diff --git a/llm/llama3/cpu/_sources/index.md.txt b/llm/llama3/cpu/_sources/index.md.txt index 21356f247..e81862dd6 100644 --- a/llm/llama3/cpu/_sources/index.md.txt +++ b/llm/llama3/cpu/_sources/index.md.txt @@ -68,7 +68,7 @@ python run.py --help # for more detailed usages |---|---| | model id | "--model-name-or-path" or "-m" to specify the , it is model id from Huggingface or downloaded local path | | generation | default: beam search (beam size = 4), "--greedy" for greedy search | -| input tokens | default: 32, provide fixed sizes for input prompt size, use "--input-tokens" for in [1024, 2048, 4096, 8192]; if "--input-tokens" is not used, use "--prompt" to choose other strings as inputs| +| input tokens | provide fixed sizes for input prompt size, use "--input-tokens" for in [1024, 2048, 4096, 8192]; if "--input-tokens" is not used, use "--prompt" to choose other strings as inputs| | output tokens | default: 32, use "--max-new-tokens" to choose any other size | | batch size | default: 1, use "--batch-size" to choose any other size | | token latency | enable "--token-latency" to print out the first or next token latency | @@ -95,7 +95,8 @@ By default, for weight-only quantization, we use quantization with [Automatic Mi - Command: ```bash -OMP_NUM_THREADS= numactl -m -C python run.py --benchmark -m --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --output-dir "saved_results" --greedy --input-tokens +OMP_NUM_THREADS= numactl -m -C python run.py --benchmark -m --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --output-dir "saved_results" --greedy --input-tokens +# Note: you can add "--group-size" to tune good accuracy, suggested range as one of [32, 64, 128, 256, 512]. ``` #### 2.1.1.3 Notes: @@ -134,6 +135,7 @@ For weight-only quantization with deepspeed, we quantize the model then run the - Command: ```bash deepspeed --bind_cores_to_rank run.py --benchmark -m --ipex --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --greedy --input-tokens --autotp --shard-model --output-dir "saved_results" +# Note: you can add "--group-size" to tune good accuracy, suggested range as one of [32, 64, 128, 256, 512]. ``` #### 2.1.2.4 How to Shard Model weight files for Distributed Inference with DeepSpeed diff --git a/llm/llama3/cpu/genindex.html b/llm/llama3/cpu/genindex.html index 86c3bc961..1fb92f757 100644 --- a/llm/llama3/cpu/genindex.html +++ b/llm/llama3/cpu/genindex.html @@ -95,7 +95,7 @@

Index

Built with Sphinx using a theme provided by Read the Docs. - +

© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD.
diff --git a/llm/llama3/cpu/index.html b/llm/llama3/cpu/index.html index 50b5552dd..355561fde 100644 --- a/llm/llama3/cpu/index.html +++ b/llm/llama3/cpu/index.html @@ -185,7 +185,7 @@

2. How To Run Llama 3 with ipex.llm in [1024, 2048, 4096, 8192]; if "--input-tokens" is not used, use "--prompt" to choose other strings as inputs +provide fixed sizes for input prompt size, use "--input-tokens" for in [1024, 2048, 4096, 8192]; if "--input-tokens" is not used, use "--prompt" to choose other strings as inputs output tokens @@ -229,7 +229,8 @@

2.1.1.2 Weight-only quantization (INT8):

  • Command:

    • -
    OMP_NUM_THREADS=<physical cores num> numactl -m <node N> -C <physical cores list>  python run.py  --benchmark -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH> --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --output-dir "saved_results"  --greedy --input-tokens <INPUT_LENGTH> 
+
    OMP_NUM_THREADS=<physical cores num> numactl -m <node N> -C <physical cores list>  python run.py  --benchmark -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH> --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --output-dir "saved_results"  --greedy --input-tokens <INPUT_LENGTH>
+# Note: you can add "--group-size" to tune good accuracy, suggested range as one of [32, 64, 128, 256, 512].
 
    
 
    
 
@@ -268,6 +269,7 @@ 
    2.1.2.3 Weight-only quantization (INT8):
    Command:
    
 
 
    deepspeed --bind_cores_to_rank run.py  --benchmark -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH> --ipex --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --greedy --input-tokens <INPUT_LENGTH>  --autotp --shard-model --output-dir "saved_results"
+# Note: you can add "--group-size" to tune good accuracy, suggested range as one of [32, 64, 128, 256, 512].
 
    
 
    
 
@@ -303,7 +305,7 @@ 
    Miscellaneous TipsSphinx using a
     theme
     provided by Read the Docs.
-    
+    
   
     
     
    © Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD. 
    
 
 
diff --git a/llm/llama3/cpu/search.html b/llm/llama3/cpu/search.html
index b8338d312..3c93f0cc5 100644
--- a/llm/llama3/cpu/search.html
+++ b/llm/llama3/cpu/search.html
@@ -103,7 +103,7 @@
   Built with Sphinx using a
     theme
     provided by Read the Docs.
-    
+    
   
     
     
    © Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD. 
    
 
 
diff --git a/llm/llama3/cpu/searchindex.js b/llm/llama3/cpu/searchindex.js
index e469c5021..350f2e8be 100644
--- a/llm/llama3/cpu/searchindex.js
+++ b/llm/llama3/cpu/searchindex.js
@@ -1 +1 @@
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diff --git a/llm/llama3/xpu/.buildinfo b/llm/llama3/xpu/.buildinfo
new file mode 100644
index 000000000..8330837f7
--- /dev/null
+++ b/llm/llama3/xpu/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 4c9ff12a9aed9d1b8faba0e0c194954d
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/llm/llama3/xpu/.nojekyll b/llm/llama3/xpu/.nojekyll
new file mode 100644
index 000000000..e69de29bb
diff --git a/llm/llama3/xpu/_sources/index.md.txt b/llm/llama3/xpu/_sources/index.md.txt
new file mode 100644
index 000000000..4b3429720
--- /dev/null
+++ b/llm/llama3/xpu/_sources/index.md.txt
@@ -0,0 +1,129 @@
+# Intel® Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3 models
+
+Intel® Extension for PyTorch* provides dedicated optimization for running Llama 3 models on Intel® Core™ Ultra Processors with Intel® Arc™ Graphics, including weight-only quantization (WOQ), Rotary Position Embedding fusion, etc. You are welcomed to have a try with these optimizations on Intel® Core™ Ultra Processors with Intel® Arc™ Graphics.
+
+# 1. Environment Setup
+
+## 1.1 Conda-based environment setup with pre-built wheels on Windows 11 Home
+
+```bash
+# Install Visual Studio 2022
+https://visualstudio.microsoft.com/zh-hans/thank-you-downloading-visual-studio/?sku=Community&channel=Release&version=VS2022&source=VSLandingPage&cid=2030&passive=false
+
+# Install Intel® oneAPI Base Toolkit 2024.1
+https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html?operatingsystem=window
+
+# Install Intel® Core™ Ultra Processors with Intel® Arc™ Graphics driver
+https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html
+
+# Create a conda environment (pre-built wheel only available with python=3.9)
+conda create -n llm python=3.9 -y
+conda activate llm
+conda install libuv
+
+# Set environment variable
+call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
+
+# Install PyTorch*
+pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/torch-2.1.0a0%2Bgit04048c2-cp39-cp39-win_amd64.whl
+
+# Install Intel® Extension for PyTorch*
+pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.1.30%2Bgit03c5535-cp39-cp39-win_amd64.whl
+
+# Install Intel® Extension for Transformers*
+git clone https://github.com/intel/intel-extension-for-transformers.git intel-extension-for-transformers -b xpu_lm_head 
+cd intel-extension-for-transformers 
+pip install -v .
+
+# Install dependencies
+pip install transformers==4.35
+pip install huggingface_hub==0.22
+pip install lm_eval==0.4.2 --no-deps
+pip install accelerate datasets diffusers
+```
+
+
+# 2. How To Run Llama 3
+
+**Intel® Extension for PyTorch\* provides a single script `run_generation_gpu_woq_for_llama.py` to facilitate running generation tasks as below:**
+
+```
+git clone https://github.com/intel/intel-extension-for-pytorch.git
+cd intel-extension-for-pytorch
+git checkout dev/llama-int4
+cd intel-extension-for-pytorch/examples/gpu/inference/python/llm
+```
+
+| Key args of run_generation_gpu_woq_for_llama.py | Notes |
+|---|---|
+| model id | "--model" or "-m" to specify the , it is model id from Huggingface or downloaded local path |
+| benchmark | "--benchmark" to specify whether to generate sentences using model.generate |
+| accuracy | "--accuracy" to specify whether to use the dataset to detect accuracy |
+| output tokens | default: 32, use "--max-new-tokens" to choose any other size |
+| token latency |  enable "--profile_token_latency" to print out the first or next token latency |
+| generation iterations |  use "--iters" and "--num-warmup" to control the repeated iterations of generation, default: 10-iter/3-warmup |
+
+## 2.1 Usage of running Llama 3 models
+
+### 2.1.1 INT4 WOQ Model
+LLM quantization procedure is heavily constrained by client memory and computation capabilities. If you plan to create an INT4 WOQ model by your own, please use a powerful machine, for example, Intel® Xeon® Server, then execute the following steps. Otherwise, it is highly recommended waiting for INT4 model available in HuggingFace Model Hub.
+
+Environment installation:
+```
+cd ${YourWorkSpace}
+git clone https://github.com/intel/neural-compressor.git neural-compressor
+cd neural-compressor
+git checkout xpu_export
+python setup.py develop
+cd ..
+git clone https://github.com/intel/intel-extension-for-transformers.git intel-extension-for-transformers
+cd intel-extension-for-transformers
+git checkout xpu_int4
+python setup.py develop
+cd ..
+git clone https://github.com/intel/auto-round.git auto-round
+cd auto-round
+git checkout lm-head-quant
+python setup.py develop
+cd ..
+pip install schema==0.7.5
+```
+
+Command to quantize:
+
+```
+cd ${YourWorkSpace}/intel-extension-for-transformers/examples/huggingface/pytorch/text-generation/quantization
+
+python run_generation_gpu_woq.py \
+    --model $model_path \
+    --woq --woq_algo AutoRound  \
+    --use_quant_input \
+    --calib_iters 200  \
+    --lr 5e-3 \
+    --minmax_lr 1e-2 \
+    --output_dir llama3_all_int4  \
+    --nsamples 512
+```
+
+The int4 model is saved in folder ~/llama3_all_int4.
+
+### 2.1.2 Measure Llama 3 WOQ INT4 Performance on Windows 11 Home
+
+- Command:
+```bash
+unset LLM_ACC_TEST
+python run_generation_gpu_woq_for_llama.py --model ${PATH/TO/MODEL} --benchmark --profile_token_latency
+*Note:* replace ${PATH/TO/MODEL} with actual Llama 3 INT4 model local path
+```
+
+### 2.1.3 Validate Llama 3 WOQ INT4 Accuracy on Windows 11 Home
+
+- Command:
+```bash
+set LLM_ACC_TEST=1 
+python run_generation_gpu_woq_for_llama.py --model ${PATH/TO/MODEL} --accuracy --task "piqa"
+*Note:* replace ${PATH/TO/MODEL} with actual Llama 3 INT4 model local path
+```
+
+## Miscellaneous Tips
+Intel® Extension for PyTorch* also provides dedicated optimization for many other Large Language Models (LLM), which covers a set of data types for supporting various scenarios. For more details, please check [Large Language Models (LLM) Optimizations Overview](https://intel.github.io/intel-extension-for-pytorch/xpu/latest/tutorials/llm.html).
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new file mode 100644
index 000000000..81415803e
--- /dev/null
+++ b/llm/llama3/xpu/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
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+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
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+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
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+ */
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+    return result;
+};
+
+/*
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+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
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+
+        return {
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+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
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new file mode 100644
index 000000000..f316efcb4
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diff --git a/llm/llama3/xpu/_static/custom.css b/llm/llama3/xpu/_static/custom.css
new file mode 100644
index 000000000..822f95ed4
--- /dev/null
+++ b/llm/llama3/xpu/_static/custom.css
@@ -0,0 +1,29 @@
+/* make the page 1000px */
+.wy-nav-content {
+   max-width: 1200px;
+}
+
+/* code block highlight color in rtd changed to lime green, no no no */
+.rst-content tt.literal, .rst-content code.literal, .highlight {
+   background: #f0f0f0;
+}
+
+.rst-content tt.literal, .rst-content code.literal {
+    color: #000000;
+}
+
+/* Footer privacy links */
+a#wap_dns {
+   display: none;
+}
+
+a#wap_nac {
+   display: none;
+}
+
+/* replace the copyright to eliminate the copyright symbol enforced by
+   the ReadTheDocs theme */
+div[role=contentinfo] {
+   visibility: hidden;
+   position: absolute;
+}
diff --git a/llm/llama3/xpu/_static/doctools.js b/llm/llama3/xpu/_static/doctools.js
new file mode 100644
index 000000000..4d67807d1
--- /dev/null
+++ b/llm/llama3/xpu/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/llm/llama3/xpu/_static/documentation_options.js b/llm/llama3/xpu/_static/documentation_options.js
new file mode 100644
index 000000000..3594dc221
--- /dev/null
+++ b/llm/llama3/xpu/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '2.1.20+xpu',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/llm/llama3/xpu/_static/file.png b/llm/llama3/xpu/_static/file.png
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diff --git a/llm/llama3/xpu/_static/jquery.js b/llm/llama3/xpu/_static/jquery.js
new file mode 100644
index 000000000..c4c6022f2
--- /dev/null
+++ b/llm/llama3/xpu/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3-pre",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b),"object"==typeof module&&module.exports&&(module.exports=t)}("undefined"!=typeof window?window:this,document);
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diff --git a/llm/llama3/xpu/_static/js/theme.js b/llm/llama3/xpu/_static/js/theme.js
new file mode 100644
index 000000000..1fddb6ee4
--- /dev/null
+++ b/llm/llama3/xpu/_static/js/theme.js
@@ -0,0 +1 @@
+!function(n){var e={};function t(i){if(e[i])return e[i].exports;var o=e[i]={i:i,l:!1,exports:{}};return n[i].call(o.exports,o,o.exports,t),o.l=!0,o.exports}t.m=n,t.c=e,t.d=function(n,e,i){t.o(n,e)||Object.defineProperty(n,e,{enumerable:!0,get:i})},t.r=function(n){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(n,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(n,"__esModule",{value:!0})},t.t=function(n,e){if(1&e&&(n=t(n)),8&e)return n;if(4&e&&"object"==typeof n&&n&&n.__esModule)return n;var i=Object.create(null);if(t.r(i),Object.defineProperty(i,"default",{enumerable:!0,value:n}),2&e&&"string"!=typeof n)for(var o in n)t.d(i,o,function(e){return n[e]}.bind(null,o));return i},t.n=function(n){var e=n&&n.__esModule?function(){return n.default}:function(){return n};return t.d(e,"a",e),e},t.o=function(n,e){return Object.prototype.hasOwnProperty.call(n,e)},t.p="",t(t.s=0)}([function(n,e,t){t(1),n.exports=t(3)},function(n,e,t){(function(){var e="undefined"!=typeof window?window.jQuery:t(2);n.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(n){var t=this;void 0===n&&(n=!0),t.isRunning||(t.isRunning=!0,e((function(e){t.init(e),t.reset(),t.win.on("hashchange",t.reset),n&&t.win.on("scroll",(function(){t.linkScroll||t.winScroll||(t.winScroll=!0,requestAnimationFrame((function(){t.onScroll()})))})),t.win.on("resize",(function(){t.winResize||(t.winResize=!0,requestAnimationFrame((function(){t.onResize()})))})),t.onResize()})))},enableSticky:function(){this.enable(!0)},init:function(n){n(document);var e=this;this.navBar=n("div.wy-side-scroll:first"),this.win=n(window),n(document).on("click","[data-toggle='wy-nav-top']",(function(){n("[data-toggle='wy-nav-shift']").toggleClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift")})).on("click",".wy-menu-vertical .current ul li a",(function(){var t=n(this);n("[data-toggle='wy-nav-shift']").removeClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift"),e.toggleCurrent(t),e.hashChange()})).on("click","[data-toggle='rst-current-version']",(function(){n("[data-toggle='rst-versions']").toggleClass("shift-up")})),n("table.docutils:not(.field-list,.footnote,.citation)").wrap("
    "),n("table.docutils.footnote").wrap("
    "),n("table.docutils.citation").wrap("
    "),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n(''),expand.on("click",(function(n){return e.toggleCurrent(t),n.stopPropagation(),!1})),t.prepend(expand)}))},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),t=e.find('[href="'+n+'"]');if(0===t.length){var i=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}if(t.length>0){$(".wy-menu-vertical .current").removeClass("current").attr("aria-expanded","false"),t.addClass("current").attr("aria-expanded","true"),t.closest("li.toctree-l1").parent().addClass("current").attr("aria-expanded","true");for(let n=1;n<=10;n++)t.closest("li.toctree-l"+n).addClass("current").attr("aria-expanded","true");t[0].scrollIntoView()}}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current").attr("aria-expanded","false"),e.siblings().find("li.current").removeClass("current").attr("aria-expanded","false");var t=e.find("> ul li");t.length&&(t.removeClass("current").attr("aria-expanded","false"),e.toggleClass("current").attr("aria-expanded",(function(n,e){return"true"==e?"false":"true"})))}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/llm/llama3/xpu/_static/minus.png b/llm/llama3/xpu/_static/minus.png
new file mode 100644
index 000000000..d96755fda
Binary files /dev/null and b/llm/llama3/xpu/_static/minus.png differ
diff --git a/llm/llama3/xpu/_static/plus.png b/llm/llama3/xpu/_static/plus.png
new file mode 100644
index 000000000..7107cec93
Binary files /dev/null and b/llm/llama3/xpu/_static/plus.png differ
diff --git a/llm/llama3/xpu/_static/pygments.css b/llm/llama3/xpu/_static/pygments.css
new file mode 100644
index 000000000..84ab3030a
--- /dev/null
+++ b/llm/llama3/xpu/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #9C6500 } /* Comment.Preproc */
+.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #E40000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #008400 } /* Generic.Inserted */
+.highlight .go { color: #717171 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #687822 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #767600 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/llm/llama3/xpu/_static/searchtools.js b/llm/llama3/xpu/_static/searchtools.js
new file mode 100644
index 000000000..92da3f8b2
--- /dev/null
+++ b/llm/llama3/xpu/_static/searchtools.js
@@ -0,0 +1,619 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlinks", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/llm/llama3/xpu/_static/sphinx_highlight.js b/llm/llama3/xpu/_static/sphinx_highlight.js
new file mode 100644
index 000000000..8a96c69a1
--- /dev/null
+++ b/llm/llama3/xpu/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '
    ' +
+            '' +
+            _("Hide Search Matches") +
+            "
    "
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/llm/llama3/xpu/_static/twemoji.css b/llm/llama3/xpu/_static/twemoji.css
new file mode 100644
index 000000000..878d070d1
--- /dev/null
+++ b/llm/llama3/xpu/_static/twemoji.css
@@ -0,0 +1,6 @@
+img.emoji {
+    height: 1em;
+    width: 1em;
+    margin: 0 .05em 0 .1em;
+    vertical-align: -0.1em;
+}
diff --git a/llm/llama3/xpu/_static/twemoji.js b/llm/llama3/xpu/_static/twemoji.js
new file mode 100644
index 000000000..91bc868f4
--- /dev/null
+++ b/llm/llama3/xpu/_static/twemoji.js
@@ -0,0 +1,10 @@
+function addEvent(element, eventName, fn) {
+    if (element.addEventListener)
+        element.addEventListener(eventName, fn, false);
+    else if (element.attachEvent)
+        element.attachEvent('on' + eventName, fn);
+}
+
+addEvent(window, 'load', function() {
+    twemoji.parse(document.body, {'folder': 'svg', 'ext': '.svg'});
+});
diff --git a/llm/llama3/xpu/genindex.html b/llm/llama3/xpu/genindex.html
new file mode 100644
index 000000000..7df70cd5c
--- /dev/null
+++ b/llm/llama3/xpu/genindex.html
@@ -0,0 +1,114 @@
+
+
+
+  
+  
+  Index — Intel&#174 Extension for PyTorch* 2.1.20+xpu documentation
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+  Built with Sphinx using a
+    theme
+    provided by Read the Docs.
+    
+
     
     
    © Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD. 
    
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+  Intel® Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3 models — Intel&#174 Extension for PyTorch* 2.1.20+xpu documentation
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    • Intel® Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3 models
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+             View page source
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    Intel® Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3 models
    
+
    Intel® Extension for PyTorch* provides dedicated optimization for running Llama 3 models on Intel® Core™ Ultra Processors with Intel® Arc™ Graphics, including weight-only quantization (WOQ), Rotary Position Embedding fusion, etc. You are welcomed to have a try with these optimizations on Intel® Core™ Ultra Processors with Intel® Arc™ Graphics.
    
+
    
+
    
+
    1. Environment Setup
    
+
    
+
    1.1 Conda-based environment setup with pre-built wheels on Windows 11 Home
    
+
    # Install Visual Studio 2022
+https://visualstudio.microsoft.com/zh-hans/thank-you-downloading-visual-studio/?sku=Community&channel=Release&version=VS2022&source=VSLandingPage&cid=2030&passive=false
+
+# Install Intel® oneAPI Base Toolkit 2024.1
+https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html?operatingsystem=window
+
+# Install Intel® Core™ Ultra Processors with Intel® Arc™ Graphics driver
+https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html
+
+# Create a conda environment (pre-built wheel only available with python=3.9)
+conda create -n llm python=3.9 -y
+conda activate llm
+conda install libuv
+
+# Set environment variable
+call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
+
+# Install PyTorch*
+pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/torch-2.1.0a0%2Bgit04048c2-cp39-cp39-win_amd64.whl
+
+# Install Intel® Extension for PyTorch*
+pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.1.30%2Bgit03c5535-cp39-cp39-win_amd64.whl
+
+# Install Intel® Extension for Transformers*
+git clone https://github.com/intel/intel-extension-for-transformers.git intel-extension-for-transformers -b xpu_lm_head 
+cd intel-extension-for-transformers 
+pip install -v .
+
+# Install dependencies
+pip install transformers==4.35
+pip install huggingface_hub==0.22
+pip install lm_eval==0.4.2 --no-deps
+pip install accelerate datasets diffusers
+
    
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+
    
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+
    2. How To Run Llama 3
    
+
    Intel® Extension for PyTorch* provides a single script run_generation_gpu_woq_for_llama.py to facilitate running generation tasks as below:
    
+
    git clone https://github.com/intel/intel-extension-for-pytorch.git
+cd intel-extension-for-pytorch
+git checkout dev/llama-int4
+cd intel-extension-for-pytorch/examples/gpu/inference/python/llm
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    Key args of run_generation_gpu_woq_for_llama.pyNotes
    model id"--model" or "-m" to specify the , it is model id from Huggingface or downloaded local path
    benchmark"--benchmark" to specify whether to generate sentences using model.generate
    accuracy"--accuracy" to specify whether to use the dataset to detect accuracy
    output tokensdefault: 32, use "--max-new-tokens" to choose any other size
    token latencyenable "--profile_token_latency" to print out the first or next token latency
    generation iterationsuse "--iters" and "--num-warmup" to control the repeated iterations of generation, default: 10-iter/3-warmup
    
+
    2.1 Usage of running Llama 3 models
    
+
    
+
    2.1.1 INT4 WOQ Model
    
+
    LLM quantization procedure is heavily constrained by client memory and computation capabilities. If you plan to create an INT4 WOQ model by your own, please use a powerful machine, for example, Intel® Xeon® Server, then execute the following steps. Otherwise, it is highly recommended waiting for INT4 model available in HuggingFace Model Hub.
    
+
    Environment installation:
    
+
    cd ${YourWorkSpace}
+git clone https://github.com/intel/neural-compressor.git neural-compressor
+cd neural-compressor
+git checkout xpu_export
+python setup.py develop
+cd ..
+git clone https://github.com/intel/intel-extension-for-transformers.git intel-extension-for-transformers
+cd intel-extension-for-transformers
+git checkout xpu_int4
+python setup.py develop
+cd ..
+git clone https://github.com/intel/auto-round.git auto-round
+cd auto-round
+git checkout lm-head-quant
+python setup.py develop
+cd ..
+pip install schema==0.7.5
+
    
+
    
+
    Command to quantize:
    
+
    cd ${YourWorkSpace}/intel-extension-for-transformers/examples/huggingface/pytorch/text-generation/quantization
+
+python run_generation_gpu_woq.py \
+    --model $model_path \
+    --woq --woq_algo AutoRound  \
+    --use_quant_input \
+    --calib_iters 200  \
+    --lr 5e-3 \
+    --minmax_lr 1e-2 \
+    --output_dir llama3_all_int4  \
+    --nsamples 512
+
    
+
    
+
    The int4 model is saved in folder ~/llama3_all_int4.
    
+
    
+
    
+
    2.1.2 Measure Llama 3 WOQ INT4 Performance on Windows 11 Home
    
+
      
+
    • Command:
      • 
+
    
+
    unset LLM_ACC_TEST
+python run_generation_gpu_woq_for_llama.py --model ${PATH/TO/MODEL} --benchmark --profile_token_latency
+*Note:* replace ${PATH/TO/MODEL} with actual Llama 3 INT4 model local path
+
    
+
    
+
    
+
    
+
    2.1.3 Validate Llama 3 WOQ INT4 Accuracy on Windows 11 Home
    
+
      
+
    • Command:
      • 
+
    
+
    set LLM_ACC_TEST=1 
+python run_generation_gpu_woq_for_llama.py --model ${PATH/TO/MODEL} --accuracy --task "piqa"
+*Note:* replace ${PATH/TO/MODEL} with actual Llama 3 INT4 model local path
+
    
+
    
+
    
+
    
+
    
+
    Miscellaneous Tips
    
+
    Intel® Extension for PyTorch* also provides dedicated optimization for many other Large Language Models (LLM), which covers a set of data types for supporting various scenarios. For more details, please check Large Language Models (LLM) Optimizations Overview.
    
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    © Copyright .
    
+  
    
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+  Built with Sphinx using a
+    theme
+    provided by Read the Docs.
+    
+
     
     
    © Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD. 
    
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+# Sphinx inventory version 2
+# Project: Intel® Extension for PyTorch*
+# Version: 2.1.20+xpu
+# The remainder of this file is compressed using zlib.
+xڅ
+0{U ޼;6k]+mK>@d[h`Jym3pcSLy>80:7/rZݣS*ϋ5d{1TIA&lnCC7yC"GC._:J%w^ʇX7r
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+  Search — Intel&#174 Extension for PyTorch* 2.1.20+xpu documentation
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    © Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD. 
    
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+Search.setIndex({"alltitles": {"1. Environment Setup": [[0, "environment-setup"]], "1.1 Conda-based environment setup with pre-built wheels on Windows 11 Home": [[0, "conda-based-environment-setup-with-pre-built-wheels-on-windows-11-home"]], "2. How To Run Llama 3": [[0, "how-to-run-llama-3"]], "2.1 Usage of running Llama 3 models": [[0, "usage-of-running-llama-3-models"]], "2.1.1 INT4 WOQ Model": [[0, "int4-woq-model"]], "2.1.2 Measure Llama 3 WOQ INT4 Performance on Windows 11 Home": [[0, "measure-llama-3-woq-int4-performance-on-windows-11-home"]], "2.1.3 Validate Llama 3 WOQ INT4 Accuracy on Windows 11 Home": [[0, "validate-llama-3-woq-int4-accuracy-on-windows-11-home"]], "Intel\u00ae Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3 models": [[0, "intel-extension-for-pytorch-large-language-model-llm-feature-get-started-for-llama-3-models"]], "Miscellaneous Tips": [[0, "miscellaneous-tips"]]}, "docnames": ["index"], "envversion": {"sphinx": 61, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2}, "filenames": ["index.md"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"0": 0, "0a0": 0, "10": 0, "1e": 0, "200": 0, "2022": 0, "2024": 0, "2030": 0, "22": 0, "2bgit03c5535": 0, "2bgit04048c2": 0, "30": 0, "32": 0, "35": 0, "4": 0, "5": 0, "512": 0, "5e": 0, "7": 0, "785597": 0, "9": 0, "If": 0, "TO": 0, "The": 0, "_": 0, "acceler": 0, "activ": 0, "actual": 0, "all": 0, "also": 0, "amazonaw": 0, "an": 0, "ani": 0, "ar": 0, "arc": 0, "arg": 0, "auto": 0, "autoround": 0, "avail": 0, "b": 0, "bat": 0, "below": 0, "benchmark": 0, "c": 0, "calib_it": 0, "call": 0, "capabl": 0, "cd": 0, "channel": 0, "check": 0, "checkout": 0, "choos": 0, "cid": 0, "client": 0, "clone": 0, "com": 0, "command": 0, "commun": 0, "compressor": 0, "comput": 0, "constrain": 0, "content": 0, "control": 0, "core": 0, "cover": 0, "cp39": 0, "creat": 0, "data": 0, "dataset": 0, "dedic": 0, "default": 0, "dep": 0, "depend": 0, "detail": 0, "detect": 0, "dev": 0, "develop": 0, "diffus": 0, "download": 0, "driver": 0, "embed": 0, "en": 0, "enabl": 0, "etc": 0, "exampl": 0, "execut": 0, "facilit": 0, "fals": 0, "file": 0, "first": 0, "folder": 0, "follow": 0, "from": 0, "fusion": 0, "gener": 0, "git": 0, "github": 0, "gpu": 0, "graphic": 0, "han": 0, "have": 0, "head": 0, "heavili": 0, "highli": 0, "html": 0, "http": 0, "hub": 0, "huggingfac": 0, "huggingface_hub": 0, "i": 0, "id": 0, "includ": 0, "infer": 0, "instal": 0, "intel_extension_for_pytorch": 0, "ipex_dev": 0, "iri": 0, "iter": 0, "kei": 0, "latenc": 0, "libuv": 0, "llama3": 0, "llama3_all_int4": 0, "llm_acc_test": 0, "lm": 0, "lm_eval": 0, "local": 0, "lr": 0, "m": 0, "machin": 0, "mani": 0, "max": 0, "memori": 0, "microsoft": 0, "minmax_lr": 0, "model_path": 0, "more": 0, "n": 0, "neural": 0, "new": 0, "next": 0, "note": 0, "nsampl": 0, "num": 0, "oneapi": 0, "onli": 0, "operatingsystem": 0, "optim": 0, "other": 0, "otherwis": 0, "out": 0, "output": 0, "output_dir": 0, "overview": 0, "own": 0, "passiv": 0, "path": 0, "pip": 0, "piqa": 0, "plan": 0, "pleas": 0, "posit": 0, "power": 0, "print": 0, "procedur": 0, "processor": 0, "profile_token_lat": 0, "program": 0, "provid": 0, "py": 0, "python": 0, "quant": 0, "quantiz": 0, "recommend": 0, "releas": 0, "repeat": 0, "replac": 0, "rotari": 0, "round": 0, "run_generation_gpu_woq": 0, "run_generation_gpu_woq_for_llama": 0, "s3": 0, "save": 0, "scenario": 0, "schema": 0, "script": 0, "sentenc": 0, "server": 0, "set": 0, "setvar": 0, "singl": 0, "size": 0, "sku": 0, "sourc": 0, "specifi": 0, "step": 0, "studio": 0, "support": 0, "task": 0, "text": 0, "thank": 0, "token": 0, "tool": 0, "toolkit": 0, "torch": 0, "transform": 0, "try": 0, "type": 0, "u": 0, "ultra": 0, "unset": 0, "us": 0, "use_quant_input": 0, "v": 0, "variabl": 0, "variou": 0, "version": 0, "visual": 0, "visualstudio": 0, "vs2022": 0, "vslandingpag": 0, "wait": 0, "warmup": 0, "weight": 0, "welcom": 0, "whether": 0, "which": 0, "whl": 0, "win_amd64": 0, "woq_algo": 0, "www": 0, "x86": 0, "xe": 0, "xeon": 0, "xpu": 0, "xpu_export": 0, "xpu_int4": 0, "xpu_lm_head": 0, "y": 0, "you": 0, "your": 0, "yourworkspac": 0, "zh": 0}, "titles": ["Intel\u00ae Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3 models"], "titleterms": {"1": 0, "11": 0, "2": 0, "3": 0, "For": 0, "To": 0, "accuraci": 0, "base": 0, "built": 0, "conda": 0, "environ": 0, "extens": 0, "featur": 0, "get": 0, "home": 0, "how": 0, "int4": 0, "intel": 0, "languag": 0, "larg": 0, "llama": 0, "llm": 0, "measur": 0, "miscellan": 0, "model": 0, "perform": 0, "pre": 0, "pytorch": 0, "run": 0, "setup": 0, "start": 0, "tip": 0, "usag": 0, "valid": 0, "wheel": 0, "window": 0, "woq": 0}})
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