- About this blog
- - -This is the first post in a Quarto blog. Welcome!
-
Since this post doesnβt specify an explicit image, the first image in the post will be used in the listing page of posts.
${missingFields[0]} field.`,
- message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`,
- };
- } else if (missingFields.length > 1) {
- const missingFieldList = missingFields
- .map((field) => {
- return `${field}`;
- })
- .join(", ");
-
- throw {
- name: `Error: Search index is missing the following fields: ${missingFieldList}.`,
- message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`,
- };
- }
- }
-}
-
-let lastQuery = null;
-function showCopyLink(query, options) {
- const language = options.language;
- lastQuery = query;
- // Insert share icon
- const inputSuffixEl = window.document.body.querySelector(
- ".aa-Form .aa-InputWrapperSuffix"
- );
-
- if (inputSuffixEl) {
- let copyButtonEl = window.document.body.querySelector(
- ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton"
- );
-
- if (copyButtonEl === null) {
- copyButtonEl = window.document.createElement("button");
- copyButtonEl.setAttribute("class", "aa-CopyButton");
- copyButtonEl.setAttribute("type", "button");
- copyButtonEl.setAttribute("title", language["search-copy-link-title"]);
- copyButtonEl.onmousedown = (e) => {
- e.preventDefault();
- e.stopPropagation();
- };
-
- const linkIcon = "bi-clipboard";
- const checkIcon = "bi-check2";
-
- const shareIconEl = window.document.createElement("i");
- shareIconEl.setAttribute("class", `bi ${linkIcon}`);
- copyButtonEl.appendChild(shareIconEl);
- inputSuffixEl.prepend(copyButtonEl);
-
- const clipboard = new window.ClipboardJS(".aa-CopyButton", {
- text: function (_trigger) {
- const copyUrl = new URL(window.location);
- copyUrl.searchParams.set(kQueryArg, lastQuery);
- copyUrl.searchParams.set(kResultsArg, "1");
- return copyUrl.toString();
- },
- });
- clipboard.on("success", function (e) {
- // Focus the input
-
- // button target
- const button = e.trigger;
- const icon = button.querySelector("i.bi");
-
- // flash "checked"
- icon.classList.add(checkIcon);
- icon.classList.remove(linkIcon);
- setTimeout(function () {
- icon.classList.remove(checkIcon);
- icon.classList.add(linkIcon);
- }, 1000);
- });
- }
-
- // If there is a query, show the link icon
- if (copyButtonEl) {
- if (lastQuery && options["copy-button"]) {
- copyButtonEl.style.display = "flex";
- } else {
- copyButtonEl.style.display = "none";
- }
- }
- }
-}
-
-/* Search Index Handling */
-// create the index
-var fuseIndex = undefined;
-var shownWarning = false;
-async function readSearchData() {
- // Initialize the search index on demand
- if (fuseIndex === undefined) {
- if (window.location.protocol === "file:" && !shownWarning) {
- window.alert(
- "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server."
- );
- shownWarning = true;
- return;
- }
- // create fuse index
- const options = {
- keys: [
- { name: "title", weight: 20 },
- { name: "section", weight: 20 },
- { name: "text", weight: 10 },
- ],
- ignoreLocation: true,
- threshold: 0.1,
- };
- const fuse = new window.Fuse([], options);
-
- // fetch the main search.json
- const response = await fetch(offsetURL("search.json"));
- if (response.status == 200) {
- return response.json().then(function (searchDocs) {
- searchDocs.forEach(function (searchDoc) {
- fuse.add(searchDoc);
- });
- fuseIndex = fuse;
- return fuseIndex;
- });
- } else {
- return Promise.reject(
- new Error(
- "Unexpected status from search index request: " + response.status
- )
- );
- }
- }
-
- return fuseIndex;
-}
-
-function inputElement() {
- return window.document.body.querySelector(".aa-Form .aa-Input");
-}
-
-function focusSearchInput() {
- setTimeout(() => {
- const inputEl = inputElement();
- if (inputEl) {
- inputEl.focus();
- }
- }, 50);
-}
-
-/* Panels */
-const kItemTypeDoc = "document";
-const kItemTypeMore = "document-more";
-const kItemTypeItem = "document-item";
-const kItemTypeError = "error";
-
-function renderItem(
- item,
- createElement,
- state,
- setActiveItemId,
- setContext,
- refresh,
- quartoSearchOptions
-) {
- switch (item.type) {
- case kItemTypeDoc:
- return createDocumentCard(
- createElement,
- "file-richtext",
- item.title,
- item.section,
- item.text,
- item.href,
- item.crumbs,
- quartoSearchOptions
- );
- case kItemTypeMore:
- return createMoreCard(
- createElement,
- item,
- state,
- setActiveItemId,
- setContext,
- refresh
- );
- case kItemTypeItem:
- return createSectionCard(
- createElement,
- item.section,
- item.text,
- item.href
- );
- case kItemTypeError:
- return createErrorCard(createElement, item.title, item.text);
- default:
- return undefined;
- }
-}
-
-function createDocumentCard(
- createElement,
- icon,
- title,
- section,
- text,
- href,
- crumbs,
- quartoSearchOptions
-) {
- const iconEl = createElement("i", {
- class: `bi bi-${icon} search-result-icon`,
- });
- const titleEl = createElement("p", { class: "search-result-title" }, title);
- const titleContents = [iconEl, titleEl];
- const showParent = quartoSearchOptions["show-item-context"];
- if (crumbs && showParent) {
- let crumbsOut = undefined;
- const crumbClz = ["search-result-crumbs"];
- if (showParent === "root") {
- crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined;
- } else if (showParent === "parent") {
- crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined;
- } else {
- crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined;
- crumbClz.push("search-result-crumbs-wrap");
- }
-
- const crumbEl = createElement(
- "p",
- { class: crumbClz.join(" ") },
- crumbsOut
- );
- titleContents.push(crumbEl);
- }
-
- const titleContainerEl = createElement(
- "div",
- { class: "search-result-title-container" },
- titleContents
- );
-
- const textEls = [];
- if (section) {
- const sectionEl = createElement(
- "p",
- { class: "search-result-section" },
- section
- );
- textEls.push(sectionEl);
- }
- const descEl = createElement("p", {
- class: "search-result-text",
- dangerouslySetInnerHTML: {
- __html: text,
- },
- });
- textEls.push(descEl);
-
- const textContainerEl = createElement(
- "div",
- { class: "search-result-text-container" },
- textEls
- );
-
- const containerEl = createElement(
- "div",
- {
- class: "search-result-container",
- },
- [titleContainerEl, textContainerEl]
- );
-
- const linkEl = createElement(
- "a",
- {
- href: offsetURL(href),
- class: "search-result-link",
- },
- containerEl
- );
-
- const classes = ["search-result-doc", "search-item"];
- if (!section) {
- classes.push("document-selectable");
- }
-
- return createElement(
- "div",
- {
- class: classes.join(" "),
- },
- linkEl
- );
-}
-
-function createMoreCard(
- createElement,
- item,
- state,
- setActiveItemId,
- setContext,
- refresh
-) {
- const moreCardEl = createElement(
- "div",
- {
- class: "search-result-more search-item",
- onClick: (e) => {
- // Handle expanding the sections by adding the expanded
- // section to the list of expanded sections
- toggleExpanded(item, state, setContext, setActiveItemId, refresh);
- e.stopPropagation();
- },
- },
- item.title
- );
-
- return moreCardEl;
-}
-
-function toggleExpanded(item, state, setContext, setActiveItemId, refresh) {
- const expanded = state.context.expanded || [];
- if (expanded.includes(item.target)) {
- setContext({
- expanded: expanded.filter((target) => target !== item.target),
- });
- } else {
- setContext({ expanded: [...expanded, item.target] });
- }
-
- refresh();
- setActiveItemId(item.__autocomplete_id);
-}
-
-function createSectionCard(createElement, section, text, href) {
- const sectionEl = createSection(createElement, section, text, href);
- return createElement(
- "div",
- {
- class: "search-result-doc-section search-item",
- },
- sectionEl
- );
-}
-
-function createSection(createElement, title, text, href) {
- const descEl = createElement("p", {
- class: "search-result-text",
- dangerouslySetInnerHTML: {
- __html: text,
- },
- });
-
- const titleEl = createElement("p", { class: "search-result-section" }, title);
- const linkEl = createElement(
- "a",
- {
- href: offsetURL(href),
- class: "search-result-link",
- },
- [titleEl, descEl]
- );
- return linkEl;
-}
-
-function createErrorCard(createElement, title, text) {
- const descEl = createElement("p", {
- class: "search-error-text",
- dangerouslySetInnerHTML: {
- __html: text,
- },
- });
-
- const titleEl = createElement("p", {
- class: "search-error-title",
- dangerouslySetInnerHTML: {
- __html: ` ${title}`,
- },
- });
- const errorEl = createElement("div", { class: "search-error" }, [
- titleEl,
- descEl,
- ]);
- return errorEl;
-}
-
-function positionPanel(pos) {
- const panelEl = window.document.querySelector(
- "#quarto-search-results .aa-Panel"
- );
- const inputEl = window.document.querySelector(
- "#quarto-search .aa-Autocomplete"
- );
-
- if (panelEl && inputEl) {
- panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`;
- if (pos === "start") {
- panelEl.style.left = `${Math.round(inputEl.left)}px`;
- } else {
- panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`;
- }
- }
-}
-
-/* Highlighting */
-// highlighting functions
-function highlightMatch(query, text) {
- if (text) {
- const start = text.toLowerCase().indexOf(query.toLowerCase());
- if (start !== -1) {
- const startMark = "";
- const endMark = "";
-
- const end = start + query.length;
- text =
- text.slice(0, start) +
- startMark +
- text.slice(start, end) +
- endMark +
- text.slice(end);
- const startInfo = clipStart(text, start);
- const endInfo = clipEnd(
- text,
- startInfo.position + startMark.length + endMark.length
- );
- text =
- startInfo.prefix +
- text.slice(startInfo.position, endInfo.position) +
- endInfo.suffix;
-
- return text;
- } else {
- return text;
- }
- } else {
- return text;
- }
-}
-
-function clipStart(text, pos) {
- const clipStart = pos - 50;
- if (clipStart < 0) {
- // This will just return the start of the string
- return {
- position: 0,
- prefix: "",
- };
- } else {
- // We're clipping before the start of the string, walk backwards to the first space.
- const spacePos = findSpace(text, pos, -1);
- return {
- position: spacePos.position,
- prefix: "",
- };
- }
-}
-
-function clipEnd(text, pos) {
- const clipEnd = pos + 200;
- if (clipEnd > text.length) {
- return {
- position: text.length,
- suffix: "",
- };
- } else {
- const spacePos = findSpace(text, clipEnd, 1);
- return {
- position: spacePos.position,
- suffix: spacePos.clipped ? "β¦" : "",
- };
- }
-}
-
-function findSpace(text, start, step) {
- let stepPos = start;
- while (stepPos > -1 && stepPos < text.length) {
- const char = text[stepPos];
- if (char === " " || char === "," || char === ":") {
- return {
- position: step === 1 ? stepPos : stepPos - step,
- clipped: stepPos > 1 && stepPos < text.length,
- };
- }
- stepPos = stepPos + step;
- }
-
- return {
- position: stepPos - step,
- clipped: false,
- };
-}
-
-// removes highlighting as implemented by the mark tag
-function clearHighlight(searchterm, el) {
- const childNodes = el.childNodes;
- for (let i = childNodes.length - 1; i >= 0; i--) {
- const node = childNodes[i];
- if (node.nodeType === Node.ELEMENT_NODE) {
- if (
- node.tagName === "MARK" &&
- node.innerText.toLowerCase() === searchterm.toLowerCase()
- ) {
- el.replaceChild(document.createTextNode(node.innerText), node);
- } else {
- clearHighlight(searchterm, node);
- }
- }
- }
-}
-
-function escapeRegExp(string) {
- return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-}
-
-// highlight matches
-function highlight(term, el) {
- const termRegex = new RegExp(term, "ig");
- const childNodes = el.childNodes;
-
- // walk back to front avoid mutating elements in front of us
- for (let i = childNodes.length - 1; i >= 0; i--) {
- const node = childNodes[i];
-
- if (node.nodeType === Node.TEXT_NODE) {
- // Search text nodes for text to highlight
- const text = node.nodeValue;
-
- let startIndex = 0;
- let matchIndex = text.search(termRegex);
- if (matchIndex > -1) {
- const markFragment = document.createDocumentFragment();
- while (matchIndex > -1) {
- const prefix = text.slice(startIndex, matchIndex);
- markFragment.appendChild(document.createTextNode(prefix));
-
- const mark = document.createElement("mark");
- mark.appendChild(
- document.createTextNode(
- text.slice(matchIndex, matchIndex + term.length)
- )
- );
- markFragment.appendChild(mark);
-
- startIndex = matchIndex + term.length;
- matchIndex = text.slice(startIndex).search(new RegExp(term, "ig"));
- if (matchIndex > -1) {
- matchIndex = startIndex + matchIndex;
- }
- }
- if (startIndex < text.length) {
- markFragment.appendChild(
- document.createTextNode(text.slice(startIndex, text.length))
- );
- }
-
- el.replaceChild(markFragment, node);
- }
- } else if (node.nodeType === Node.ELEMENT_NODE) {
- // recurse through elements
- highlight(term, node);
- }
- }
-}
-
-/* Link Handling */
-// get the offset from this page for a given site root relative url
-function offsetURL(url) {
- var offset = getMeta("quarto:offset");
- return offset ? offset + url : url;
-}
-
-// read a meta tag value
-function getMeta(metaName) {
- var metas = window.document.getElementsByTagName("meta");
- for (let i = 0; i < metas.length; i++) {
- if (metas[i].getAttribute("name") === metaName) {
- return metas[i].getAttribute("content");
- }
- }
- return "";
-}
-
-function algoliaSearch(query, limit, algoliaOptions) {
- const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"];
-
- const applicationId = algoliaOptions["application-id"];
- const searchOnlyApiKey = algoliaOptions["search-only-api-key"];
- const indexName = algoliaOptions["index-name"];
- const indexFields = algoliaOptions["index-fields"];
- const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey);
- const searchParams = algoliaOptions["params"];
- const searchAnalytics = !!algoliaOptions["analytics-events"];
-
- return getAlgoliaResults({
- searchClient,
- queries: [
- {
- indexName: indexName,
- query,
- params: {
- hitsPerPage: limit,
- clickAnalytics: searchAnalytics,
- ...searchParams,
- },
- },
- ],
- transformResponse: (response) => {
- if (!indexFields) {
- return response.hits.map((hit) => {
- return hit.map((item) => {
- return {
- ...item,
- text: highlightMatch(query, item.text),
- };
- });
- });
- } else {
- const remappedHits = response.hits.map((hit) => {
- return hit.map((item) => {
- const newItem = { ...item };
- ["href", "section", "title", "text", "crumbs"].forEach(
- (keyName) => {
- const mappedName = indexFields[keyName];
- if (
- mappedName &&
- item[mappedName] !== undefined &&
- mappedName !== keyName
- ) {
- newItem[keyName] = item[mappedName];
- delete newItem[mappedName];
- }
- }
- );
- newItem.text = highlightMatch(query, newItem.text);
- return newItem;
- });
- });
- return remappedHits;
- }
- },
- });
-}
-
-function fuseSearch(query, fuse, fuseOptions) {
- return fuse.search(query, fuseOptions).map((result) => {
- const addParam = (url, name, value) => {
- const anchorParts = url.split("#");
- const baseUrl = anchorParts[0];
- const sep = baseUrl.search("\\?") > 0 ? "&" : "?";
- anchorParts[0] = baseUrl + sep + name + "=" + value;
- return anchorParts.join("#");
- };
-
- return {
- title: result.item.title,
- section: result.item.section,
- href: addParam(result.item.href, kQueryArg, query),
- text: highlightMatch(query, result.item.text),
- crumbs: result.item.crumbs,
- };
- });
-}
diff --git a/about.qmd b/about.qmd
deleted file mode 100644
index 692676f7686f66dcd68bcb9faa842517a1464015..0000000000000000000000000000000000000000
--- a/about.qmd
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: "About"
-image: profile.jpg
-about:
- template: jolla
- links:
- - icon: twitter
- text: Twitter
- href: https://twitter.com
- - icon: linkedin
- text: LinkedIn
- href: https://linkedin.com
- - icon: github
- text: Github
- href: https://github.com
-
----
-
-About this blog
diff --git a/index.qmd b/index.qmd
deleted file mode 100644
index 1179fed01c739cce342dbc0028550f37cd16a2cd..0000000000000000000000000000000000000000
--- a/index.qmd
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: "."
-listing:
- contents: posts
- sort: "date desc"
- type: default
- categories: true
- sort-ui: false
- filter-ui: false
-page-layout: full
-title-block-banner: true
----
-
-
diff --git a/posts/_metadata.yml b/posts/_metadata.yml
deleted file mode 100644
index 3e9dd01bc3de145a64472a950a828dd41dd167f0..0000000000000000000000000000000000000000
--- a/posts/_metadata.yml
+++ /dev/null
@@ -1,8 +0,0 @@
-# options specified here will apply to all posts in this folder
-
-# freeze computational output
-# (see https://quarto.org/docs/projects/code-execution.html#freeze)
-freeze: true
-
-# Enable banner style title blocks
-title-block-banner: true
diff --git a/posts/post-with-code/image.jpg b/posts/post-with-code/image.jpg
deleted file mode 100644
index 3ec04c8c4e169aa7cfbd882821eb0d436b62c93a..0000000000000000000000000000000000000000
Binary files a/posts/post-with-code/image.jpg and /dev/null differ
diff --git a/posts/post-with-code/index.qmd b/posts/post-with-code/index.qmd
deleted file mode 100644
index 1c07484271a6378fc1f47cdfd3ad6821450ae15f..0000000000000000000000000000000000000000
--- a/posts/post-with-code/index.qmd
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: "Post With Code"
-author: "Harlow Malloc"
-date: "2024-02-15"
-categories: [news, code, analysis]
-image: "image.jpg"
----
-
-This is a post with executable code.
diff --git a/posts/welcome/index.qmd b/posts/welcome/index.qmd
deleted file mode 100644
index 393f4c2f08a1592ffd5f9c413a510c7e3ff103b7..0000000000000000000000000000000000000000
--- a/posts/welcome/index.qmd
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: "Welcome To My Blog"
-author: "Tristan O'Malley"
-date: "2024-02-12"
-categories: [news]
----
-
-This is the first post in a Quarto blog. Welcome!
-
-
-
-Since this post doesn't specify an explicit `image`, the first image in the post will be used in the listing page of posts.
diff --git a/posts/welcome/thumbnail.jpg b/posts/welcome/thumbnail.jpg
deleted file mode 100644
index 8e3107c9e0a0b43b424bcad3e04f6ece0f8ddb14..0000000000000000000000000000000000000000
Binary files a/posts/welcome/thumbnail.jpg and /dev/null differ
diff --git a/profile.jpg b/profile.jpg
deleted file mode 100644
index 9d50b914ff00f24da73b3f3a51d0080fb0908f4d..0000000000000000000000000000000000000000
Binary files a/profile.jpg and /dev/null differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/.gitignore b/src/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..075b2542afb820ca0c990f02a196dfbb35c41a3a
--- /dev/null
+++ b/src/.gitignore
@@ -0,0 +1 @@
+/.quarto/
diff --git a/src/_quarto.yml b/src/_quarto.yml
new file mode 100644
index 0000000000000000000000000000000000000000..0cbcd69c98065acc65481d72db4a0ca8107b22ff
--- /dev/null
+++ b/src/_quarto.yml
@@ -0,0 +1,26 @@
+project:
+ type: website
+
+website:
+ title: "Open-Source AI Cookbook"
+ navbar:
+ left:
+ - href: index.qmd
+ text: Open-Source AI Cookbook
+ - notebooks/automatic_embedding.ipynb
+ - notebooks/faiss.ipynb
+ - notebooks/single_gpu.ipynb
+ - text: RAG
+ menu:
+ - notebooks/rag_zephyr_langchain.ipynb
+ - notebooks/advanced_rag.ipynb
+ - notebooks/rag_evaluation.ipynb
+
+format:
+ html:
+ theme: cosmo
+ css: styles.css
+ toc: true
+
+
+
diff --git a/src/about.qmd b/src/about.qmd
new file mode 100644
index 0000000000000000000000000000000000000000..07c5e7f9d1316e6eb092b0b312625c29742fcca1
--- /dev/null
+++ b/src/about.qmd
@@ -0,0 +1,5 @@
+---
+title: "About"
+---
+
+About this site
diff --git a/src/index.qmd b/src/index.qmd
new file mode 100644
index 0000000000000000000000000000000000000000..45b8ac2b891e7e9db2c4f2696823406416e39b51
--- /dev/null
+++ b/src/index.qmd
@@ -0,0 +1,31 @@
+---
+title: "Open Source Cookbook"
+---
+
+This is a Quarto implementation of [the Open-Source AI Cookbook](https://github.com/huggingface/cookbook),
+which is a collection of notebooks illustrating practical aspects of building AI
+applications and solving various machine learning tasks using open-source tools and models.
+
+## About Quarto
+[Quarto](https://quarto.org/) is a Markdown-based documentation system which lets you write documents in Markdown or Jupyter Notebooks, and render them to a variety of formats including HTML, PDF, Powerpoint, and more.
+You can also use Quarto to write [books](https://quarto.org/docs/books/), create [dashboards](https://quarto.org/docs/dashboards/), and embed web applications with [Observable](https://quarto.org/docs/interactive/ojs/) and [Shinylive](https://quarto.org/docs/blog/posts/2022-10-25-shinylive-extension/).
+
+
+## Latest notebooks
+
+Check out the recently added notebooks:
+
+- [Automatic Embeddings with TEI through Inference Endpoints](automatic_embedding_tei_inference_endpoints)
+- [Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain](rag_zephyr_langchain)
+- [Embedding multimodal data for similarity search using π€ transformers, π€ datasets and FAISS](faiss_with_hf_datasets_and_clip)
+- [Fine-tuning a Code LLM on Custom Code on a single GPU](fine_tuning_code_llm_on_single_gpu)
+- [RAG Evaluation Using Synthetic data and LLM-As-A-Judge](rag_evaluation)
+- [Advanced RAG on HuggingFace documentation using LangChain](advanced_rag)
+
+You can also check out the notebooks in the cookbook's [GitHub repo](https://github.com/huggingface/cookbook).
+
+## Contributing
+
+The Open-Source AI Cookbook is a community effort, and we welcome contributions from everyone!
+Check out the cookbook's [Contribution guide](https://github.com/huggingface/cookbook/blob/main/README.md) to learn
+how you can add your "recipe".
\ No newline at end of file
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+++ b/src/notebooks/advanced_rag.ipynb
@@ -0,0 +1,1249 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hUCaGdAj9-9F"
+ },
+ "source": [
+ "---\n",
+ "title: \"Advanced RAG\"\n",
+ "---\n",
+ "_Authored by: [Aymeric Roucher](https://huggingface.co/m-ric)_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DKv51c_h9-9H"
+ },
+ "source": [
+ "This notebook demonstrates how you can build an advanced RAG (Retrieval Augmented Generation) for answering a user's question about a specific knowledge base (here, the HuggingFace documentation), using LangChain.\n",
+ "\n",
+ "For an introduction to RAG, you can check [this other cookbook](rag_zephyr_langchain)!\n",
+ "\n",
+ "RAG systems are complex, with many moving parts: here a RAG diagram, where we noted in blue all possibilities for system enhancement:\n",
+ "\n",
+ "![](\"https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/RAG_workflow.png\")
\n",
+ "\n",
+ "> π‘ As you can see, there are many steps to tune in this architecture: tuning the system properly will yield significant performance gains.\n",
+ "\n",
+ "In this notebook, we will take a look into many of these blue notes to see how to tune your RAG system and get the best performance.\n",
+ "\n",
+ "__Let's dig into the model building!__ First, we install the required model dependancies."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NSX0p0rV9-9I"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q torch transformers transformers accelerate bitsandbytes langchain sentence-transformers faiss-gpu openpyxl pacmap"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8_Uyukt39-9J"
+ },
+ "outputs": [],
+ "source": [
+ "%reload_ext dotenv\n",
+ "%dotenv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eoujYMwW9-9J"
+ },
+ "outputs": [],
+ "source": [
+ "from tqdm.notebook import tqdm\n",
+ "import pandas as pd\n",
+ "from typing import Optional, List, Tuple\n",
+ "from datasets import Dataset\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "pd.set_option(\n",
+ " \"display.max_colwidth\", None\n",
+ ") # this will be helpful when visualizing retriever outputs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Kr6rN10U9-9J"
+ },
+ "source": [
+ "### Load your knowledge base"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "qZLVIEVW9-9J"
+ },
+ "outputs": [],
+ "source": [
+ "import datasets\n",
+ "\n",
+ "ds = datasets.load_dataset(\"m-ric/huggingface_doc\", split=\"train\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "836Q7vF49-9K"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.docstore.document import Document as LangchainDocument\n",
+ "\n",
+ "RAW_KNOWLEDGE_BASE = [\n",
+ " LangchainDocument(page_content=doc[\"text\"], metadata={\"source\": doc[\"source\"]})\n",
+ " for doc in tqdm(ds)\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0_LxjD5h9-9K"
+ },
+ "source": [
+ "# 1. Retriever - embeddings ποΈ\n",
+ "The __retriever acts like an internal search engine__: given the user query, it returns a few relevant snippets from your knowledge base.\n",
+ "\n",
+ "These snippets will then be fed to the Reader Model to help it generate its answer.\n",
+ "\n",
+ "So __our objective here is, given a user question, to find the most snippets from our knowledge base to answer that question.__\n",
+ "\n",
+ "This is a wide objective, it leaves open some questions. How many snippets should we retrieve? This parameter will be named `top_k`.\n",
+ "\n",
+ "How long should these snippets be? This is called the `chunk size`. There's no one-size-fits-all answers, but here are a few elements:\n",
+ "- π Your `chunk size` is allowed to vary from one snippet to the other.\n",
+ "- Since there will always be some noise in your retrieval, increasing the `top_k` increases the chance to get relevant elements in your retrieved snippets. π― Shooting more arrows increases your probability to hit your target.\n",
+ "- Meanwhile, the summed length of your retrieved documents should not be too high: for instance, for most current models 16k tokens will probably drown your Reader model in information due to [Lost-in-the-middle phenomenon](https://huggingface.co/papers/2307.03172). π― Give your reader model only the most relevant insights, not a huge pile of books!\n",
+ "\n",
+ "\n",
+ "> In this notebook, we use Langchain library since __it offers a huge variety of options for vector databases and allows us to keep document metadata throughout the processing__."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-uS6Mv8O9-9L"
+ },
+ "source": [
+ "### 1.1 Split the documents into chunks\n",
+ "\n",
+ "- In this part, __we split the documents from our knowledge base into smaller chunks__ which will be the snippets on which the reader LLM will base its answer.\n",
+ "- The goal is to prepare a collection of **semantically relevant snippets**. So their size should be adapted to precise ideas: too small will truncate ideas, too large will dilute them.\n",
+ "\n",
+ "π‘ _Many options exist for text splitting: splitting on words, on sentence boundaries, recursive chunking that processes documents in a tree-like way to preserve structure information... To learn more about chunking, I recommend you read [this great notebook](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb) by Greg Kamradt._\n",
+ "\n",
+ "\n",
+ "- **Recursive chunking** breaks down the text into smaller parts step by step using a given list of separators sorted from the most important to the least important separator. If the first split doesn't give the right size or shape chunks, the method repeats itself on the new chunks using a different separator. For instance with the list of separators `[\"\\n\\n\", \"\\n\", \".\", \"\"]`:\n",
+ " - The method will first break down the document wherever there is a double line break `\"\\n\\n\"`.\n",
+ " - Resulting documents will be split again on simple line breaks `\"\\n\"`, then on sentence ends `\".\"`.\n",
+ " - And finally, if some chunks are still too big, they will be split whenever they overflow the maximum size.\n",
+ "\n",
+ "- With this method, the global structure is well preserved, at the expense of getting slight variations in chunk size.\n",
+ "\n",
+ "> [This space](https://huggingface.co/spaces/A-Roucher/chunk_visualizer) lets you visualize how different splitting options affect the chunks you get.\n",
+ "\n",
+ "π¬ Let's experiment a bit with chunk sizes, beginning with an arbitrary size, and see how splits work. We use Langchain's implementation of recursive chunking with `RecursiveCharacterTextSplitter`.\n",
+ "- Parameter `chunk_size` controls the length of individual chunks: this length is counted by default as the number of characters in the chunk.\n",
+ "- Parameter `chunk_overlap` lets adjacent chunks get a bit of overlap on each other. This reduces the probability that an idea could be cut in half by the split between two adjacent chunks. We ~arbitrarily set this to 1/10th of the chunk size, you could try different values!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "M4m6TwDJ9-9L"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+ "\n",
+ "# We use a hierarchical list of separators specifically tailored for splitting Markdown documents\n",
+ "# This list is taken from LangChain's MarkdownTextSplitter class.\n",
+ "MARKDOWN_SEPARATORS = [\n",
+ " \"\\n#{1,6} \",\n",
+ " \"```\\n\",\n",
+ " \"\\n\\\\*\\\\*\\\\*+\\n\",\n",
+ " \"\\n---+\\n\",\n",
+ " \"\\n___+\\n\",\n",
+ " \"\\n\\n\",\n",
+ " \"\\n\",\n",
+ " \" \",\n",
+ " \"\",\n",
+ "]\n",
+ "\n",
+ "text_splitter = RecursiveCharacterTextSplitter(\n",
+ " chunk_size=1000, # the maximum number of characters in a chunk: we selected this value arbitrarily\n",
+ " chunk_overlap=100, # the number of characters to overlap between chunks\n",
+ " add_start_index=True, # If `True`, includes chunk's start index in metadata\n",
+ " strip_whitespace=True, # If `True`, strips whitespace from the start and end of every document\n",
+ " separators=MARKDOWN_SEPARATORS,\n",
+ ")\n",
+ "\n",
+ "docs_processed = []\n",
+ "for doc in RAW_KNOWLEDGE_BASE:\n",
+ " docs_processed += text_splitter.split_documents([doc])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d5jJUMgb9-9M"
+ },
+ "source": [
+ "We also have to keep in mind that when embedding documents, we will use an embedding model that has accepts a certain maximum sequence length `max_seq_length`.\n",
+ "\n",
+ "So we should make sure that our chunk sizes are below this limit, because any longer chunk will be truncated before processing, thus losing relevancy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "referenced_widgets": [
+ "ae043feeb0914c879e2a9008b413d952"
+ ]
+ },
+ "id": "B4hoki349-9M",
+ "outputId": "64f92a61-7839-476d-f456-7eefde04c20b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model's maximum sequence length: 512\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ae043feeb0914c879e2a9008b413d952",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/31085 [00:00"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sentence_transformers import SentenceTransformer\n",
+ "\n",
+ "# To get the value of the max sequence_length, we will query the underlying `SentenceTransformer` object used in the RecursiveCharacterTextSplitter.\n",
+ "print(\n",
+ " f\"Model's maximum sequence length: {SentenceTransformer('thenlper/gte-small').max_seq_length}\"\n",
+ ")\n",
+ "\n",
+ "from transformers import AutoTokenizer\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(\"thenlper/gte-small\")\n",
+ "lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)]\n",
+ "\n",
+ "# Plot the distrubution of document lengths, counted as the number of tokens\n",
+ "fig = pd.Series(lengths).hist()\n",
+ "plt.title(\"Distribution of document lengths in the knowledge base (in count of tokens)\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L3teXczl9-9M"
+ },
+ "source": [
+ "π As you can see, __the chunk lengths are not aligned with our limit of 512 tokens__, and some documents are above the limit, thus some part of them will be lost in truncation!\n",
+ " - So we should change the `RecursiveCharacterTextSplitter` class to count length in number of tokens instead of number of characters.\n",
+ " - Then we can choose a specific chunk size, here we would choose a lower threshold than 512:\n",
+ " - smaller documents could allow the split to focus more on specific ideas.\n",
+ " - But too small chunks would split sentences in half, thus losing meaning again: the proper tuning is a matter of balance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "referenced_widgets": [
+ "f900cf4ab3a94f45bfa7298f433566ed"
+ ]
+ },
+ "id": "9hvIL2jO9-9M",
+ "outputId": "9baf219d-2954-4927-9681-e28572db90db"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f900cf4ab3a94f45bfa7298f433566ed",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/17995 [00:00"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+ "from transformers import AutoTokenizer\n",
+ "\n",
+ "EMBEDDING_MODEL_NAME = \"thenlper/gte-small\"\n",
+ "\n",
+ "\n",
+ "def split_documents(\n",
+ " chunk_size: int,\n",
+ " knowledge_base: List[LangchainDocument],\n",
+ " tokenizer_name: Optional[str] = EMBEDDING_MODEL_NAME,\n",
+ ") -> List[LangchainDocument]:\n",
+ " \"\"\"\n",
+ " Split documents into chunks of maximum size `chunk_size` tokens and return a list of documents.\n",
+ " \"\"\"\n",
+ " text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(\n",
+ " AutoTokenizer.from_pretrained(tokenizer_name),\n",
+ " chunk_size=chunk_size,\n",
+ " chunk_overlap=int(chunk_size / 10),\n",
+ " add_start_index=True,\n",
+ " strip_whitespace=True,\n",
+ " separators=MARKDOWN_SEPARATORS,\n",
+ " )\n",
+ "\n",
+ " docs_processed = []\n",
+ " for doc in knowledge_base:\n",
+ " docs_processed += text_splitter.split_documents([doc])\n",
+ "\n",
+ " # Remove duplicates\n",
+ " unique_texts = {}\n",
+ " docs_processed_unique = []\n",
+ " for doc in docs_processed:\n",
+ " if doc.page_content not in unique_texts:\n",
+ " unique_texts[doc.page_content] = True\n",
+ " docs_processed_unique.append(doc)\n",
+ "\n",
+ " return docs_processed_unique\n",
+ "\n",
+ "\n",
+ "docs_processed = split_documents(\n",
+ " 512, # We choose a chunk size adapted to our model\n",
+ " RAW_KNOWLEDGE_BASE,\n",
+ " tokenizer_name=EMBEDDING_MODEL_NAME,\n",
+ ")\n",
+ "\n",
+ "# Let's visualize the chunk sizes we would have in tokens from a common model\n",
+ "from transformers import AutoTokenizer\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME)\n",
+ "lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)]\n",
+ "fig = pd.Series(lengths).hist()\n",
+ "plt.title(\"Distribution of document lengths in the knowledge base (in count of tokens)\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wc3riwX39-9M"
+ },
+ "source": [
+ "β‘οΈ Now the chunk length distribution looks better!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J1ho-UKM9-9M"
+ },
+ "source": [
+ "### 1.2 Building the vector database\n",
+ "\n",
+ "We want to compute the embeddings for all the chunks of our knowledge base: to learn more on sentence embeddings, we recommend reading [this guide](https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/).\n",
+ "\n",
+ "#### How does retrieval work ?\n",
+ "\n",
+ "Once the chunks are all embedded, we store them into a vector database. When the user types in a query, it gets embedded by the same model previously used, and a similarity search returns the closest documents from the vector database.\n",
+ "\n",
+ "The technical challenge is thus, given a query vector, to quickly find the nearest neighbours of this vector in the vector database. To do this, we need to choose two things: a distance, and a search algorithm to find the nearest neighbors quickly within a database of thousands of records.\n",
+ "\n",
+ "##### Nearest Neighbor search algorithm\n",
+ "\n",
+ "There are plentiful choices for the nearest neighbor search algorithm: we go with Facebook's [FAISS](https://github.com/facebookresearch/faiss), since FAISS is performant enough for most use cases, and it is well known thus widely implemented.\n",
+ "\n",
+ "##### Distances\n",
+ "\n",
+ "Regarding distances, you can find a good guide [here](https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/#distance-between-embeddings). In short:\n",
+ "\n",
+ "- **Cosine similarity** computes similarity between two vectors as the cosinus of their relative angle: it allows us to compare vector directions are regardless of their magnitude. Using it requires to normalize all vectors, to rescale them into unit norm.\n",
+ "- **Dot product** takes into account magnitude, with the sometimes undesirable effect that increasing a vector's length will make it more similar to all others.\n",
+ "- **Euclidean distance** is the distance between the ends of vectors.\n",
+ "\n",
+ "You can try [this small exercise](https://developers.google.com/machine-learning/clustering/similarity/check-your-understanding) to check your understanding of these concepts. But once vectors are normalized, [the choice of a specific distance does not matter much](https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use).\n",
+ "\n",
+ "Our particular model works well with cosine similarity, so choose this distance, and we set it up both in the Embedding model, and in the `distance_strategy` argument of our FAISS index. With cosine similarity, we have to normalize our embeddings.\n",
+ "\n",
+ "π¨π The cell below takes a few minutes to run on A10G!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dalledM99-9M"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.vectorstores import FAISS\n",
+ "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
+ "from langchain_community.vectorstores.utils import DistanceStrategy\n",
+ "\n",
+ "embedding_model = HuggingFaceEmbeddings(\n",
+ " model_name=EMBEDDING_MODEL_NAME,\n",
+ " multi_process=True,\n",
+ " model_kwargs={\"device\": \"cuda\"},\n",
+ " encode_kwargs={\"normalize_embeddings\": True}, # set True for cosine similarity\n",
+ ")\n",
+ "\n",
+ "KNOWLEDGE_VECTOR_DATABASE = FAISS.from_documents(\n",
+ " docs_processed, embedding_model, distance_strategy=DistanceStrategy.COSINE\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0zM-wfiJ9-9N"
+ },
+ "source": [
+ "π To visualize the search for the closest documents, let's project our embeddings from 384 dimensions down to 2 dimensions using PaCMAP.\n",
+ "\n",
+ "π‘ _We chose PaCMAP rather than other techniques such as t-SNE or UMAP, since [it is efficient (preserves local and global structure), robust to initialization parameters and fast](https://www.nature.com/articles/s42003-022-03628-x#Abs1)._"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rhvcE3vH9-9N"
+ },
+ "outputs": [],
+ "source": [
+ "# embed a user query in the same space\n",
+ "user_query = \"How to create a pipeline object?\"\n",
+ "query_vector = embedding_model.embed_query(user_query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "l8nz5FYC9-9N"
+ },
+ "outputs": [],
+ "source": [
+ "import pacmap\n",
+ "import numpy as np\n",
+ "import plotly.express as px\n",
+ "\n",
+ "embedding_projector = pacmap.PaCMAP(\n",
+ " n_components=2, n_neighbors=None, MN_ratio=0.5, FP_ratio=2.0, random_state=1\n",
+ ")\n",
+ "\n",
+ "embeddings_2d = [\n",
+ " list(KNOWLEDGE_VECTOR_DATABASE.index.reconstruct_n(idx, 1)[0])\n",
+ " for idx in range(len(docs_processed))\n",
+ "] + [query_vector]\n",
+ "\n",
+ "# fit the data (The index of transformed data corresponds to the index of the original data)\n",
+ "documents_projected = embedding_projector.fit_transform(np.array(embeddings_2d), init=\"pca\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7Cl9Fw2A9-9N"
+ },
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame.from_dict(\n",
+ " [\n",
+ " {\n",
+ " \"x\": documents_projected[i, 0],\n",
+ " \"y\": documents_projected[i, 1],\n",
+ " \"source\": docs_processed[i].metadata[\"source\"].split(\"/\")[1],\n",
+ " \"extract\": docs_processed[i].page_content[:100] + \"...\",\n",
+ " \"symbol\": \"circle\",\n",
+ " \"size_col\": 4,\n",
+ " }\n",
+ " for i in range(len(docs_processed))\n",
+ " ]\n",
+ " + [\n",
+ " {\n",
+ " \"x\": documents_projected[-1, 0],\n",
+ " \"y\": documents_projected[-1, 1],\n",
+ " \"source\": \"User query\",\n",
+ " \"extract\": user_query,\n",
+ " \"size_col\": 100,\n",
+ " \"symbol\": \"star\",\n",
+ " }\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "# visualize the embedding\n",
+ "fig = px.scatter(\n",
+ " df,\n",
+ " x=\"x\",\n",
+ " y=\"y\",\n",
+ " color=\"source\",\n",
+ " hover_data=\"extract\",\n",
+ " size=\"size_col\",\n",
+ " symbol=\"symbol\",\n",
+ " color_discrete_map={\"User query\": \"black\"},\n",
+ " width=1000,\n",
+ " height=700,\n",
+ ")\n",
+ "fig.update_traces(\n",
+ " marker=dict(opacity=1, line=dict(width=0, color=\"DarkSlateGrey\")), selector=dict(mode=\"markers\")\n",
+ ")\n",
+ "fig.update_layout(\n",
+ " legend_title_text=\"Chunk source\",\n",
+ " title=\"2D Projection of Chunk Embeddings via PaCMAP\",\n",
+ ")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kWesCSGt9-9N"
+ },
+ "source": [
+ "![](\"https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/PaCMAP_embeddings.png\")
\n",
+ "\n",
+ "\n",
+ "β‘οΈ On the graph above, you can see a spatial representation of the kowledge base documents. As the vector embeddings represent the document's meaning, their closeness in meaning should be reflected in their embedding's closeness.\n",
+ "\n",
+ "The user query's embedding is also shown : we want to find the `k` document that have the closest meaning, thus we pick the `k` closest vectors.\n",
+ "\n",
+ "In the LangChain vector database implementation, this search operation is performed by the method `vector_database.similarity_search(query)`.\n",
+ "\n",
+ "Here is the result:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VcjQzejH9-9N",
+ "outputId": "d5b817c2-1b0e-4e47-9658-4892a91e7c51"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Starting retrieval for user_query='How to create a pipeline object?'...\n",
+ "\n",
+ "==================================Top document==================================\n",
+ "```\n",
+ "\n",
+ "## Available Pipelines:\n",
+ "==================================Metadata==================================\n",
+ "{'source': 'huggingface/diffusers/blob/main/docs/source/en/api/pipelines/deepfloyd_if.md', 'start_index': 16887}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"\\nStarting retrieval for {user_query=}...\")\n",
+ "retrieved_docs = KNOWLEDGE_VECTOR_DATABASE.similarity_search(query=user_query, k=5)\n",
+ "print(\"\\n==================================Top document==================================\")\n",
+ "print(retrieved_docs[0].page_content)\n",
+ "print(\"==================================Metadata==================================\")\n",
+ "print(retrieved_docs[0].metadata)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VjVqmDGh9-9N"
+ },
+ "source": [
+ "# 2. Reader - LLM π¬\n",
+ "\n",
+ "In this part, the __LLM Reader reads the retrieved context to formulate its answer.__\n",
+ "\n",
+ "There are actually substeps that can all be tuned:\n",
+ "1. The content of the retrieved documents is aggregated together into the \"context\", with many processing options like _prompt compression_.\n",
+ "2. The context and the user query are aggregated into a prompt then given to the LLM to generate its answer."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0xiXcG269-9N"
+ },
+ "source": [
+ "### 2.1. Reader model\n",
+ "\n",
+ "The choice of a reader model is important on a few aspects:\n",
+ "- the reader model's `max_seq_length` must accomodate our prompt, which includes the context output by the retriever call: the context consists in 5 documents of 512 tokens each, so we aim for a context length of 4k tokens at least.\n",
+ "- the reader model\n",
+ "\n",
+ "For this example, we chose [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a small but powerful model.\n",
+ "\n",
+ "With many models being released every week, you may want to substitute this model to the latest and greatest. The best way to keep track of open source LLMs is to check the [Open-source LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n",
+ "\n",
+ "To make inference faster, we will load the quantized version of the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "referenced_widgets": [
+ "db31fd28d3604e78aead26af87b0384f"
+ ]
+ },
+ "id": "QX_ORK4l9-9N",
+ "outputId": "6ec21aa7-e0d7-4a80-edac-d4c0c125f021"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "db31fd28d3604e78aead26af87b0384f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/8 [00:00\n",
+ "Using the information contained in the context, \n",
+ "give a comprehensive answer to the question.\n",
+ "Respond only to the question asked, response should be concise and relevant to the question.\n",
+ "Provide the number of the source document when relevant.\n",
+ "If the answer cannot be deduced from the context, do not give an answer.\n",
+ "<|user|>\n",
+ "Context:\n",
+ "{context}\n",
+ "---\n",
+ "Now here is the question you need to answer.\n",
+ "\n",
+ "Question: {question}\n",
+ "<|assistant|>\n"
+ ]
+ }
+ ],
+ "source": [
+ "prompt_in_chat_format = [\n",
+ " {\n",
+ " \"role\": \"system\",\n",
+ " \"content\": \"\"\"Using the information contained in the context,\n",
+ "give a comprehensive answer to the question.\n",
+ "Respond only to the question asked, response should be concise and relevant to the question.\n",
+ "Provide the number of the source document when relevant.\n",
+ "If the answer cannot be deduced from the context, do not give an answer.\"\"\",\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"\"\"Context:\n",
+ "{context}\n",
+ "---\n",
+ "Now here is the question you need to answer.\n",
+ "\n",
+ "Question: {question}\"\"\",\n",
+ " },\n",
+ "]\n",
+ "RAG_PROMPT_TEMPLATE = tokenizer.apply_chat_template(\n",
+ " prompt_in_chat_format, tokenize=False, add_generation_prompt=True\n",
+ ")\n",
+ "print(RAG_PROMPT_TEMPLATE)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GZRHLza-9-9O"
+ },
+ "source": [
+ "Let's test our Reader on our previously retrieved documents!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "G4XprIih9-9O",
+ "outputId": "94c63d34-67ad-4f82-a3b4-2a32cecc8427"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "To create a pipeline object, follow these steps:\n",
+ "\n",
+ "1. Define the inputs and outputs of your pipeline. These could be strings, dictionaries, or any other format that best suits your use case.\n",
+ "\n",
+ "2. Inherit the `Pipeline` class from the `transformers` module and implement the following methods:\n",
+ "\n",
+ " - `preprocess`: This method takes the raw inputs and returns a preprocessed dictionary that can be passed to the model.\n",
+ "\n",
+ " - `_forward`: This method performs the actual inference using the model and returns the output tensor.\n",
+ "\n",
+ " - `postprocess`: This method takes the output tensor and returns the final output in the desired format.\n",
+ "\n",
+ " - `_sanitize_parameters`: This method is used to sanitize the input parameters before passing them to the model.\n",
+ "\n",
+ "3. Load the necessary components, such as the model and scheduler, into the pipeline object.\n",
+ "\n",
+ "4. Instantiate the pipeline object and return it.\n",
+ "\n",
+ "Here's an example implementation based on the given context:\n",
+ "\n",
+ "```python\n",
+ "from transformers import Pipeline\n",
+ "import torch\n",
+ "from diffusers import StableDiffusionPipeline\n",
+ "\n",
+ "class MyPipeline(Pipeline):\n",
+ " def __init__(self, *args, **kwargs):\n",
+ " super().__init__(*args, **kwargs)\n",
+ " self.pipe = StableDiffusionPipeline.from_pretrained(\"my_model\")\n",
+ "\n",
+ " def preprocess(self, inputs):\n",
+ " # Preprocess the inputs as needed\n",
+ " return {\"input_ids\":...}\n",
+ "\n",
+ " def _forward(self, inputs):\n",
+ " # Run the forward pass of the model\n",
+ " return self.pipe(**inputs).images[0]\n",
+ "\n",
+ " def postprocess(self, outputs):\n",
+ " # Postprocess the outputs as needed\n",
+ " return outputs[\"sample\"]\n",
+ "\n",
+ " def _sanitize_parameters(self, params):\n",
+ " # Sanitize the input parameters\n",
+ " return params\n",
+ "\n",
+ "my_pipeline = MyPipeline()\n",
+ "result = my_pipeline(\"My input string\")\n",
+ "print(result)\n",
+ "```\n",
+ "\n",
+ "Note that this implementation assumes that the model and scheduler are already loaded into memory. If they need to be loaded dynamically, you can modify the `__init__` method accordingly.\n"
+ ]
+ }
+ ],
+ "source": [
+ "retrieved_docs_text = [\n",
+ " doc.page_content for doc in retrieved_docs\n",
+ "] # we only need the text of the documents\n",
+ "context = \"\\nExtracted documents:\\n\"\n",
+ "context += \"\".join([f\"Document {str(i)}:::\\n\" + doc for i, doc in enumerate(retrieved_docs_text)])\n",
+ "\n",
+ "final_prompt = RAG_PROMPT_TEMPLATE.format(\n",
+ " question=\"How to create a pipeline object?\", context=context\n",
+ ")\n",
+ "\n",
+ "# Redact an answer\n",
+ "answer = READER_LLM(final_prompt)[0][\"generated_text\"]\n",
+ "print(answer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rhRHZoww9-9O"
+ },
+ "source": [
+ "### 2.3. Reranking\n",
+ "\n",
+ "A good option for RAG is to retrieve more documents than you want in the end, then rerank the results with a more powerful retrieval model before keeping only the `top_k`.\n",
+ "\n",
+ "For this, [Colbertv2](https://arxiv.org/abs/2112.01488) is a great choice: instead of a bi-encoder like our classical embedding models, it is a cross-encoder that computes more fine-grained interactions between the query tokens and each document's tokens.\n",
+ "\n",
+ "It is easily usable thanks to [the RAGatouille library](https://github.com/bclavie/RAGatouille)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "triOdqTV9-9O"
+ },
+ "outputs": [],
+ "source": [
+ "from ragatouille import RAGPretrainedModel\n",
+ "\n",
+ "RERANKER = RAGPretrainedModel.from_pretrained(\"colbert-ir/colbertv2.0\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Minj2SV59-9O"
+ },
+ "source": [
+ "# 3. Assembling it all!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "n11zYRfn9-9O"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import Pipeline\n",
+ "\n",
+ "\n",
+ "def answer_with_rag(\n",
+ " question: str,\n",
+ " llm: Pipeline,\n",
+ " knowledge_index: FAISS,\n",
+ " reranker: Optional[RAGPretrainedModel] = None,\n",
+ " num_retrieved_docs: int = 30,\n",
+ " num_docs_final: int = 5,\n",
+ ") -> Tuple[str, List[LangchainDocument]]:\n",
+ " # Gather documents with retriever\n",
+ " print(\"=> Retrieving documents...\")\n",
+ " relevant_docs = knowledge_index.similarity_search(query=question, k=num_retrieved_docs)\n",
+ " relevant_docs = [doc.page_content for doc in relevant_docs] # keep only the text\n",
+ "\n",
+ " # Optionally rerank results\n",
+ " if reranker:\n",
+ " print(\"=> Reranking documents...\")\n",
+ " relevant_docs = reranker.rerank(question, relevant_docs, k=num_docs_final)\n",
+ " relevant_docs = [doc[\"content\"] for doc in relevant_docs]\n",
+ "\n",
+ " relevant_docs = relevant_docs[:num_docs_final]\n",
+ "\n",
+ " # Build the final prompt\n",
+ " context = \"\\nExtracted documents:\\n\"\n",
+ " context += \"\".join([f\"Document {str(i)}:::\\n\" + doc for i, doc in enumerate(relevant_docs)])\n",
+ "\n",
+ " final_prompt = RAG_PROMPT_TEMPLATE.format(question=question, context=context)\n",
+ "\n",
+ " # Redact an answer\n",
+ " print(\"=> Generating answer...\")\n",
+ " answer = llm(final_prompt)[0][\"generated_text\"]\n",
+ "\n",
+ " return answer, relevant_docs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9nA4nwRQ9-9P"
+ },
+ "source": [
+ "Let's see how our RAG pipeline answers a user query."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7ZTC1FtX9-9P",
+ "outputId": "22597be1-ab72-4f68-d577-0e12820463cf"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=> Retrieving documents...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=> Reranking documents...\n",
+ "=> Generating answer...\n"
+ ]
+ }
+ ],
+ "source": [
+ "question = \"how to create a pipeline object?\"\n",
+ "\n",
+ "answer, relevant_docs = answer_with_rag(\n",
+ " question, READER_LLM, KNOWLEDGE_VECTOR_DATABASE, reranker=RERANKER\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "SwW0oqhZ9-9P",
+ "outputId": "361f28ed-9cd5-40b8-f8c4-57e8e4a530d9"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "==================================Answer==================================\n",
+ "To create a pipeline object, follow these steps:\n",
+ "\n",
+ "1. Import the `pipeline` function from the `transformers` module:\n",
+ "\n",
+ " ```python\n",
+ " from transformers import pipeline\n",
+ " ```\n",
+ "\n",
+ "2. Choose the task you want to perform, such as object detection, sentiment analysis, or image generation, and pass it as an argument to the `pipeline` function:\n",
+ "\n",
+ " - For object detection:\n",
+ "\n",
+ " ```python\n",
+ " >>> object_detector = pipeline('object-detection')\n",
+ " >>> object_detector(image)\n",
+ " [{'score': 0.9982201457023621,\n",
+ " 'label':'remote',\n",
+ " 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},\n",
+ " ...]\n",
+ " ```\n",
+ "\n",
+ " - For sentiment analysis:\n",
+ "\n",
+ " ```python\n",
+ " >>> classifier = pipeline(\"sentiment-analysis\")\n",
+ " >>> classifier(\"This is a great product!\")\n",
+ " {'labels': ['POSITIVE'],'scores': tensor([0.9999], device='cpu', dtype=torch.float32)}\n",
+ " ```\n",
+ "\n",
+ " - For image generation:\n",
+ "\n",
+ " ```python\n",
+ " >>> image = pipeline(\n",
+ " ... \"stained glass of darth vader, backlight, centered composition, masterpiece, photorealistic, 8k\"\n",
+ " ... ).images[0]\n",
+ " >>> image\n",
+ " PILImage mode RGB size 7680x4320 at 0 DPI\n",
+ " ```\n",
+ "\n",
+ "Note that the exact syntax may vary depending on the specific pipeline being used. Refer to the documentation for more details on how to use each pipeline.\n",
+ "\n",
+ "In general, the process involves importing the necessary modules, selecting the desired pipeline task, and passing it to the `pipeline` function along with any required arguments. The resulting pipeline object can then be used to perform the selected task on input data.\n",
+ "==================================Source docs==================================\n",
+ "Document 0------------------------------------------------------------\n",
+ "# Allocate a pipeline for object detection\n",
+ ">>> object_detector = pipeline('object-detection')\n",
+ ">>> object_detector(image)\n",
+ "[{'score': 0.9982201457023621,\n",
+ " 'label': 'remote',\n",
+ " 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},\n",
+ " {'score': 0.9960021376609802,\n",
+ " 'label': 'remote',\n",
+ " 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},\n",
+ " {'score': 0.9954745173454285,\n",
+ " 'label': 'couch',\n",
+ " 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},\n",
+ " {'score': 0.9988006353378296,\n",
+ " 'label': 'cat',\n",
+ " 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},\n",
+ " {'score': 0.9986783862113953,\n",
+ " 'label': 'cat',\n",
+ " 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]\n",
+ "Document 1------------------------------------------------------------\n",
+ "# Allocate a pipeline for object detection\n",
+ ">>> object_detector = pipeline('object_detection')\n",
+ ">>> object_detector(image)\n",
+ "[{'score': 0.9982201457023621,\n",
+ " 'label': 'remote',\n",
+ " 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},\n",
+ " {'score': 0.9960021376609802,\n",
+ " 'label': 'remote',\n",
+ " 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},\n",
+ " {'score': 0.9954745173454285,\n",
+ " 'label': 'couch',\n",
+ " 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},\n",
+ " {'score': 0.9988006353378296,\n",
+ " 'label': 'cat',\n",
+ " 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},\n",
+ " {'score': 0.9986783862113953,\n",
+ " 'label': 'cat',\n",
+ " 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]\n",
+ "Document 2------------------------------------------------------------\n",
+ "Start by creating an instance of [`pipeline`] and specifying a task you want to use it for. In this guide, you'll use the [`pipeline`] for sentiment analysis as an example:\n",
+ "\n",
+ "```py\n",
+ ">>> from transformers import pipeline\n",
+ "\n",
+ ">>> classifier = pipeline(\"sentiment-analysis\")\n",
+ "Document 3------------------------------------------------------------\n",
+ "```\n",
+ "\n",
+ "## Add the pipeline to π€ Transformers\n",
+ "\n",
+ "If you want to contribute your pipeline to π€ Transformers, you will need to add a new module in the `pipelines` submodule\n",
+ "with the code of your pipeline, then add it to the list of tasks defined in `pipelines/__init__.py`.\n",
+ "\n",
+ "Then you will need to add tests. Create a new file `tests/test_pipelines_MY_PIPELINE.py` with examples of the other tests.\n",
+ "\n",
+ "The `run_pipeline_test` function will be very generic and run on small random models on every possible\n",
+ "architecture as defined by `model_mapping` and `tf_model_mapping`.\n",
+ "\n",
+ "This is very important to test future compatibility, meaning if someone adds a new model for\n",
+ "`XXXForQuestionAnswering` then the pipeline test will attempt to run on it. Because the models are random it's\n",
+ "impossible to check for actual values, that's why there is a helper `ANY` that will simply attempt to match the\n",
+ "output of the pipeline TYPE.\n",
+ "\n",
+ "You also *need* to implement 2 (ideally 4) tests.\n",
+ "\n",
+ "- `test_small_model_pt` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)\n",
+ " and test the pipeline outputs. The results should be the same as `test_small_model_tf`.\n",
+ "- `test_small_model_tf` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)\n",
+ " and test the pipeline outputs. The results should be the same as `test_small_model_pt`.\n",
+ "- `test_large_model_pt` (`optional`): Tests the pipeline on a real pipeline where the results are supposed to\n",
+ " make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make\n",
+ " sure there is no drift in future releases.\n",
+ "- `test_large_model_tf` (`optional`): Tests the pipeline on a real pipeline where the results are supposed to\n",
+ " make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make\n",
+ " sure there is no drift in future releases.\n",
+ "Document 4------------------------------------------------------------\n",
+ "```\n",
+ "\n",
+ "2. Pass a prompt to the pipeline to generate an image:\n",
+ "\n",
+ "```py\n",
+ "image = pipeline(\n",
+ "\t\"stained glass of darth vader, backlight, centered composition, masterpiece, photorealistic, 8k\"\n",
+ ").images[0]\n",
+ "image\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"==================================Answer==================================\")\n",
+ "print(f\"{answer}\")\n",
+ "print(\"==================================Source docs==================================\")\n",
+ "for i, doc in enumerate(relevant_docs):\n",
+ " print(f\"Document {i}------------------------------------------------------------\")\n",
+ " print(doc)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "w6iNo7lY9-9S"
+ },
+ "source": [
+ "β
We now have a fully functional, performant RAG sytem. That's it for today! Congratulations for making it to the end π₯³\n",
+ "\n",
+ "\n",
+ "# To go further πΊοΈ\n",
+ "\n",
+ "This is not the end of the journey! You can try many steps to improve your RAG system. We recommend doing so in an iterative way: bring small changes to the system and see what improves performance.\n",
+ "\n",
+ "### Setting up an evaluation pipeline\n",
+ "\n",
+ "- π¬ \"You cannot improve the model performance that you do not measure\", said Gandhi... or at least Llama2 told me he said it. Anyway, you should absolutely start by measuring performance: this means building a small evaluation dataset, then monitor the performance of your RAG system on this evaluation dataset.\n",
+ "\n",
+ "### Improving the retriever\n",
+ "\n",
+ "π οΈ __You can use these options to tune the results:__\n",
+ "\n",
+ "- Tune the chunking method:\n",
+ " - Size of the chunks\n",
+ " - Method: split on different separators, use [semantic chunking](https://python.langchain.com/docs/modules/data_connection/document_transformers/semantic-chunker)...\n",
+ "- Change the embedding model\n",
+ "\n",
+ "π·ββοΈ __More could be considered:__\n",
+ "- Try another chunking method, like semantic chunking\n",
+ "- Change the index used (here, FAISS)\n",
+ "- Query expansion: reformulate the user query in slightly different ways to retrieve more documents.\n",
+ "\n",
+ "### Improving the reader\n",
+ "\n",
+ "π οΈ __Here you can try the following options to improve results:__\n",
+ "- Tune the prompt\n",
+ "- Switch reranking on/off\n",
+ "- Choose a more powerful reader model\n",
+ "\n",
+ "π‘ __Many options could be considered here to further improve the results:__\n",
+ "- Compress the retrieved context to keep only the most relevant parts to answer the query.\n",
+ "- Extend the RAG system to make it more user-friendly:\n",
+ " - cite source\n",
+ " - make conversational"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ml2",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/src/notebooks/automatic_embedding.ipynb b/src/notebooks/automatic_embedding.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..176b26789df38b7be056a14bbbad729e02e85be2
--- /dev/null
+++ b/src/notebooks/automatic_embedding.ipynb
@@ -0,0 +1,825 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5d9aca72-957a-4ee2-862f-e011b9cd3a62",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "title: \"Inference Endpoints\"\n",
+ "---\n",
+ "\n",
+ "# How to use Inference Endpoints to Embed Documents\n",
+ "\n",
+ "_Authored by: [Derek Thomas](https://huggingface.co/derek-thomas)_\n",
+ "\n",
+ "## Goal\n",
+ "I have a dataset I want to embed for semantic search (or QA, or RAG), I want the easiest way to do embed this and put it in a new dataset.\n",
+ "\n",
+ "## Approach\n",
+ "I'm using a dataset from my favorite subreddit [r/bestofredditorupdates](https://www.reddit.com/r/bestofredditorupdates/). Because it has long entries, I will use the new [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) since it has an 8k context length. I will deploy this using [Inference Endpoint](https://huggingface.co/inference-endpoints) to save time and money. To follow this tutorial, you will need to **have already added a payment method**. If you haven't, you can add one here in [billing](https://huggingface.co/docs/hub/billing#billing). To make it even easier, I'll make this fully API based.\n",
+ "\n",
+ "To make this MUCH faster I will use the [Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference) image. This has many benefits like:\n",
+ "- No model graph compilation step\n",
+ "- Small docker images and fast boot times. Get ready for true serverless!\n",
+ "- Token based dynamic batching\n",
+ "- Optimized transformers code for inference using Flash Attention, Candle and cuBLASLt\n",
+ "- Safetensors weight loading\n",
+ "- Production ready (distributed tracing with Open Telemetry, Prometheus metrics)\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c830114-dd88-45a9-81b9-78b0e3da7384",
+ "metadata": {},
+ "source": [
+ "## Requirements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "35386f72-32cb-49fa-a108-3aa504e20429",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q aiohttp==3.8.3 datasets==2.14.6 pandas==1.5.3 requests==2.31.0 tqdm==4.66.1 huggingface-hub>=0.20"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f72042-173d-4a72-ade1-9304b43b528d",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e2beecdd-d033-4736-bd45-6754ec53b4ac",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import asyncio\n",
+ "from getpass import getpass\n",
+ "import json\n",
+ "from pathlib import Path\n",
+ "import time\n",
+ "from typing import Optional\n",
+ "\n",
+ "from aiohttp import ClientSession, ClientTimeout\n",
+ "from datasets import load_dataset, Dataset, DatasetDict\n",
+ "from huggingface_hub import notebook_login, create_inference_endpoint, list_inference_endpoints, whoami\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import requests\n",
+ "from tqdm.auto import tqdm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5eece903-64ce-435d-a2fd-096c0ff650bf",
+ "metadata": {},
+ "source": [
+ "## Config\n",
+ "`DATASET_IN` is where your text data is\n",
+ "`DATASET_OUT` is where your embeddings will be stored\n",
+ "\n",
+ "Note I used 5 for the `MAX_WORKERS` since `jina-embeddings-v2` are quite memory hungry. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "df2f79f0-9f28-46e6-9fc7-27e9537ff5be",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "DATASET_IN = 'derek-thomas/dataset-creator-reddit-bestofredditorupdates'\n",
+ "DATASET_OUT = \"processed-subset-bestofredditorupdates\"\n",
+ "ENDPOINT_NAME = \"boru-jina-embeddings-demo-ie\"\n",
+ "\n",
+ "MAX_WORKERS = 5 # This is for how many async workers you want. Choose based on the model and hardware \n",
+ "ROW_COUNT = 100 # Choose None to use all rows, Im using 100 just for a demo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e680f3d-4900-46cc-8b49-bb6ba3e27e2b",
+ "metadata": {},
+ "source": [
+ "Hugging Face offers a number of GPUs that you can choose from a number of GPUs that you can choose in Inference Endpoints. Here they are in table form:\n",
+ "\n",
+ "| GPU | instanceType | instanceSize | vRAM |\n",
+ "|---------------------|----------------|--------------|-------|\n",
+ "| 1x Nvidia Tesla T4 | g4dn.xlarge | small | 16GB |\n",
+ "| 4x Nvidia Tesla T4 | g4dn.12xlarge | large | 64GB |\n",
+ "| 1x Nvidia A10G | g5.2xlarge | medium | 24GB |\n",
+ "| 4x Nvidia A10G | g5.12xlarge | xxlarge | 96GB |\n",
+ "| 1x Nvidia A100* | p4de | xlarge | 80GB |\n",
+ "| 2x Nvidia A100* | p4de | 2xlarge | 160GB |\n",
+ "\n",
+ "\\*Note that for A100s you might get a note to email us to get access."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3c2106c1-2e5a-443a-9ea8-a3cd0e9c5a94",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# GPU Choice\n",
+ "VENDOR=\"aws\"\n",
+ "REGION=\"us-east-1\"\n",
+ "INSTANCE_SIZE=\"medium\"\n",
+ "INSTANCE_TYPE=\"g5.2xlarge\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "0ca1140c-3fcc-4b99-9210-6da1505a27b7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ee80821056e147fa9cabf30f64dc85a8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "VBox(children=(HTML(value='\n",
+ "\n",
+ "Implementing any of these improvements can bring a huge performance boost; but changing anything is useless if you cannot monitor the impact of your changes on the system's performance!\n",
+ "So let's see how to evaluate our RAG system.\n",
+ "\n",
+ "### Evaluating RAG performance\n",
+ "\n",
+ "Since there are so many moving parts to tune with a big impact on performance, benchmarking the RAG system is crucial.\n",
+ "\n",
+ "For our evaluation pipeline, we will need:\n",
+ "1. An evaluation dataset with question - answer couples (QA couples)\n",
+ "2. An evaluator to compute the accuracy of our system on the above evaluation dataset.\n",
+ "\n",
+ "β‘οΈ It turns out, we can use LLMs to help us all along the way!\n",
+ "1. The evaluation dataset will be synthetically generated by an LLM π€, and questions will be filtered out by other LLMs π€\n",
+ "2. An [LLM-as-a-judge](https://huggingface.co/papers/2306.05685) agent π€ will then perform the evaluation on this synthetic dataset.\n",
+ "\n",
+ "__Let's dig into it and start building our evaluation pipeline!__ First, we install the required model dependancies."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bCKBvOcp9jVK"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q torch transformers transformers langchain sentence-transformers faiss-gpu openpyxl openai"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "k_lJFbYm9jVL"
+ },
+ "outputs": [],
+ "source": [
+ "%reload_ext autoreload\n",
+ "%autoreload 2\n",
+ "%reload_ext dotenv\n",
+ "%dotenv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oIlNZ1Mn9jVL"
+ },
+ "outputs": [],
+ "source": [
+ "from tqdm.notebook import tqdm\n",
+ "import pandas as pd\n",
+ "from typing import Optional, List, Tuple\n",
+ "from langchain_core.language_models import BaseChatModel\n",
+ "import json\n",
+ "import datasets\n",
+ "\n",
+ "pd.set_option(\"display.max_colwidth\", None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zeW8P62J9jVM"
+ },
+ "source": [
+ "### Load your knowledge base"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YRbm5tNF9jVM"
+ },
+ "outputs": [],
+ "source": [
+ "ds = datasets.load_dataset(\"m-ric/huggingface_doc\", split=\"train\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wy9CKj0M9jVM"
+ },
+ "source": [
+ "# 1. Build a synthetic dataset for evaluation\n",
+ "We first build a synthetic dataset of questions and associated contexts. The method is to get elements from our knowledge base, and ask an LLM to generate questions based on these documents.\n",
+ "\n",
+ "Then we setup other LLM agents to act as quality filters for the generated QA couples: each of them will act as the filter for a specific flaw."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QkoEgiDg9jVM"
+ },
+ "source": [
+ "### 1.1. Prepare source documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3gTOlRKO9jVM"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+ "from langchain.docstore.document import Document as LangchainDocument\n",
+ "\n",
+ "langchain_docs = [\n",
+ " LangchainDocument(page_content=doc[\"text\"], metadata={\"source\": doc[\"source\"]})\n",
+ " for doc in tqdm(ds)\n",
+ "]\n",
+ "\n",
+ "\n",
+ "text_splitter = RecursiveCharacterTextSplitter(\n",
+ " chunk_size=2000,\n",
+ " chunk_overlap=200,\n",
+ " add_start_index=True,\n",
+ " separators=[\"\\n\\n\", \"\\n\", \".\", \" \", \"\"],\n",
+ ")\n",
+ "\n",
+ "docs_processed = []\n",
+ "for doc in langchain_docs:\n",
+ " docs_processed += text_splitter.split_documents([doc])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WjrNhcCh9jVN"
+ },
+ "source": [
+ "### 1.2. Setup agents for question generation\n",
+ "\n",
+ "We use [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) for QA couple generation because it it has excellent performance in leaderboards such as [Chatbot Arena](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GoRySj3Q9jVN"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_community.llms import HuggingFaceHub\n",
+ "\n",
+ "repo_id = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n",
+ "\n",
+ "llm = HuggingFaceHub(\n",
+ " repo_id=repo_id,\n",
+ " task=\"text-generation\",\n",
+ " model_kwargs={\n",
+ " \"max_new_tokens\": 512,\n",
+ " \"top_k\": 30,\n",
+ " \"temperature\": 0.1,\n",
+ " \"repetition_penalty\": 1.03,\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wubTNTaV9jVN"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_community.chat_models import ChatHuggingFace\n",
+ "\n",
+ "chat_model = ChatHuggingFace(llm=llm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hIM_DJRo9jVN"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.prompts import ChatPromptTemplate\n",
+ "\n",
+ "QA_generation_prompt = \"\"\"\n",
+ "Your task is to write a factoid question and an answer given a context.\n",
+ "Your factoid question should be answerable with a specific, concise piece of factual information from the context.\n",
+ "Your factoid question should be formulated in the same style as questions users could ask in a search engine.\n",
+ "This means that your factoid question MUST NOT mention something like \"according to the passage\" or \"context\".\n",
+ "\n",
+ "Provide your answer as follows:\n",
+ "\n",
+ "Output:::\n",
+ "Factoid question: (your factoid question)\n",
+ "Answer: (your answer to the factoid question)\n",
+ "\n",
+ "Now here is the context.\n",
+ "\n",
+ "Context: {context}\\n\n",
+ "Output:::\"\"\"\n",
+ "\n",
+ "QA_generation_prompt = ChatPromptTemplate.from_template(QA_generation_prompt)\n",
+ "QA_generation_agent = QA_generation_prompt | chat_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lVFc-lVy9jVN"
+ },
+ "source": [
+ "Now let's generate our QA couples.\n",
+ "For this example, we generate only 10 QA couples and will load the rest from the Hub.\n",
+ "\n",
+ "But for your specific knowledge base, given that you want to get at least ~100 test samples, and accounting for the fact that we will filter out around half of these with our critique agents later on, you should generate much more, in the >200 samples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8fteqDDD9jVN"
+ },
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "\n",
+ "N_GENERATIONS = (\n",
+ " 10 # We intentionally generate only 10 QA couples here for cost and time considerations\n",
+ ")\n",
+ "\n",
+ "print(f\"Generating {N_GENERATIONS} QA couples...\")\n",
+ "outputs = []\n",
+ "for context in tqdm(random.sample(langchain_docs, N_GENERATIONS)):\n",
+ " # Generate QA couple\n",
+ " output_QA_couple = QA_generation_agent.invoke({\"context\": context.page_content}).content\n",
+ " try:\n",
+ " question = output_QA_couple.split(\"Factoid question: \")[1].split(\"Answer: \")[0]\n",
+ " answer = output_QA_couple.split(\"Answer: \")[1]\n",
+ " outputs.append(\n",
+ " {\n",
+ " \"context\": context.page_content,\n",
+ " \"question\": question,\n",
+ " \"answer\": answer,\n",
+ " \"source_doc\": context.metadata[\"source\"],\n",
+ " }\n",
+ " )\n",
+ " except:\n",
+ " continue"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aUlOUDv59jVN",
+ "outputId": "c9634fdb-2a7f-43a6-c4eb-e60b166b8238"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | context | \n", + "question | \n", + "answer | \n", + "source_doc | \n", + "
|---|---|---|---|---|
| 0 | \n", + "!--Copyright 2023 The HuggingFace Team. All rights reserved.\\n\\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with\\nthe License. You may obtain a copy of the License at\\n\\nhttp://www.apache.org/licenses/LICENSE-2.0\\n\\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on\\nan \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the\\nspecific language governing permissions and limitations under the License.\\n-->\\n\\n# Schedulers\\n\\nπ€ Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model's output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is generated by iterating forward *n* timesteps and inference occurs by propagating backward through the timesteps. Based on the timestep, a scheduler may be *discrete* in which case the timestep is an `int` or *continuous* in which case the timestep is a `float`.\\n\\nDepending on the context, a scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output:\\n\\n- during *training*, a scheduler adds noise (there are different algorithms for how to add noise) to a sample to train a diffusion model\\n- during *inference*, a scheduler defines how to update a sample based on a pretrained model's output\\n\\nMany schedulers are implemented from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library by [Katherine Crowson](https://github.com/crowsonkb/), and they're also widely used in A1111. To help you map the schedulers from k-diffusion and A1111 to the schedulers in π€ Diffusers, take a look at the table below:\\n\\n| A1111/k-diffusion | π€ Diffusers | Usage |\\n|---------------------|-------------------------------------|---------------------------------------------------------------------------------------------------------------|\\n| DPM++ 2M | [`DPMSolverMultistepScheduler`] | |\\n| DPM++ 2M Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM++ 2M SDE | [`DPMSolverMultistepScheduler`] | init with `algorithm_type=\"sde-dpmsolver++\"` |\\n| DPM++ 2M SDE Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` and `algorithm_type=\"sde-dpmsolver++\"` |\\n| DPM++ 2S a | N/A | very similar to `DPMSolverSinglestepScheduler` |\\n| DPM++ 2S a Karras | N/A | very similar to `DPMSolverSinglestepScheduler(use_karras_sigmas=True, ...)` |\\n| DPM++ SDE | [`DPMSolverSinglestepScheduler`] | |\\n| DPM++ SDE Karras | [`DPMSolverSinglestepScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM2 | [`KDPM2DiscreteScheduler`] | |\\n| DPM2 Karras | [`KDPM2DiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM2 a | [`KDPM2AncestralDiscreteScheduler`] | |\\n| DPM2 a Karras | [`KDPM2AncestralDiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM adaptive | N/A | |\\n| DPM fast | N/A | |\\n| Euler | [`EulerDiscreteScheduler`] | |\\n| Euler a | [`EulerAncestralDiscreteScheduler`] | |\\n| Heun | [`HeunDiscreteScheduler`] | |\\n| LMS | [`LMSDiscreteScheduler`] | |\\n| LMS Karras | [`LMSDiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| N/A | [`DEISMultistepScheduler`] | |\\n| N/A | [`UniPCMultistepScheduler`] | |\\n\\nAll schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers.\\n\\n## SchedulerMixin\\n[[autodoc]] SchedulerMixin\\n\\n## SchedulerOutput\\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\\n\\n## KarrasDiffusionSchedulers\\n\\n[`KarrasDiffusionSchedulers`] are a broad generalization of schedulers in π€ Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, the training strategy, and how the loss is weighed.\\n\\nThe different schedulers in this class, depending on the ordinary differential equations (ODE) solver type, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in π€ Diffusers. The schedulers in this class are given [here](https://github.com/huggingface/diffusers/blob/a69754bb879ed55b9b6dc9dd0b3cf4fa4124c765/src/diffusers/schedulers/scheduling_utils.py#L32).\\n\\n## PushToHubMixin\\n\\n[[autodoc]] utils.PushToHubMixin\\n | \n", + "What is the class of schedulers in π€ Diffusers that are distinguished by their noise sampling strategy, type of network and scaling, training strategy, and loss weighing?\\n | \n", + "[`KarrasDiffusionSchedulers`] | \n", + "huggingface/diffusers/blob/main/docs/source/en/api/schedulers/overview.md | \n", + "
| \n", + " | question | \n", + "answer | \n", + "groundedness_score | \n", + "relevance_score | \n", + "standalone_score | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "What is the class of schedulers in π€ Diffusers that are distinguished by their noise sampling strategy, type of network and scaling, training strategy, and loss weighing?\\n | \n", + "[`KarrasDiffusionSchedulers`] | \n", + "3.0 | \n", + "1.0 | \n", + "4.0 | \n", + "
| 1 | \n", + "What are some utility functions provided by the Hugging Face library for pipelines?\\n | \n", + "The Hugging Face library provides several utility functions for pipelines, including `ArgumentHandler`, `ZeroShotClassificationArgumentHandler`, `QuestionAnsweringArgumentHandler` for argument handling, `PipelineDataFormat`, `CsvPipelineDataFormat`, `JsonPipelineDataFormat`, `PipedPipelineDataFormat` for data format, and `PipelineException` for exceptions. | \n", + "5.0 | \n", + "4.0 | \n", + "5.0 | \n", + "
| 2 | \n", + "What is the default name used in the Gradio demo if no name is provided?\\n | \n", + "User\\n\\nExplanation: The factoid question asks for the default name used in the Gradio demo if no name is provided. The answer to this question can be found in the `argparse.ArgumentParser()` function, where a default value of \"User\" is set for the `--name` argument. | \n", + "5.0 | \n", + "3.0 | \n", + "5.0 | \n", + "
| 3 | \n", + "What is the function used to load a pre-trained Resnet-18 model in the provided context?\\n | \n", + "The function used to load a pre-trained Resnet-18 model in the provided context is `torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()`. | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 4 | \n", + "What is the name of the component used for creating a button in the given code?\\n | \n", + "The name of the component used for creating a button in the given code is `BaseButton`. | \n", + "5.0 | \n", + "1.0 | \n", + "5.0 | \n", + "
| 5 | \n", + "What is the command to get the example ONNX file for Bart model?\\n | \n", + "The command is `python run_onnx_exporter.py --model_name_or_path facebook/bart-base`. | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 6 | \n", + "What will be covered in the next unit of the course?\\n | \n", + "The next unit of the course will cover learning more about Unity MLAgents and training agents in Unity environments. It will also prepare students for AI vs AI challenges where they will train their agents to compete against other agents in a snowball fight and a soccer game. | \n", + "5.0 | \n", + "1.0 | \n", + "5.0 | \n", + "
| 7 | \n", + "What is the purpose of the `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` parameters in SDXL?\\n | \n", + "These parameters allow SDXL to negatively condition the model on image resolution and cropping parameters. | \n", + "2.0 | \n", + "4.0 | \n", + "2.0 | \n", + "
| 8 | \n", + "How are transformers models tested in the Hugging Face repository?\\n | \n", + "Transformers models are tested in the Hugging Face repository using two test suites: `tests` for the general API and `examples` for various applications that aren't part of the API. These tests are run on CircleCI and GitHub Actions, with different jobs and configurations for each. The tests can be run in various ways, including running all tests, getting the list of all tests, running a specific test module, and running specific tests by name or keyword expression. Additionally, there are options for running tests in parallel, repeating tests, and running tests on a specific GPU or CPU. | \n", + "3.0 | \n", + "4.0 | \n", + "4.0 | \n", + "
| 9 | \n", + "What command is used to create a virtual environment in the given context?\\n | \n", + "The command used to create a virtual environment in the given context is `python -m venv <env_name>`. | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| \n", + " | question | \n", + "answer | \n", + "groundedness_score | \n", + "relevance_score | \n", + "standalone_score | \n", + "
|---|---|---|---|---|---|
| 1 | \n", + "What are some utility functions provided by the Hugging Face library for pipelines?\\n | \n", + "The Hugging Face library provides several utility functions for pipelines, including `ArgumentHandler`, `ZeroShotClassificationArgumentHandler`, `QuestionAnsweringArgumentHandler` for argument handling, `PipelineDataFormat`, `CsvPipelineDataFormat`, `JsonPipelineDataFormat`, `PipedPipelineDataFormat` for data format, and `PipelineException` for exceptions. | \n", + "5.0 | \n", + "4.0 | \n", + "5.0 | \n", + "
![](\"https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/RAG_settings_accuracy.png\")
\n",
+ "\n",
+ "As you can see, these had varying impact on performance. In particular, tuning the chunk size is both easy and very impactful.\n",
+ "\n",
+ "But this is our case: your results could be very different: now that you have a robust evaluation pipeline, you can set on to explore other options! πΊοΈ"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ml2",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/src/notebooks/rag_zephyr_langchain.ipynb b/src/notebooks/rag_zephyr_langchain.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1bcf53f4881edd13ea3c357a9483035b52446224
--- /dev/null
+++ b/src/notebooks/rag_zephyr_langchain.ipynb
@@ -0,0 +1,527 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Kih21u1tyr-I"
+ },
+ "source": [
+ "---\n",
+ "title: Simple RAG\n",
+ "---\n",
+ "\n",
+ "# Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain\n",
+ "\n",
+ "_Authored by: [Maria Khalusova](https://github.com/MKhalusova)_\n",
+ "\n",
+ "This notebook demonstrates how you can quickly build a RAG (Retrieval Augmented Generation) for a project's GitHub issues using [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) model, and LangChain.\n",
+ "\n",
+ "\n",
+ "**What is RAG?**\n",
+ "\n",
+ "RAG is a popular approach to address the issue of a powerful LLM not being aware of specific content due to said content not being in its training data, or hallucinating even when it has seen it before. Such specific content may be proprietary, sensitive, or, as in this example, recent and updated often.\n",
+ "\n",
+ "If your data is static and doesn't change regularly, you may consider fine-tuning a large model. In many cases, however, fine-tuning can be costly, and, when done repeatedly (e.g. to address data drift), leads to \"model shift\". This is when the model's behavior changes in ways that are not desirable.\n",
+ "\n",
+ "**RAG (Retrieval Augmented Generation)** does not require model fine-tuning. Instead, RAG works by providing an LLM with additional context that is retrieved from relevant data so that it can generate a better-informed response.\n",
+ "\n",
+ "Here's a quick illustration:\n",
+ "\n",
+ "\n",
+ "\n",
+ "* The external data is converted into embedding vectors with a separate embeddings model, and the vectors are kept in a database. Embeddings models are typically small, so updating the embedding vectors on a regular basis is faster, cheaper, and easier than fine-tuning a model.\n",
+ "\n",
+ "* At the same time, the fact that fine-tuning is not required gives you the freedom to swap your LLM for a more powerful one when it becomes available, or switch to a smaller distilled version, should you need faster inference.\n",
+ "\n",
+ "Let's illustrate building a RAG using an open-source LLM, embeddings model, and LangChain.\n",
+ "\n",
+ "First, install the required dependencies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lC9frDOlyi38"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q torch transformers accelerate bitsandbytes transformers sentence-transformers faiss-gpu"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "-aYENQwZ-p_c"
+ },
+ "outputs": [],
+ "source": [
+ "# If running in Google Colab, you may need to run this cell to make sure you're using UTF-8 locale to install LangChain\n",
+ "import locale\n",
+ "locale.getpreferredencoding = lambda: \"UTF-8\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "W5HhMZ2c-NfU"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q langchain"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R8po01vMWzXL"
+ },
+ "source": [
+ "## Prepare the data\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3cCmQywC04x6"
+ },
+ "source": [
+ "In this example, we'll load all of the issues (both open and closed) from [PEFT library's repo](https://github.com/huggingface/peft).\n",
+ "\n",
+ "First, you need to acquire a [GitHub personal access token](https://github.com/settings/tokens?type=beta) to access the GitHub API."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8MoD7NbsNjlM"
+ },
+ "outputs": [],
+ "source": [
+ "from getpass import getpass\n",
+ "ACCESS_TOKEN = getpass(\"YOUR_GITHUB_PERSONAL_TOKEN\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fccecm3a10N6"
+ },
+ "source": [
+ "Next, we'll load all of the issues in the [huggingface/peft](https://github.com/huggingface/peft) repo:\n",
+ "- By default, pull requests are considered issues as well, here we chose to exclude them from data with by setting `include_prs=False`\n",
+ "- Setting `state = \"all\"` means we will load both open and closed issues."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "8EKMit4WNDY8"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.document_loaders import GitHubIssuesLoader\n",
+ "\n",
+ "loader = GitHubIssuesLoader(\n",
+ " repo=\"huggingface/peft\",\n",
+ " access_token=ACCESS_TOKEN,\n",
+ " include_prs=False,\n",
+ " state=\"all\"\n",
+ ")\n",
+ "\n",
+ "docs = loader.load()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CChTrY-k2qO5"
+ },
+ "source": [
+ "The content of individual GitHub issues may be longer than what an embedding model can take as input. If we want to embed all of the available content, we need to chunk the documents into appropriately sized pieces.\n",
+ "\n",
+ "The most common and straightforward approach to chunking is to define a fixed size of chunks and whether there should be any overlap between them. Keeping some overlap between chunks allows us to preserve some semantic context between the chunks.\n",
+ "\n",
+ "Other approaches are typically more involved and take into account the documents' structure and context. For example, one may want to split a document based on sentences or paragraphs, or create chunks based on the\n",
+ "\n",
+ "The fixed-size chunking, however, works well for most common cases, so that is what we'll do here."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "OmsXOf59Pmm-"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.text_splitter import CharacterTextSplitter\n",
+ "\n",
+ "splitter = CharacterTextSplitter(chunk_size=512, chunk_overlap=30)\n",
+ "\n",
+ "chunked_docs = splitter.split_documents(docs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DAt_zPVlXOn7"
+ },
+ "source": [
+ "## Create the embeddings + retriever"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-mvat6JQl4yp"
+ },
+ "source": [
+ "Now that the docs are all of the appropriate size, we can create a database with their embeddings.\n",
+ "\n",
+ "To create document chunk embeddings we'll use the `HuggingFaceEmbeddings` and the [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model. There are many other embeddings models available on the Hub, and you can keep an eye on the best performing ones by checking the [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).\n",
+ "\n",
+ "\n",
+ "To create the vector database, we'll use `FAISS`, a library developed by Facebook AI. This library offers efficient similarity search and clustering of dense vectors, which is what we need here. FAISS is currently one of the most used libraries for NN search in massive datasets.\n",
+ "\n",
+ "We'll access both the embeddings model and FAISS via LangChain API."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ixmCdRzBQ5gu"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.vectorstores import FAISS\n",
+ "from langchain.embeddings import HuggingFaceEmbeddings\n",
+ "\n",
+ "db = FAISS.from_documents(chunked_docs,\n",
+ " HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2iCgEPi0nnN6"
+ },
+ "source": [
+ "We need a way to return(retrieve) the documents given an unstructured query. For that, we'll use the `as_retriever` method using the `db` as a backbone:\n",
+ "- `search_type=\"similarity\"` means we want to perform similarity search between the query and documents\n",
+ "- `search_kwargs={'k': 4}` instructs the retriever to return top 4 results.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "mBTreCQ9noHK"
+ },
+ "outputs": [],
+ "source": [
+ "retriever = db.as_retriever(\n",
+ " search_type=\"similarity\",\n",
+ " search_kwargs={'k': 4}\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WgEhlISJpTgj"
+ },
+ "source": [
+ "The vector database and retriever are now set up, next we need to set up the next piece of the chain - the model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tzQxx0HkXVFU"
+ },
+ "source": [
+ "## Load quantized model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9jy1cC65p_GD"
+ },
+ "source": [
+ "For this example, we chose [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a small but powerful model.\n",
+ "\n",
+ "With many models being released every week, you may want to substitute this model to the latest and greatest. The best way to keep track of open source LLMs is to check the [Open-source LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n",
+ "\n",
+ "To make inference faster, we will load the quantized version of the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "L-ggaa763VRo"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
+ "\n",
+ "model_name = 'HuggingFaceH4/zephyr-7b-beta'\n",
+ "\n",
+ "bnb_config = BitsAndBytesConfig(\n",
+ " load_in_4bit=True,\n",
+ " bnb_4bit_use_double_quant=True,\n",
+ " bnb_4bit_quant_type=\"nf4\",\n",
+ " bnb_4bit_compute_dtype=torch.bfloat16\n",
+ ")\n",
+ "\n",
+ "model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hVNRJALyXYHG"
+ },
+ "source": [
+ "## Setup the LLM chain"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RUUNneJ1smhl"
+ },
+ "source": [
+ "Finally, we have all the pieces we need to set up the LLM chain.\n",
+ "\n",
+ "First, create a text_generation pipeline using the loaded model and its tokenizer.\n",
+ "\n",
+ "Next, create a prompt template - this should follow the format of the model, so if you substitute the model checkpoint, make sure to use the appropriate formatting."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "cR0k1cRWz8Pm"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.llms import HuggingFacePipeline\n",
+ "from langchain.prompts import PromptTemplate\n",
+ "from transformers import pipeline\n",
+ "from langchain_core.output_parsers import StrOutputParser\n",
+ "\n",
+ "text_generation_pipeline = pipeline(\n",
+ " model=model,\n",
+ " tokenizer=tokenizer,\n",
+ " task=\"text-generation\",\n",
+ " temperature=0.2,\n",
+ " do_sample=True,\n",
+ " repetition_penalty=1.1,\n",
+ " return_full_text=True,\n",
+ " max_new_tokens=400,\n",
+ ")\n",
+ "\n",
+ "llm = HuggingFacePipeline(pipeline=text_generation_pipeline)\n",
+ "\n",
+ "prompt_template = \"\"\"\n",
+ "<|system|>\n",
+ "Answer the question based on your knowledge. Use the following context to help:\n",
+ "\n",
+ "{context}\n",
+ "\n",
+ "\n",
+ "<|user|>\n",
+ "{question}\n",
+ "\n",
+ "<|assistant|>\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ "prompt = PromptTemplate(\n",
+ " input_variables=[\"context\", \"question\"],\n",
+ " template=prompt_template,\n",
+ ")\n",
+ "\n",
+ "llm_chain = prompt | llm | StrOutputParser()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "l19UKq5HXfSp"
+ },
+ "source": [
+ "Note: _You can also use `tokenizer.apply_chat_template` to convert a list of messages (as dicts: `{'role': 'user', 'content': '(...)'}`) into a string with the appropriate chat format._\n",
+ "\n",
+ "\n",
+ "Finally, we need to combine the `llm_chain` with the retriever to create a RAG chain. We pass the original question through to the final generation step, as well as the retrieved context docs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "_rI3YNp9Xl4s"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_core.runnables import RunnablePassthrough\n",
+ "\n",
+ "retriever = db.as_retriever()\n",
+ "\n",
+ "rag_chain = (\n",
+ " {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+ " | llm_chain\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UsCOhfDDXpaS"
+ },
+ "source": [
+ "## Compare the results\n",
+ "\n",
+ "Let's see the difference RAG makes in generating answers to the library-specific questions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "W7F07fQLXusU"
+ },
+ "outputs": [],
+ "source": [
+ "question = \"How do you combine multiple adapters?\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KC0rJYU1x1ir"
+ },
+ "source": [
+ "First, let's see what kind of answer we can get with just the model itself, no context added:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 125
+ },
+ "id": "GYh-HG1l0De5",
+ "outputId": "277d8e89-ce9b-4e04-c11b-639ad2645759"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "\" To combine multiple adapters, you need to ensure that they are compatible with each other and the devices you want to connect. Here's how you can do it:\\n\\n1. Identify the adapters you need: Determine which adapters you require to connect the devices you want to use together. For example, if you want to connect a USB-C device to an HDMI monitor, you may need a USB-C to HDMI adapter and a USB-C to USB-A adapter (if your computer only has USB-A ports).\\n\\n2. Connect the first adapter: Plug in the first adapter into the device you want to connect. For instance, if you're connecting a USB-C laptop to an HDMI monitor, plug the USB-C to HDMI adapter into the laptop's USB-C port.\\n\\n3. Connect the second adapter: Next, connect the second adapter to the first one. In this case, connect the USB-C to USB-A adapter to the USB-C port of the USB-C to HDMI adapter.\\n\\n4. Connect the final device: Finally, connect the device you want to use to the second adapter. For example, connect the HDMI cable from the monitor to the HDMI port on the USB-C to HDMI adapter.\\n\\n5. Test the connection: Turn on both devices and check whether everything is working correctly. If necessary, adjust the settings on your devices to ensure optimal performance.\\n\\nBy combining multiple adapters, you can connect a variety of devices together, even if they don't have the same type of connector. Just be sure to choose adapters that are compatible with all the devices you want to connect and test the connection thoroughly before relying on it for critical tasks.\""
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "llm_chain.invoke({\"context\":\"\", \"question\": question})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i-TIWr3wx9w8"
+ },
+ "source": [
+ "As you can see, the model interpreted the question as one about physical computer adapters, while in the context of PEFT, \"adapters\" refer to LoRA adapters.\n",
+ "Let's see if adding context from GitHub issues helps the model give a more relevant answer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 125
+ },
+ "id": "FZpNA3o10H10",
+ "outputId": "31f9aed3-3dd7-4ff8-d1a8-866794fefe80"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "\" Based on the provided context, it seems that combining multiple adapters is still an open question in the community. Here are some possibilities:\\n\\n 1. Save the output from the base model and pass it to each adapter separately, as described in the first context snippet. This allows you to run multiple adapters simultaneously and reuse the output from the base model. However, this approach requires loading and running each adapter separately.\\n\\n 2. Export everything into a single PyTorch model, as suggested in the second context snippet. This would involve saving all the adapters and their weights into a single model, potentially making it larger and more complex. The advantage of this approach is that it would allow you to run all the adapters simultaneously without having to load and run them separately.\\n\\n 3. Merge multiple Lora adapters, as mentioned in the third context snippet. This involves adding multiple distinct, independent behaviors to a base model by merging multiple Lora adapters. It's not clear from the context how this would be done, but it suggests that there might be a recommended way of doing it.\\n\\n 4. Combine adapters through a specific architecture, as proposed in the fourth context snippet. This involves merging multiple adapters into a single architecture, potentially creating a more complex model with multiple behaviors. Again, it's not clear from the context how this would be done.\\n\\n Overall, combining multiple adapters is still an active area of research, and there doesn't seem to be a widely accepted solution yet. If you're interested in exploring this further, it might be worth reaching out to the Hugging Face community or checking out their documentation for more information.\""
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rag_chain.invoke(question)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hZQedZKSyrwO"
+ },
+ "source": [
+ "As we can see, the added context, really helps the exact same model, provide a much more relevant and informed answer to the library-specific question.\n",
+ "\n",
+ "Notably, combining multiple adapters for inference has been added to the library, and one can find this information in the documentation, so for the next iteration of this RAG it may be worth including documentation embeddings."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/src/notebooks/single_gpu.ipynb b/src/notebooks/single_gpu.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f59a9ad16e3388b316c89121ab19a4126a02e35a
--- /dev/null
+++ b/src/notebooks/single_gpu.ipynb
@@ -0,0 +1,1129 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FNdZ-kD0l78P"
+ },
+ "source": [
+ "---\n",
+ "title: Single GPU Fine-tuning\n",
+ "---\n",
+ "\n",
+ "# Fine-tuning a Code LLM on Custom Code on a single GPU\n",
+ "\n",
+ "_Authored by: [Maria Khalusova](https://github.com/MKhalusova)_\n",
+ "\n",
+ "Publicly available code LLMs such as Codex, StarCoder, and Code Llama are great at generating code that adheres to general programming principles and syntax, but they may not align with an organization's internal conventions, or be aware of proprietary libraries.\n",
+ "\n",
+ "In this notebook, we'll see show how you can fine-tune a code LLM on private code bases to enhance its contextual awareness and improve a model's usefulness to your organization's needs. Since the code LLMs are quite large, fine-tuning them in a traditional manner can be resource-draining. Worry not! We will show how you can optimize fine-tuning to fit on a single GPU.\n",
+ "\n",
+ "\n",
+ "## Dataset\n",
+ "\n",
+ "For this example, we picked the top 10 Hugging Face public repositories on GitHub. We have excluded non-code files from the data, such as images, audio files, presentations, and so on. For Jupyter notebooks, we've kept only cells containing code. The resulting code is stored as a dataset that you can find on the Hugging Face Hub under [`smangrul/hf-stack-v1`](https://huggingface.co/datasets/smangrul/hf-stack-v1). It contains repo id, file path, and file content.\n",
+ "\n",
+ "\n",
+ "## Model\n",
+ "\n",
+ "We'll finetune [`bigcode/starcoderbase-1b`](https://huggingface.co/bigcode/starcoderbase-1b), which is a 1B parameter model trained on 80+ programming languages. This is a gated model, so if you plan to run this notebook with this exact model, you'll need to gain access to it on the model's page. Log in to your Hugging Face account to do so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bPlCJYDK6vrF"
+ },
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WMVe_c8q43Qo"
+ },
+ "source": [
+ "To get started, let's install all the necessary libraries. As you can see, in addition to `transformers` and `datasets`, we'll be using `peft`, `bitsandbytes`, and `flash-attn` to optimize the training.\n",
+ "\n",
+ "By employing parameter-efficient training techniques, we can run this notebook on a single A100 High-RAM GPU."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Fp7i8WMCjKJG"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q transformers datasets peft bitsandbytes flash-attn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "16EdABzt3_Ig"
+ },
+ "source": [
+ "Let's define some variables now. Feel free to play with these."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hru3G-CLmqis"
+ },
+ "outputs": [],
+ "source": [
+ "MODEL=\"bigcode/starcoderbase-1b\" # Model checkpoint on the Hugging Face Hub\n",
+ "DATASET=\"smangrul/hf-stack-v1\" # Dataset on the Hugging Face Hub\n",
+ "DATA_COLUMN=\"content\" # Column name containing the code content\n",
+ "\n",
+ "SEQ_LENGTH=2048 # Sequence length\n",
+ "\n",
+ "# Training arguments\n",
+ "MAX_STEPS=2000 # max_steps\n",
+ "BATCH_SIZE=16 # batch_size\n",
+ "GR_ACC_STEPS=1 # gradient_accumulation_steps\n",
+ "LR=5e-4 # learning_rate\n",
+ "LR_SCHEDULER_TYPE=\"cosine\" # lr_scheduler_type\n",
+ "WEIGHT_DECAY=0.01 # weight_decay\n",
+ "NUM_WARMUP_STEPS=30 # num_warmup_steps\n",
+ "EVAL_FREQ=100 # eval_freq\n",
+ "SAVE_FREQ=100 # save_freq\n",
+ "LOG_FREQ=25 # log_freq\n",
+ "OUTPUT_DIR=\"peft-starcoder-lora-a100\" # output_dir\n",
+ "BF16=True # bf16\n",
+ "FP16=False # no_fp16\n",
+ "\n",
+ "# FIM trasformations arguments\n",
+ "FIM_RATE=0.5 # fim_rate\n",
+ "FIM_SPM_RATE=0.5 # fim_spm_rate\n",
+ "\n",
+ "# LORA\n",
+ "LORA_R=8 # lora_r\n",
+ "LORA_ALPHA=32 # lora_alpha\n",
+ "LORA_DROPOUT=0.0 # lora_dropout\n",
+ "LORA_TARGET_MODULES=\"c_proj,c_attn,q_attn,c_fc,c_proj\" # lora_target_modules\n",
+ "\n",
+ "# bitsandbytes config\n",
+ "USE_NESTED_QUANT=True # use_nested_quant\n",
+ "BNB_4BIT_COMPUTE_DTYPE=\"bfloat16\"# bnb_4bit_compute_dtype\n",
+ "\n",
+ "SEED=0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "FyZSXTbJrcnC"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import (\n",
+ " AutoModelForCausalLM,\n",
+ " AutoTokenizer,\n",
+ " Trainer,\n",
+ " TrainingArguments,\n",
+ " logging,\n",
+ " set_seed,\n",
+ " BitsAndBytesConfig,\n",
+ ")\n",
+ "\n",
+ "set_seed(SEED)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pO7F5L5AtKo1"
+ },
+ "source": [
+ "## Prepare the data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1LmrIZqP0oUE"
+ },
+ "source": [
+ "Begin by loading the data. As the dataset is likely to be quite large, make sure to enable the streaming mode. Streaming allows us to load the data progressively as we iterate over the dataset instead of downloading the whole dataset at once.\n",
+ "\n",
+ "We'll reserve the first 4000 examples as the validation set, and everything else will be the training data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4oJZvZb-1J88"
+ },
+ "outputs": [],
+ "source": [
+ "from datasets import load_dataset\n",
+ "import torch\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "\n",
+ "dataset = load_dataset(\n",
+ " DATASET,\n",
+ " data_dir=\"data\",\n",
+ " split=\"train\",\n",
+ " streaming=True,\n",
+ ")\n",
+ "\n",
+ "valid_data = dataset.take(4000)\n",
+ "train_data = dataset.skip(4000)\n",
+ "train_data = train_data.shuffle(buffer_size=5000, seed=SEED)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sLQ8t0LM2GR6"
+ },
+ "source": [
+ "At this step, the dataset still contains raw data with code of arbitraty length. For training, we need inputs of fixed length. Let's create an Iterable dataset that would return constant-length chunks of tokens from a stream of text files.\n",
+ "\n",
+ "First, let's estimate the average number of characters per token in the dataset, which will help us later estimate the number of tokens in the text buffer later. By default, we'll only take 400 examples (`nb_examples`) from the dataset. Using only a subset of the entire dataset will reduce computational cost while still providing a reasonable estimate of the overall character-to-token ratio."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KCiAvydztNsu",
+ "outputId": "cabf7fd0-a922-4371-cbc6-60ee99ef7469"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|ββββββββββ| 400/400 [00:10<00:00, 39.87it/s] "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The character to token ratio of the dataset is: 2.43\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n",
+ "\n",
+ "def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):\n",
+ " \"\"\"\n",
+ " Estimate the average number of characters per token in the dataset.\n",
+ " \"\"\"\n",
+ "\n",
+ " total_characters, total_tokens = 0, 0\n",
+ " for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):\n",
+ " total_characters += len(example[data_column])\n",
+ " total_tokens += len(tokenizer(example[data_column]).tokens())\n",
+ "\n",
+ " return total_characters / total_tokens\n",
+ "\n",
+ "\n",
+ "chars_per_token = chars_token_ratio(train_data, tokenizer, DATA_COLUMN)\n",
+ "print(f\"The character to token ratio of the dataset is: {chars_per_token:.2f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6F13VGobB3Ma"
+ },
+ "source": [
+ "The character-to-token ratio can also be used as an indicator of the quality of text tokenization. For instance, a character-to-token ratio of 1.0 would mean that each character is represented with a token, which is not very meaningful. This would indicate poor tokenization. In standard English text, one token is typically equivalent to approximately four characters, meaning the character-to-token ratio is around 4.0. We can expect a lower ratio in the code dataset, but generally speaking, a number between 2.0 and 3.5 can be considered good enough."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rcwYFRPpwxea"
+ },
+ "source": [
+ "**Optional FIM transformations**\n",
+ "\n",
+ "\n",
+ "Autoregressive language models typically generate sequences from left to right. By applying the FIM transformations, the model can also learn to infill text. Check out [\"Efficient Training of Language Models to Fill in the Middle\" paper](https://arxiv.org/pdf/2207.14255.pdf) to learn more about the technique.\n",
+ "We'll define the FIM transformations here and will use them when creating the Iterable Dataset. However, if you want to omit transformations, feel free to set `fim_rate` to 0."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zmejYvEKw1E-"
+ },
+ "outputs": [],
+ "source": [
+ "import functools\n",
+ "import numpy as np\n",
+ "\n",
+ "\n",
+ "# Helper function to get token ids of the special tokens for prefix, suffix and middle for FIM transformations.\n",
+ "@functools.lru_cache(maxsize=None)\n",
+ "def get_fim_token_ids(tokenizer):\n",
+ " try:\n",
+ " FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD = tokenizer.special_tokens_map[\"additional_special_tokens\"][1:5]\n",
+ " suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (\n",
+ " tokenizer.vocab[tok] for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE, FIM_PAD]\n",
+ " )\n",
+ " except KeyError:\n",
+ " suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = None, None, None, None\n",
+ " return suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id\n",
+ "\n",
+ "\n",
+ "## Adapted from https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py\n",
+ "def permute(\n",
+ " sample,\n",
+ " np_rng,\n",
+ " suffix_tok_id,\n",
+ " prefix_tok_id,\n",
+ " middle_tok_id,\n",
+ " pad_tok_id,\n",
+ " fim_rate=0.5,\n",
+ " fim_spm_rate=0.5,\n",
+ " truncate_or_pad=False,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Take in a sample (list of tokens) and perform a FIM transformation on it with a probability of fim_rate, using two FIM modes:\n",
+ " PSM and SPM (with a probability of fim_spm_rate).\n",
+ " \"\"\"\n",
+ "\n",
+ " # The if condition will trigger with the probability of fim_rate\n",
+ " # This means FIM transformations will apply to samples with a probability of fim_rate\n",
+ " if np_rng.binomial(1, fim_rate):\n",
+ "\n",
+ " # Split the sample into prefix, middle, and suffix, based on randomly generated indices stored in the boundaries list.\n",
+ " boundaries = list(np_rng.randint(low=0, high=len(sample) + 1, size=2))\n",
+ " boundaries.sort()\n",
+ "\n",
+ " prefix = np.array(sample[: boundaries[0]], dtype=np.int64)\n",
+ " middle = np.array(sample[boundaries[0] : boundaries[1]], dtype=np.int64)\n",
+ " suffix = np.array(sample[boundaries[1] :], dtype=np.int64)\n",
+ "\n",
+ " if truncate_or_pad:\n",
+ " # calculate the new total length of the sample, taking into account tokens indicating prefix, middle, and suffix\n",
+ " new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3\n",
+ " diff = new_length - len(sample)\n",
+ "\n",
+ " # trancate or pad if there's a difference in length between the new length and the original\n",
+ " if diff > 0:\n",
+ " if suffix.shape[0] <= diff:\n",
+ " return sample, np_rng\n",
+ " suffix = suffix[: suffix.shape[0] - diff]\n",
+ " elif diff < 0:\n",
+ " suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)])\n",
+ "\n",
+ " # With the probability of fim_spm_rateapply SPM variant of FIM transformations\n",
+ " # SPM: suffix, prefix, middle\n",
+ " if np_rng.binomial(1, fim_spm_rate):\n",
+ " new_sample = np.concatenate(\n",
+ " [\n",
+ " [prefix_tok_id, suffix_tok_id],\n",
+ " suffix,\n",
+ " [middle_tok_id],\n",
+ " prefix,\n",
+ " middle,\n",
+ " ]\n",
+ " )\n",
+ " # Otherwise, apply the PSM variant of FIM transformations\n",
+ " # PSM: prefix, suffix, middle\n",
+ " else:\n",
+ "\n",
+ " new_sample = np.concatenate(\n",
+ " [\n",
+ " [prefix_tok_id],\n",
+ " prefix,\n",
+ " [suffix_tok_id],\n",
+ " suffix,\n",
+ " [middle_tok_id],\n",
+ " middle,\n",
+ " ]\n",
+ " )\n",
+ " else:\n",
+ " # don't apply FIM transformations\n",
+ " new_sample = sample\n",
+ "\n",
+ " return list(new_sample), np_rng\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AwW5FviD9xBH"
+ },
+ "source": [
+ "Let's define the `ConstantLengthDataset`, an Iterable dataset that will return constant-length chunks of tokens. To do so, we'll read a buffer of text from the original dataset until we hit the size limits and then apply tokenizer to convert the raw text into tokenized inputs. Optionally, we'll perform FIM transformations on some sequences (the proportion of sequences affected is controlled by `fim_rate`).\n",
+ "\n",
+ "Once defined, we can create instances of the `ConstantLengthDataset` from both training and validation data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "AgDW-692wzOl"
+ },
+ "outputs": [],
+ "source": [
+ "from torch.utils.data import IterableDataset\n",
+ "from torch.utils.data.dataloader import DataLoader\n",
+ "import random\n",
+ "\n",
+ "# Create an Iterable dataset that returns constant-length chunks of tokens from a stream of text files.\n",
+ "\n",
+ "class ConstantLengthDataset(IterableDataset):\n",
+ " \"\"\"\n",
+ " Iterable dataset that returns constant length chunks of tokens from stream of text files.\n",
+ " Args:\n",
+ " tokenizer (Tokenizer): The processor used for proccessing the data.\n",
+ " dataset (dataset.Dataset): Dataset with text files.\n",
+ " infinite (bool): If True the iterator is reset after dataset reaches end else stops.\n",
+ " seq_length (int): Length of token sequences to return.\n",
+ " num_of_sequences (int): Number of token sequences to keep in buffer.\n",
+ " chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.\n",
+ " fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.\n",
+ " fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.\n",
+ " seed (int): Seed for random number generator.\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self,\n",
+ " tokenizer,\n",
+ " dataset,\n",
+ " infinite=False,\n",
+ " seq_length=1024,\n",
+ " num_of_sequences=1024,\n",
+ " chars_per_token=3.6,\n",
+ " content_field=\"content\",\n",
+ " fim_rate=0.5,\n",
+ " fim_spm_rate=0.5,\n",
+ " seed=0,\n",
+ " ):\n",
+ " self.tokenizer = tokenizer\n",
+ " self.concat_token_id = tokenizer.eos_token_id\n",
+ " self.dataset = dataset\n",
+ " self.seq_length = seq_length\n",
+ " self.infinite = infinite\n",
+ " self.current_size = 0\n",
+ " self.max_buffer_size = seq_length * chars_per_token * num_of_sequences\n",
+ " self.content_field = content_field\n",
+ " self.fim_rate = fim_rate\n",
+ " self.fim_spm_rate = fim_spm_rate\n",
+ " self.seed = seed\n",
+ "\n",
+ " (\n",
+ " self.suffix_tok_id,\n",
+ " self.prefix_tok_id,\n",
+ " self.middle_tok_id,\n",
+ " self.pad_tok_id,\n",
+ " ) = get_fim_token_ids(self.tokenizer)\n",
+ " if not self.suffix_tok_id and self.fim_rate > 0:\n",
+ " print(\"FIM is not supported by tokenizer, disabling FIM\")\n",
+ " self.fim_rate = 0\n",
+ "\n",
+ " def __iter__(self):\n",
+ " iterator = iter(self.dataset)\n",
+ " more_examples = True\n",
+ " np_rng = np.random.RandomState(seed=self.seed)\n",
+ " while more_examples:\n",
+ " buffer, buffer_len = [], 0\n",
+ " while True:\n",
+ " if buffer_len >= self.max_buffer_size:\n",
+ " break\n",
+ " try:\n",
+ " buffer.append(next(iterator)[self.content_field])\n",
+ " buffer_len += len(buffer[-1])\n",
+ " except StopIteration:\n",
+ " if self.infinite:\n",
+ " iterator = iter(self.dataset)\n",
+ " else:\n",
+ " more_examples = False\n",
+ " break\n",
+ " tokenized_inputs = self.tokenizer(buffer, truncation=False)[\"input_ids\"]\n",
+ " all_token_ids = []\n",
+ "\n",
+ " for tokenized_input in tokenized_inputs:\n",
+ " # optionally do FIM permutations\n",
+ " if self.fim_rate > 0:\n",
+ " tokenized_input, np_rng = permute(\n",
+ " tokenized_input,\n",
+ " np_rng,\n",
+ " self.suffix_tok_id,\n",
+ " self.prefix_tok_id,\n",
+ " self.middle_tok_id,\n",
+ " self.pad_tok_id,\n",
+ " fim_rate=self.fim_rate,\n",
+ " fim_spm_rate=self.fim_spm_rate,\n",
+ " truncate_or_pad=False,\n",
+ " )\n",
+ "\n",
+ " all_token_ids.extend(tokenized_input + [self.concat_token_id])\n",
+ " examples = []\n",
+ " for i in range(0, len(all_token_ids), self.seq_length):\n",
+ " input_ids = all_token_ids[i : i + self.seq_length]\n",
+ " if len(input_ids) == self.seq_length:\n",
+ " examples.append(input_ids)\n",
+ " random.shuffle(examples)\n",
+ " for example in examples:\n",
+ " self.current_size += 1\n",
+ " yield {\n",
+ " \"input_ids\": torch.LongTensor(example),\n",
+ " \"labels\": torch.LongTensor(example),\n",
+ " }\n",
+ "\n",
+ "\n",
+ "train_dataset = ConstantLengthDataset(\n",
+ " tokenizer,\n",
+ " train_data,\n",
+ " infinite=True,\n",
+ " seq_length=SEQ_LENGTH,\n",
+ " chars_per_token=chars_per_token,\n",
+ " content_field=DATA_COLUMN,\n",
+ " fim_rate=FIM_RATE,\n",
+ " fim_spm_rate=FIM_SPM_RATE,\n",
+ " seed=SEED,\n",
+ ")\n",
+ "eval_dataset = ConstantLengthDataset(\n",
+ " tokenizer,\n",
+ " valid_data,\n",
+ " infinite=False,\n",
+ " seq_length=SEQ_LENGTH,\n",
+ " chars_per_token=chars_per_token,\n",
+ " content_field=DATA_COLUMN,\n",
+ " fim_rate=FIM_RATE,\n",
+ " fim_spm_rate=FIM_SPM_RATE,\n",
+ " seed=SEED,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rxev1sk6tRW9"
+ },
+ "source": [
+ "## Prepare the model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UCtWV-U42Eq_"
+ },
+ "source": [
+ "Now that the data is prepared, it's time to load the model! We're going to load the quantized version of the model.\n",
+ "\n",
+ "This will allow us to reduce memory usage, as quantization represents data with fewer bits. We'll use the `bitsandbytes` library to quantize the model, as it has a nice integration with `transformers`. All we need to do is define a `bitsandbytes` config, and then use it when loading the model.\n",
+ "\n",
+ "There are different variants of 4bit quantization, but generally, we recommend using NF4 quantization for better performance (`bnb_4bit_quant_type=\"nf4\"`).\n",
+ "\n",
+ "The `bnb_4bit_use_double_quant` option adds a second quantization after the first one to save an additional 0.4 bits per parameter.\n",
+ "\n",
+ "To learn more about quantization, check out the [\"Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA\" blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).\n",
+ "\n",
+ "Once defined, pass the config to the `from_pretrained` method to load the quantized version of the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XuwoX6U2DUvK"
+ },
+ "outputs": [],
+ "source": [
+ "from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training\n",
+ "from peft.tuners.lora import LoraLayer\n",
+ "\n",
+ "load_in_8bit = False\n",
+ "\n",
+ "# 4-bit quantization\n",
+ "compute_dtype = getattr(torch, BNB_4BIT_COMPUTE_DTYPE)\n",
+ "\n",
+ "bnb_config = BitsAndBytesConfig(\n",
+ " load_in_4bit=True,\n",
+ " bnb_4bit_quant_type=\"nf4\",\n",
+ " bnb_4bit_compute_dtype=compute_dtype,\n",
+ " bnb_4bit_use_double_quant=USE_NESTED_QUANT,\n",
+ ")\n",
+ "\n",
+ "device_map = {\"\": 0}\n",
+ "\n",
+ "model = AutoModelForCausalLM.from_pretrained(\n",
+ " MODEL,\n",
+ " load_in_8bit=load_in_8bit,\n",
+ " quantization_config=bnb_config,\n",
+ " device_map=device_map,\n",
+ " use_cache=False, # We will be using gradient checkpointing\n",
+ " trust_remote_code=True,\n",
+ " use_flash_attention_2=True,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bO9e2FV8D8ZF"
+ },
+ "source": [
+ "When using a quantized model for training, you need to call the `prepare_model_for_kbit_training()` function to preprocess the quantized model for training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Qb_eB4xzEDBk"
+ },
+ "outputs": [],
+ "source": [
+ "model = prepare_model_for_kbit_training(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lmnLjPZpDVtg"
+ },
+ "source": [
+ "Now that the quantized model is ready, we can set up a LoRA configuration. LoRA makes fine-tuning more efficient by drastically reducing the number of trainable parameters.\n",
+ "\n",
+ "To train a model using LoRA technique, we need to wrap the base model as a `PeftModel`. This involves definign LoRA configuration with `LoraConfig`, and wrapping the original model with `get_peft_model()` using the `LoraConfig`.\n",
+ "\n",
+ "To learn more about LoRA and its parameters, refer to [PEFT documentation](https://huggingface.co/docs/peft/conceptual_guides/lora)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "_pAUU2FR2Gey",
+ "outputId": "63328c2b-e693-49b1-ce0a-3ca8722f852a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "trainable params: 5,554,176 || all params: 1,142,761,472 || trainable%: 0.4860310866343243\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Set up lora\n",
+ "peft_config = LoraConfig(\n",
+ " lora_alpha=LORA_ALPHA,\n",
+ " lora_dropout=LORA_DROPOUT,\n",
+ " r=LORA_R,\n",
+ " bias=\"none\",\n",
+ " task_type=\"CAUSAL_LM\",\n",
+ " target_modules=LORA_TARGET_MODULES.split(\",\"),\n",
+ ")\n",
+ "\n",
+ "model = get_peft_model(model, peft_config)\n",
+ "model.print_trainable_parameters()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tHe7AElXzXVV"
+ },
+ "source": [
+ "As you can see, by applying LoRA technique we will now need to train less than 1% of the parameters."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T_CqVydc40IM"
+ },
+ "source": [
+ "## Train the model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Q_iN2khjrbD3"
+ },
+ "source": [
+ "Now that we have prepared the data, and optimized the model, we are ready to bring everything together to start the training.\n",
+ "\n",
+ "To instantiate a `Trainer`, you need to define the training configuration. The most important is the `TrainingArguments`, which is a class that contains all the attributes to configure the training.\n",
+ "\n",
+ "These are similar to any other kind of model training you may run, so we won't go into detail here."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "65QHS8l1tKQe"
+ },
+ "outputs": [],
+ "source": [
+ "train_data.start_iteration = 0\n",
+ "\n",
+ "\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=f\"Your_HF_username/{OUTPUT_DIR}\",\n",
+ " dataloader_drop_last=True,\n",
+ " evaluation_strategy=\"steps\",\n",
+ " save_strategy=\"steps\",\n",
+ " max_steps=MAX_STEPS,\n",
+ " eval_steps=EVAL_FREQ,\n",
+ " save_steps=SAVE_FREQ,\n",
+ " logging_steps=LOG_FREQ,\n",
+ " per_device_train_batch_size=BATCH_SIZE,\n",
+ " per_device_eval_batch_size=BATCH_SIZE,\n",
+ " learning_rate=LR,\n",
+ " lr_scheduler_type=LR_SCHEDULER_TYPE,\n",
+ " warmup_steps=NUM_WARMUP_STEPS,\n",
+ " gradient_accumulation_steps=GR_ACC_STEPS,\n",
+ " gradient_checkpointing=True,\n",
+ " fp16=FP16,\n",
+ " bf16=BF16,\n",
+ " weight_decay=WEIGHT_DECAY,\n",
+ " push_to_hub=True,\n",
+ " include_tokens_per_second=True,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kB_fLRex09ut"
+ },
+ "source": [
+ "As a final step, instantiate the `Trainer` and call the `train` method. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "rS3nVwhUC69O",
+ "outputId": "61a5bdb2-b7d0-4aed-8290-4bf20c2ccd38"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " | Step | \n", + "Training Loss | \n", + "Validation Loss | \n", + "
|---|---|---|
| 100 | \n", + "5.524600 | \n", + "7.456872 | \n", + "
| 200 | \n", + "5.617800 | \n", + "7.262190 | \n", + "
| 300 | \n", + "5.129100 | \n", + "6.410039 | \n", + "
| 400 | \n", + "5.052200 | \n", + "6.306774 | \n", + "
| 500 | \n", + "5.202900 | \n", + "6.117062 | \n", + "
| 600 | \n", + "4.654100 | \n", + "6.018349 | \n", + "
| 700 | \n", + "5.100200 | \n", + "6.000355 | \n", + "
| 800 | \n", + "5.049800 | \n", + "5.889457 | \n", + "
| 900 | \n", + "4.541200 | \n", + "5.813823 | \n", + "
| 1000 | \n", + "5.000700 | \n", + "5.834208 | \n", + "
| 1100 | \n", + "5.026500 | \n", + "5.781939 | \n", + "
| 1200 | \n", + "4.411800 | \n", + "5.720596 | \n", + "
| 1300 | \n", + "4.782500 | \n", + "5.736376 | \n", + "
| 1400 | \n", + "4.980200 | \n", + "5.712276 | \n", + "
| 1500 | \n", + "4.368700 | \n", + "5.689637 | \n", + "
| 1600 | \n", + "4.884700 | \n", + "5.675920 | \n", + "
| 1700 | \n", + "4.914400 | \n", + "5.662421 | \n", + "
| 1800 | \n", + "4.248700 | \n", + "5.660122 | \n", + "
| 1900 | \n", + "4.798400 | \n", + "5.664026 | \n", + "
| 2000 | \n", + "4.704200 | \n", + "5.655665 | \n", + "
"
+ ],
+ "text/plain": [
+ "