens.clear();
+        if (n_last > 0) {
+            last_n_tokens.insert(last_n_tokens.begin(),
+                std::max(prompt_tokens.begin(), prompt_tokens.end() - n_last), prompt_tokens.end());
+        }
+
+        // compare the evaluated prompt with the new prompt
+        n_past = common_part(embd, prompt_tokens);
+        embd = 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()) },
+        });
+    }
+
+    bool evaluate(size_t n_threads, size_t n_batch) {
+        if (embd.size() >= n_ctx) {
+            // Reset context
+            const size_t n_left = (n_ctx - n_keep) / 2;
+
+            std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + n_keep);
+            new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
+            embd = new_tokens;
+            n_past = n_keep;
+            truncated = true;
+            LOG_VERBOSE("input truncated", {
+                { "n_ctx", n_ctx },
+                { "n_keep", n_keep },
+                { "n_left", n_left },
+                { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
+            });
+        }
+
+        while (n_past < embd.size()) {
+            size_t n_eval = embd.size() - n_past;
+            if (n_eval > n_batch) {
+                n_eval = n_batch;
+            }
+
+            //LOG_VERBOSE("eval", {
+            //    { "n_eval", n_eval },
+            //    { "n_past", n_past },
+            //    { "n_threads", n_threads },
+            //    { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
+            //});
+
+            if (llama_eval(ctx, &embd[n_past], n_eval, n_past, n_threads)) {
+                LOG_ERROR("failed to eval", {
+                    { "n_eval", n_eval },
+                    { "n_past", n_past },
+                    { "n_threads", n_threads },
+                    { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
+                });
+                return false;
+            }
+            n_past += n_eval;
+        }
+
+        return true;
+    }
+
+    void append_token(llama_token id) {
+        if (repeat_last_n > 0) {
+            if (last_n_tokens.size() >= repeat_last_n) {
+                last_n_tokens.erase(last_n_tokens.begin());
+            }
+            last_n_tokens.push_back(id);
+        }
+        embd.push_back(id);
+    }
+};
+
+struct llama_server_context {
     bool stream = false;
     bool has_next_token = false;
     std::string generated_text;
     std::vector<completion_token_output> generated_token_probs;
 
-    size_t num_prompt_tokens = 0;
     size_t num_tokens_predicted = 0;
-    size_t n_past = 0;
+    int n_keep_guidance = 0;
     size_t n_remain = 0;
+    bool cfg_enabled = false;
 
-    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;
+    prompt_evaluator evaluator;
+    prompt_evaluator evaluator_guidance;
+
     bool stopped_eos = false;
     bool stopped_word = false;
     bool stopped_limit = false;
@@ -204,34 +331,23 @@ 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_free(ctx);
-            ctx = nullptr;
-        }
-        if (model)
-        {
+    ~llama_server_context() {
+        if (model) {
             llama_free_model(model);
             model = nullptr;
         }
     }
 
-    void rewind()
-    {
+    void rewind() {
         params.antiprompt.clear();
-        num_prompt_tokens = 0;
         num_tokens_predicted = 0;
         generated_text = "";
         generated_text.reserve(params.n_ctx);
         generated_token_probs.clear();
-        truncated = false;
         stopped_eos = false;
         stopped_word = false;
         stopped_limit = false;
@@ -239,135 +355,54 @@ struct llama_server_context
         multibyte_pending = 0;
 
         n_remain = 0;
-        n_past = 0;
+        cfg_enabled = false;
+        evaluator.rewind();
+        evaluator_guidance.rewind();
     }
 
-    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;
         }
+        evaluator.set_context(ctx);
+
+        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
+        llama_context * ctx_guidance = llama_new_context_with_model(model, lparams);
+        evaluator_guidance.set_context(ctx_guidance);
 
-        last_n_tokens.resize(params.n_ctx);
-        std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
         return true;
     }
 
-    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)
-        {
-            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)
-        {
-            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;
-            new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
-            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())},
-                                           });
-
-            truncated = true;
-            prompt_tokens = new_tokens;
-        }
-        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);
-        }
-
-        // compare the evaluated prompt with the new prompt
-        n_past = common_part(embd, prompt_tokens);
-        embd = 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())},
-                                       });
-
+    void loadPrompt() {
+        evaluator.load_prompt(params.prompt, params.n_keep, params.repeat_last_n);
         has_next_token = true;
     }
 
-    void beginCompletion()
-    {
+    void loadGuidancePrompt() {
+        evaluator_guidance.load_prompt(params.cfg_negative_prompt, n_keep_guidance, 0);
+    }
+
+    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)
-        {
-            // Reset context
-            const int n_left = (params.n_ctx - params.n_keep) / 2;
+        evaluator.evaluate(params.n_threads, params.n_batch);
 
-            std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + params.n_keep);
-            new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
-            embd = new_tokens;
-            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())},
-                                           });
+        if (cfg_enabled) {
+            evaluator_guidance.evaluate(params.n_threads, params.n_batch);
         }
 
-        while (n_past < embd.size())
-        {
-            int n_eval = (int)embd.size() - n_past;
-            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))
-            {
-                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())},
-                                            });
-                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;
         }
 
@@ -377,7 +412,6 @@ struct llama_server_context
         const float top_p = params.top_p;
         const float tfs_z = params.tfs_z;
         const float typical_p = params.typical_p;
-        const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n;
         const float repeat_penalty = params.repeat_penalty;
         const float alpha_presence = params.presence_penalty;
         const float alpha_frequency = params.frequency_penalty;
@@ -388,64 +422,56 @@ 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 };
+
+            if (cfg_enabled) {
+                llama_sample_classifier_free_guidance(
+                    ctx, &candidates_p, evaluator_guidance.ctx, params.cfg_scale);
+            }
 
             // 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);
+                evaluator.last_n_tokens.data(), evaluator.last_n_tokens.size(),
+                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)
-            {
+                evaluator.last_n_tokens.data(), evaluator.last_n_tokens.size(),
+                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);
@@ -457,23 +483,22 @@ 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());
-            last_n_tokens.push_back(result.tok);
             num_tokens_predicted++;
         }
 
         // add it to the context
-        embd.push_back(result.tok);
+        evaluator.append_token(result.tok);
+        if (cfg_enabled) {
+            evaluator_guidance.append_token(result.tok);
+        }
         // decrement remaining sampling budget
         --n_remain;
 
-        if (!embd.empty() && embd.back() == llama_token_eos())
-        {
-            // stopping_word = llama_token_to_str(ctx, embd.back());
+        if (result.tok == llama_token_eos()) {
+            stopping_word = "";
             has_next_token = false;
             stopped_eos = true;
             LOG_VERBOSE("eos token found", {});
@@ -484,28 +509,22 @@ 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;
@@ -516,83 +535,68 @@ 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) },
+            { "n_past", evaluator.n_past },
+            { "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;
     }
@@ -615,12 +619,10 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     fprintf(stdout, "  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     fprintf(stdout, "  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
     fprintf(stdout, "                        not recommended: doubles context memory required and no measurable increase in quality\n");
-    if (llama_mlock_supported())
-    {
+    if (llama_mlock_supported()) {
         fprintf(stdout, "  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
     }
-    if (llama_mmap_supported())
-    {
+    if (llama_mmap_supported()) {
         fprintf(stdout, "  --no-mmap             do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
     }
 #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
@@ -649,148 +651,102 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     fprintf(stdout, "\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 == "-gqa" || arg == "--gqa")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-gqa" || arg == "--gqa") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_gqa = std::stoi(argv[i]);
-        }
-        else if (arg == "-eps" || arg == "--rms-norm-eps") {
-            if (++i >= argc)
-            {
+        } else if (arg == "-eps" || arg == "--rms-norm-eps") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.rms_norm_eps = std::stof(argv[i]);
-        }
-        else if (arg == "--rope-freq-base")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--rope-freq-base") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.rope_freq_base = std::stof(argv[i]);
-        }
-        else if (arg == "--rope-freq-scale")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--rope-freq-scale") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.rope_freq_scale = std::stof(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;
             }
@@ -799,13 +755,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
 #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}});
+                        {{ "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;
             }
@@ -813,46 +766,35 @@ 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;
                 }
             }
 #else
             LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
 #endif // GGML_USE_CUBLAS
-        }
-        else if (arg == "--low-vram" || arg == "-lv")
-        {
+        } else if (arg == "--low-vram" || arg == "-lv") {
 #ifdef GGML_USE_CUBLAS
             params.low_vram = true;
 #else
             LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {});
 #endif // GGML_USE_CUBLAS
-        }
-        else if (arg == "--mul-mat-q" || arg == "-mmq")
-        {
+        } else if (arg == "--mul-mat-q" || arg == "-mmq") {
 #ifdef GGML_USE_CUBLAS
             params.mul_mat_q = true;
 #else
             LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\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;
             }
@@ -861,173 +803,148 @@ 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);
+        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},
+    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 },
+        { "cfg_scale", llama.params.cfg_scale },
+        { "cfg_n_keep", llama.n_keep_guidance },
     };
 }
 
-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)
-{
+static json format_timings(llama_server_context & llama) {
     const auto timings = llama_get_timings(llama.ctx);
 
-    assert(timings.n_eval == llama.num_tokens_predicted);
+    //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},
+    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},
+        { "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)
-{
+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)},
+    json res = json {
+        { "content", content },
+        { "stop", true },
+        { "model", llama.params.model_alias },
+        { "tokens_predicted", llama.num_tokens_predicted },
+        { "tokens_evaluated", llama.evaluator.num_prompt_tokens },
+        { "generation_settings", format_generation_settings(llama) },
+        { "prompt", llama.params.prompt },
+        { "cfg_negative_prompt", llama.params.cfg_negative_prompt },
+        { "truncated", llama.evaluator.truncated },
+        { "stopped_eos", llama.stopped_eos },
+        { "stopped_word", llama.stopped_word },
+        { "stopped_limit", llama.stopped_limit },
+        { "stopping_word", llama.stopping_word },
+        { "tokens_cached", llama.evaluator.n_past },
+        { "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);
@@ -1048,31 +965,26 @@ static void parse_options_completion(const json &body, llama_server_context &lla
     llama.params.n_keep = body.value("n_keep", default_params.n_keep);
     llama.params.seed = body.value("seed", default_params.seed);
     llama.params.prompt = body.value("prompt", default_params.prompt);
+    llama.params.cfg_negative_prompt = body.value("cfg_negative_prompt", default_params.cfg_negative_prompt);
+    llama.params.cfg_scale = body.value("cfg_scale", default_params.cfg_scale);
+    llama.n_keep_guidance = body.value("cfg_n_keep", 0);
     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;
                     }
                 }
@@ -1081,13 +993,10 @@ static void parse_options_completion(const json &body, llama_server_context &lla
     }
 
     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);
             }
         }
@@ -1096,25 +1005,23 @@ static void parse_options_completion(const json &body, llama_server_context &lla
     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;
@@ -1124,53 +1031,54 @@ 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_backend_init(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"}});
+    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)
-            {
-        res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
-        return false; });
+    svr.Get("/", [](const Request &, Response & res) {
+        res.set_content(reinterpret_cast<const char *>(&index_html), index_html_len, "text/html");
+        return false;
+    });
 
     // this is only called if no index.js is found in the public --path
-    svr.Get("/index.js", [](const Request &, Response &res)
-            {
+    svr.Get("/index.js", [](const Request &, Response & res) {
         res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
-        return false; });
+        return false;
+    });
 
     // this is only called if no index.html is found in the public --path
-    svr.Get("/completion.js", [](const Request &, Response &res)
-            {
-        res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
-        return false; });
+    svr.Get("/completion.js", [](const Request &, Response & res) {
+        res.set_content(reinterpret_cast<const char *>(&completion_js), completion_js_len, "application/javascript");
+        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();
@@ -1182,6 +1090,11 @@ int main(int argc, char **argv)
         llama.loadPrompt();
         llama.beginCompletion();
 
+        if (llama.params.cfg_scale > 1.0f && llama.params.cfg_negative_prompt.size() > 0) {
+            llama.cfg_enabled = true;
+            llama.loadGuidancePrompt();
+        }
+
         if (!llama.stream) {
             size_t stop_pos = std::string::npos;
 
@@ -1275,28 +1188,29 @@ 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)
-            {
+    svr.Get("/model.json", [&llama](const Request &, Response & res) {
         const json data = format_generation_settings(llama);
-        return res.set_content(data.dump(), "application/json"); });
+        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);
@@ -1310,12 +1224,12 @@ 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 {
@@ -1326,19 +1240,20 @@ 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;
     }
@@ -1350,12 +1265,11 @@ 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;
     }