Add Chapter 3 and 6 folders

Adds the full Chapter 3 and Chapter 6 folder contents from the GitHub notebooks repository.

The folders include notebooks, JSON loss files, generated plots, and supporting figure source files.

Chapter 3/chapter_3_batched_loss.json

@@ -0,0 +1 @@
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1.5859, 2.3125, 2.4844, 0.7812, 1.4766, 0.7734, 1.6484, 0.9062, 1.6328, 1.5469, 0.5898, 1.7422, 1.0312, 1.2812, 1.5703, 2.3906, 1.3672, 1.7266]

Chapter 3/create_fig_3_1.py

@@ -0,0 +1,220 @@
+"""
+iMessage-style visualization for VLM dataset samples.
+User = right-aligned blue bubbles with white text.
+Assistant = left-aligned light-gray bubbles with dark text.
+Image inlined after the first user message (right-aligned).
+Max 2 turns. Generates 10 images.
+"""
+
+import sys, os
+from datasets import load_dataset
+from PIL import Image, ImageDraw, ImageFont
+
+
+# ── Fonts ────────────────────────────────────────────────────────────
+
+def get_font(size):
+    for name in [
+        "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
+        "DejaVuSans.ttf",
+        "Arial.ttf",
+        "/System/Library/Fonts/Helvetica.ttc",
+    ]:
+        try:
+            return ImageFont.truetype(name, size)
+        except (OSError, IOError):
+            continue
+    return ImageFont.load_default()
+
+
+FONT = get_font(20)
+WIDTH = 600
+PAD = 16
+BUBBLE_PAD_H = 16
+BUBBLE_PAD_V = 12
+LINE_HEIGHT = 28
+BUBBLE_MAX_W = int(WIDTH * 0.72)
+BUBBLE_RADIUS = 18
+GAP = 10
+BG_COLOR = "#F2F2F7"
+
+USER_BG = "#007AFF"
+USER_TEXT = "#FFFFFF"
+ASST_BG = "#E9E9EB"
+ASST_TEXT = "#000000"
+
+
+# ── Data helpers ─────────────────────────────────────────────────────
+
+def extract_sample_image(sample):
+    if "image" in sample and sample["image"] is not None:
+        return sample["image"]
+    if "images" in sample and sample["images"]:
+        return sample["images"][0]
+    raise KeyError("No image found in sample.")
+
+
+def normalize_conversations(sample, max_turns=2):
+    raw = sample.get("texts", [])
+    normalized = []
+    for msg in raw:
+        if not isinstance(msg, dict):
+            continue
+        if "role" in msg and "content" in msg:
+            normalized.append({"role": msg["role"], "content": msg["content"]})
+            continue
+        if "user" in msg:
+            normalized.append({"role": "user", "content": str(msg["user"])})
+        if "assistant" in msg:
+            normalized.append({"role": "assistant", "content": str(msg["assistant"])})
+
+    turns, current = [], []
+    for msg in normalized:
+        current.append(msg)
+        if msg["role"] == "assistant":
+            turns.append(current)
+            current = []
+    if current:
+        turns.append(current)
+    turns = turns[:max_turns]
+    return [msg for turn in turns for msg in turn]
+
+
+# ── Drawing helpers ──────────────────────────────────────────────────
+
+def wrap_text(text, font, max_width, draw):
+    lines = []
+    for paragraph in text.split("\n"):
+        if not paragraph.strip():
+            lines.append("")
+            continue
+        words = paragraph.split()
+        cur = ""
+        for w in words:
+            test = f"{cur} {w}".strip()
+            bbox = draw.textbbox((0, 0), test, font=font)
+            if bbox[2] - bbox[0] > max_width:
+                if cur:
+                    lines.append(cur)
+                cur = w
+            else:
+                cur = test
+        if cur:
+            lines.append(cur)
+    return lines
+
+
+def measure_bubble(lines):
+    tmp = Image.new("RGB", (1, 1))
+    d = ImageDraw.Draw(tmp)
+    text_w = 0
+    for line in lines:
+        bbox = d.textbbox((0, 0), line, font=FONT)
+        text_w = max(text_w, bbox[2] - bbox[0])
+    text_h = len(lines) * LINE_HEIGHT
+    bw = text_w + 2 * BUBBLE_PAD_H
+    bh = text_h + 2 * BUBBLE_PAD_V
+    return bw, bh
+
+
+def round_image_corners(img, radius):
+    w, h = img.size
+    mask = Image.new("L", (w, h), 0)
+    d = ImageDraw.Draw(mask)
+    d.rounded_rectangle([0, 0, w, h], radius=radius, fill=255)
+    result = img.copy().convert("RGBA")
+    result.putalpha(mask)
+    return result
+
+
+# ── Main renderer ────────────────────────────────────────────────────
+
+def render_imessage(sample_image, conversations, filename="imessage_style.png"):
+    tmp = Image.new("RGB", (WIDTH, 100))
+    tmp_draw = ImageDraw.Draw(tmp)
+    inner_text_w = BUBBLE_MAX_W - 2 * BUBBLE_PAD_H
+
+    items = []
+    image_inserted = False
+
+    for msg in conversations:
+        role = msg["role"]
+        text = msg["content"]
+
+        text = text.strip()
+        lines = wrap_text(text, FONT, inner_text_w, tmp_draw)
+        bw, bh = measure_bubble(lines)
+        items.append(("bubble", (role, lines, bw, bh), bh + GAP))
+
+        # Insert image right after the first user message
+        if role == "user" and not image_inserted:
+            img_w, img_h = sample_image.size
+            max_img_w = BUBBLE_MAX_W
+            max_img_h = 320
+            scale = min(max_img_w / img_w, max_img_h / img_h, 1.0)
+            new_w = int(img_w * scale)
+            new_h = int(img_h * scale)
+            scaled = sample_image.resize((new_w, new_h), Image.LANCZOS)
+            items.append(("image", scaled, new_h + 8))
+            image_inserted = True
+
+    total_h = PAD
+    for _, _, h in items:
+        total_h += h + GAP
+    total_h += PAD
+
+    canvas = Image.new("RGB", (WIDTH, total_h), BG_COLOR)
+    draw = ImageDraw.Draw(canvas)
+    y = PAD
+
+    for item_type, data, h in items:
+        if item_type == "image":
+            scaled = data
+            sw, sh = scaled.size
+            ix = WIDTH - PAD - sw
+            rounded = round_image_corners(scaled, BUBBLE_RADIUS)
+            canvas.paste(rounded, (ix, y), rounded)
+            y += sh + GAP + 4
+
+        elif item_type == "bubble":
+            role, lines, bw, bh = data
+            is_user = role == "user"
+            bg = USER_BG if is_user else ASST_BG
+            text_color = USER_TEXT if is_user else ASST_TEXT
+            bx = (WIDTH - PAD - bw) if is_user else PAD
+
+            draw.rounded_rectangle(
+                [bx, y, bx + bw, y + bh],
+                radius=BUBBLE_RADIUS,
+                fill=bg,
+            )
+
+            ty = y + BUBBLE_PAD_V
+            for line in lines:
+                draw.text((bx + BUBBLE_PAD_H, ty), line, fill=text_color, font=FONT)
+                ty += LINE_HEIGHT
+            y += bh + GAP
+
+    canvas.save(filename)
+    print(f"Saved {filename} ({WIDTH}x{total_h})")
+    return canvas
+
+
+# ── Generate 10 images ───────────────────────────────────────────────
+
+dataset = load_dataset("HuggingFaceM4/FineVisionMax", split="train", streaming=True)
+
+count = 0
+for sample in dataset:
+    if count >= 50:
+        break
+    try:
+        sample_image = extract_sample_image(sample).convert("RGB")
+        conversations = normalize_conversations(sample, max_turns=2)
+    except (KeyError, ValueError):
+        continue
+    render_imessage(sample_image, conversations, filename=f"imessage_style_{count:02d}.png")
+    count += 1
+
+print(f"Done — saved {count} images.")
+os._exit(0)

Chapter 3/create_fig_3_2.tsx

@@ -0,0 +1,221 @@
+import { useState, useRef, useCallback } from "react";
+
+const TOKENS = ["the", "a", "cat", "sun", "is", "big", "ran", "to", "red", "it"];
+const CORRECT = 3;
+const lowLoss = [0.04, 0.02, 0.06, 0.52, 0.04, 0.06, 0.03, 0.05, 0.12, 0.06];
+const highLoss = [0.05, 0.03, 0.22, 0.04, 0.06, 0.28, 0.08, 0.05, 0.12, 0.07];
+const ce = (dist, c) => -Math.log(Math.max(dist[c], 1e-9));
+
+const SCALE = 2;
+
+function drawChart(ctx, ox, oy, title, dist, subtitle, W, H) {
+  const PAD = { top: 100, right: 20, bottom: 64, left: 46 };
+  const pw = W - PAD.left - PAD.right;
+  const ph = H - PAD.top - PAD.bottom;
+  const n = dist.length;
+  const gap = 5;
+  const bw = (pw - gap * (n - 1)) / n;
+  const maxP = 0.55;
+  const loss = ce(dist, CORRECT);
+  const isLow = loss < 1;
+
+  ctx.save();
+  ctx.translate(ox, oy);
+
+  // Title
+  ctx.font = `bold ${15 * SCALE}px Inter, -apple-system, system-ui, sans-serif`;
+  ctx.fillStyle = "#222";
+  ctx.textAlign = "center";
+  ctx.fillText(title, W / 2, 24 * SCALE);
+
+  // Y grid + labels
+  ctx.font = `${10 * SCALE}px Inter, -apple-system, system-ui, sans-serif`;
+  ctx.textAlign = "right";
+  for (const t of [0, 0.1, 0.2, 0.3, 0.4, 0.5]) {
+    const y = PAD.top + ph * (1 - t / maxP);
+    ctx.strokeStyle = "#e0e0e0";
+    ctx.lineWidth = 1;
+    ctx.beginPath(); ctx.moveTo(PAD.left, y); ctx.lineTo(W - PAD.right, y); ctx.stroke();
+    ctx.fillStyle = "#999";
+    ctx.fillText(t === 0 ? "0" : `${(t * 100).toFixed(0)}%`, PAD.left - 8, y + 4 * SCALE);
+  }
+
+  // Baseline
+  ctx.strokeStyle = "#ccc";
+  ctx.lineWidth = 1.5;
+  ctx.beginPath(); ctx.moveTo(PAD.left, PAD.top + ph); ctx.lineTo(W - PAD.right, PAD.top + ph); ctx.stroke();
+
+  // Bars
+  for (let i = 0; i < n; i++) {
+    const p = dist[i];
+    const x = PAD.left + i * (bw + gap);
+    const barH = (p / maxP) * ph;
+    const y = PAD.top + ph - barH;
+    const isC = i === CORRECT;
+
+    const r = 4 * SCALE;
+    const bx = x, by = y, bww = bw, bhh = barH;
+
+    ctx.fillStyle = isC ? (isLow ? "#22a861" : "#d63031") : "#b0b5bc";
+    ctx.globalAlpha = isC ? 1 : 0.5;
+    ctx.beginPath();
+    ctx.moveTo(bx + r, by);
+    ctx.lineTo(bx + bww - r, by);
+    ctx.quadraticCurveTo(bx + bww, by, bx + bww, by + r);
+    ctx.lineTo(bx + bww, by + bhh);
+    ctx.lineTo(bx, by + bhh);
+    ctx.lineTo(bx, by + r);
+    ctx.quadraticCurveTo(bx, by, bx + r, by);
+    ctx.closePath();
+    ctx.fill();
+    ctx.globalAlpha = 1;
+
+    // Percentage on correct bar
+    if (isC) {
+      ctx.font = `bold ${10 * SCALE}px Inter, sans-serif`;
+      ctx.fillStyle = "#333";
+      ctx.textAlign = "center";
+      ctx.fillText(`${(p * 100).toFixed(0)}%`, x + bw / 2, y - 14 * SCALE);
+    }
+
+    // Token label
+    ctx.font = `${isC ? "bold " : ""}${10.5 * SCALE}px 'SF Mono', 'Fira Code', monospace`;
+    ctx.fillStyle = isC ? "#222" : "#999";
+    ctx.textAlign = "center";
+    ctx.fillText(TOKENS[i], x + bw / 2, PAD.top + ph + 18 * SCALE);
+
+    // "correct" marker
+    if (isC) {
+      ctx.font = `${8 * SCALE}px Inter, sans-serif`;
+      ctx.fillStyle = "#777";
+      ctx.letterSpacing = "1px";
+      ctx.fillText("correct", x + bw / 2, PAD.top + ph + 32 * SCALE);
+
+      // Arrow
+      const ax = x + bw / 2;
+      const ay1 = PAD.top + ph + 36 * SCALE;
+      const ay2 = ay1 + 7 * SCALE;
+      ctx.strokeStyle = "#aaa";
+      ctx.lineWidth = 1.5;
+      ctx.beginPath(); ctx.moveTo(ax, ay1); ctx.lineTo(ax, ay2); ctx.stroke();
+      ctx.fillStyle = "#aaa";
+      ctx.beginPath();
+      ctx.moveTo(ax - 4 * SCALE, ay2);
+      ctx.lineTo(ax + 4 * SCALE, ay2);
+      ctx.lineTo(ax, ay2 + 5 * SCALE);
+      ctx.closePath();
+      ctx.fill();
+    }
+  }
+
+  // Pill
+  const pillW = 200 * SCALE;
+  const pillH = 30 * SCALE;
+  const pillX = (W - pillW) / 2;
+  const pillY = H - 10 * SCALE;
+
+  ctx.fillStyle = isLow ? "#dff5e8" : "#fce4e4";
+  ctx.beginPath();
+  const pr = pillH / 2;
+  ctx.moveTo(pillX + pr, pillY);
+  ctx.lineTo(pillX + pillW - pr, pillY);
+  ctx.quadraticCurveTo(pillX + pillW, pillY, pillX + pillW, pillY + pr);
+  ctx.quadraticCurveTo(pillX + pillW, pillY + pillH, pillX + pillW - pr, pillY + pillH);
+  ctx.lineTo(pillX + pr, pillY + pillH);
+  ctx.quadraticCurveTo(pillX, pillY + pillH, pillX, pillY + pr);
+  ctx.quadraticCurveTo(pillX, pillY, pillX + pr, pillY);
+  ctx.closePath();
+  ctx.fill();
+
+  ctx.font = `bold ${11 * SCALE}px Inter, sans-serif`;
+  ctx.fillStyle = isLow ? "#1e8449" : "#b71c1c";
+  ctx.textAlign = "center";
+  ctx.fillText(`${subtitle}  ·  loss = ${loss.toFixed(2)}`, W / 2, pillY + pillH / 2 + 4 * SCALE);
+
+  ctx.restore();
+}
+
+export default function App() {
+  const canvasRef = useRef(null);
+  const [rendered, setRendered] = useState(false);
+
+  const chartW = 320 * SCALE;
+  const chartH = 280 * SCALE;
+  const totalGap = 50 * SCALE;
+  const padX = 30 * SCALE;
+  const padTop = 50 * SCALE;
+  const padBot = 30 * SCALE;
+  const totalW = padX * 2 + chartW * 2 + totalGap;
+  const totalH = padTop + chartH + padBot;
+
+  const draw = useCallback((canvas) => {
+    if (!canvas || rendered) return;
+    canvasRef.current = canvas;
+    const ctx = canvas.getContext("2d");
+
+    // White background
+    ctx.fillStyle = "#ffffff";
+    ctx.fillRect(0, 0, totalW, totalH);
+
+    // Header
+    ctx.font = `${13 * SCALE}px Inter, -apple-system, system-ui, sans-serif`;
+    ctx.fillStyle = "#222";
+    ctx.textAlign = "center";
+    ctx.fillText("Predicted probability distribution over vocabulary", totalW / 2, 22 * SCALE);
+
+    ctx.font = `${11 * SCALE}px 'SF Mono', 'Fira Code', monospace`;
+    ctx.fillStyle = "#222";
+    ctx.fillText('The model predicts the next token after: "the __ rises"', totalW / 2, 38 * SCALE);
+
+    drawChart(ctx, padX, padTop, "Low surprise", lowLoss, "Confident & correct", chartW, chartH);
+    drawChart(ctx, padX + chartW + totalGap, padTop, "High surprise", highLoss, "Spread & wrong", chartW, chartH);
+
+    setRendered(true);
+  }, [rendered, totalW, totalH]);
+
+  const download = () => {
+    const c = canvasRef.current;
+    if (!c) return;
+    const link = document.createElement("a");
+    link.download = "cross-entropy-loss.png";
+    link.href = c.toDataURL("image/png");
+    link.click();
+  };
+
+  return (
+    <div style={{
+      display: "flex", flexDirection: "column", alignItems: "center", justifyContent: "center",
+      minHeight: "100vh", background: "#f5f5f5",
+      fontFamily: "'Inter', -apple-system, system-ui, sans-serif",
+      padding: 24, gap: 20,
+    }}>
+      <canvas
+        ref={draw}
+        width={totalW}
+        height={totalH}
+        style={{
+          width: totalW / SCALE,
+          height: totalH / SCALE,
+          borderRadius: 8,
+          boxShadow: "0 2px 16px rgba(0,0,0,0.07)",
+        }}
+      />
+      <button
+        onClick={download}
+        style={{
+          padding: "10px 28px",
+          borderRadius: 8,
+          border: "none",
+          background: "#222",
+          color: "#fff",
+          fontSize: 14,
+          fontWeight: 600,
+          cursor: "pointer",
+          letterSpacing: 0.3,
+        }}
+      >
+        Download PNG
+      </button>
+    </div>
+  );
+}

Chapter 3/minimal_vlm_training.ipynb

@@ -0,0 +1,2307 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 223,
+     "referenced_widgets": [
+      "6af6b0d5fb5942f0874062514fecdac9",
+      "aaceb8bb8d144e62a7fc19dde30e981b",
+      "9a9026e81b974c7ba30f92d2e7d5f16f",
+      "81bebd074665482b9af8e6d39b6b171a",
+      "c1891461614e45189d47fa3948da6808",
+      "db773254517a428684eb679771212f2c",
+      "5cb1c4db303b4b6aaaabc7cf88e975d5",
+      "edcb5218b9bc4e9d9da375eb93de2829",
+      "59c2f3d1128a45ebbc2f1405ae42751a",
+      "8de705db750048ff890e74afcf97f964",
+      "032dcdea26234490a141913d7e361a15",
+      "f05122bb2187477ab3f5181938c86a21",
+      "e74fce8c74c548d285e41413679a080d",
+      "3686ac8683c745ee8bc5638d1e962c54",
+      "9049575e797c4bffa86129fd9394f8fd",
+      "64363326d62641f3aee10f317d92140e",
+      "6baf2c3128d1425c8f979ed703b0fc6b",
+      "2f69578d14bb4ad6967e00931f611f63",
+      "b391ef77f8ed4d4f9ccd41ace6a80075",
+      "7f01a3c81bb34621aa7f2744722de6bf",
+      "6d97eea8e68948698db477acaa7ff4f6",
+      "dbbd386fb13b4dbcae40301c1bbbbf90"
+     ]
+    },
+    "id": "DzFaTkPrpVse",
+    "outputId": "90b58e6c-046d-4982-c94e-8372cc44d6f5"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da8eb18c731c47e59a4aeff1800b16a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "678bdcb6f77a42e8b5e50f86267094fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Resolving data files:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1506x1102 at 0xEC954E9D5600>], 'texts': [{'user': 'What is the primary key of the Equipment entity?', 'assistant': 'The primary key of the Equipment entity is EquipmentID, which uniquely identifies each piece of equipment in the diagram.\\nAnswer: EquipmentID'}, {'user': 'What is the relationship between Equipment and Maintenance Record?', 'assistant': 'The relationship between Equipment and Maintenance Record is labeled as \"Maintained By\", indicating that one piece of equipment can have multiple maintenance records associated with it.\\nAnswer: Maintained By'}, {'user': 'How many relationships does the Supplier entity have?', 'assistant': 'The Supplier entity has two relationships: \"Supplied By\" with Equipment and \"Delivered By\" with Logistics Company, indicating it serves as a midpoint in both flows.\\nAnswer: 2'}, {'user': 'What is the cardinality of the relationship between Equipment and Supplier?', 'assistant': 'The cardinality of the relationship between Equipment and Supplier is M:1, which means many pieces of equipment can be supplied by one supplier.\\nAnswer: M:1'}, {'user': 'Which entity is connected to Logistics Company through the Delivered By relationship?', 'assistant': 'The entity connected to Logistics Company through the Delivered By relationship is Supplier, indicating that the supplier relies on the logistics company for delivering equipment.\\nAnswer: Supplier'}, {'user': 'How many attributes does the Maintenance Record entity have?', 'assistant': 'The Maintenance Record entity has five attributes: RecordID, Date, Description, ServiceCost, and ServiceProvider, detailing the specifics of each maintenance entry.\\nAnswer: 5'}, {'user': 'What type of diagram is represented in this illustration?', 'assistant': 'This illustration represents an Entity Relationship Diagram, which shows entities, their attributes, and how they are related to one another within a system, particularly for equipment maintenance and service records.\\nAnswer: Entity Relationship Diagram'}, {'user': 'Which attribute of Supplier is related to contact information?', 'assistant': 'The contact information attribute related to Supplier is ContactNumber which provides the phone number for communication.\\nAnswer: ContactNumber'}, {'user': 'Who maintains the Equipment records?', 'assistant': 'The Equipment records are maintained through the relationship labeled \"Maintained By\" with the Maintenance Record, indicating a direct link between equipment details and their records of maintenance.\\nAnswer: Maintenance Record'}], 'source': 'CoSyn-400k-diagram', 'relevance_ratings': [5, 5, 5, 5, 5, 5, 5, 5, 2], 'relevance_min': 2, 'visual_dependency_ratings': [1, 2, 4, 4, 4, 4, 5, 2, 2], 'visual_dependency_min': 1, 'image_correspondence_ratings': [3, 5, 4, 4, 5, 5, 5, 3, 5], 'image_correspondence_min': 3, 'formatting_ratings': [4, 4, 4, 4, 4, 5, 4, 4, 4], 'formatting_min': 4}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"HuggingFaceM4/FineVisionMax\", split=\"train\", streaming=True)\n",
+    "print(next(iter(dataset)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "AU4DEPrKpeAX"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from transformers import AutoModel, AutoProcessor, AutoTokenizer, AutoModelForCausalLM\n",
+    "\n",
+    "class VisionLanguageModel(nn.Module):\n",
+    "    def __init__(self, vision_encoder_ckpt, language_model_ckpt, modality_input_dim=768, modality_output_dim=576):\n",
+    "        super().__init__()\n",
+    "        self.vision_encoder = AutoModel.from_pretrained(vision_encoder_ckpt).vision_model  # only take vision backbone\n",
+    "        self.vision_processor = AutoProcessor.from_pretrained(vision_encoder_ckpt)\n",
+    "        self.modality_projector = nn.Linear(modality_input_dim, modality_output_dim, bias=False)\n",
+    "        self.tokenizer = AutoTokenizer.from_pretrained(language_model_ckpt)\n",
+    "        self.llm = AutoModelForCausalLM.from_pretrained(language_model_ckpt)\n",
+    "\n",
+    "    def forward(self, text, image, labels=None):\n",
+    "        processed_img = self.vision_processor(images=[image], return_tensors=\"pt\").to(self.llm.device)\n",
+    "        image_embd = self.vision_encoder(**processed_img).last_hidden_state\n",
+    "        image_embd = self.modality_projector(image_embd).to(dtype=self.llm.dtype)  # match LLM dtype\n",
+    "\n",
+    "        input_ids = self.tokenizer(text, return_tensors=\"pt\").input_ids.to(self.llm.device)\n",
+    "        token_embd = self.llm.model.embed_tokens(input_ids)\n",
+    "\n",
+    "        combined_embd = torch.cat((image_embd, token_embd), dim=1) # Concatenate image embeddings to token embeddings\n",
+    "\n",
+    "        logits = self.llm(inputs_embeds=combined_embd).logits\n",
+    "\n",
+    "        shift_logits = logits[:, image_embd.size(1):-1, :].contiguous()\n",
+    "        shift_labels = input_ids[:, 1:].contiguous()\n",
+    "\n",
+    "        loss = nn.functional.cross_entropy(shift_logits.reshape(-1, shift_logits.size(-1)), shift_labels.reshape(-1))\n",
+    "\n",
+    "        return logits, loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 81,
+     "referenced_widgets": [
+      "059fc34bbe9c40a0a81c5640003ccc47",
+      "3cc6112c0037496a9337382c1d16a3d6",
+      "f4fcf974faad42a08fb6863763eedb81",
+      "d6cc3547f86c4629a8e8e53592167b43",
+      "c04849b1932040188a760a2ebabc91ec",
+      "5ae3364706d04df0afcd0e941c09a01a",
+      "f09cbd8806bc41808b5594cfb16c1a83",
+      "18111ed761e146698786fb4a5d0c7051",
+      "1e523935a83d434c976f10797cbe2286",
+      "9db3a6aae167458b8e3c6b785c25f225",
+      "cd7b84b3c61e49d093df411ccfbc3bb0",
+      "8d851590df7c4cf3a9fc293d03befa40",
+      "aa9c7dbcf7b04f339fce5bee0d24c98d",
+      "fba6cbf72ad54e748031d11669b3e31a",
+      "296ba3ca9f34496ba8edfeee4c76a5d2",
+      "cc1e631bd9154962bcb42eb6fc234303",
+      "a65cbec1df084615915186797051a9e3",
+      "4d95c6fe1c434425b1368f07952e250a",
+      "891d641ad7634c9aa5186589f2aba958",
+      "aa902a918c79418ea4b4516fb5673fa4",
+      "d8ee4430ef8c47bc96d6d7ae2060f9ad",
+      "667ff0bc56f94ab582252968d3dca89b"
+     ]
+    },
+    "id": "V3lGxumXq9VD",
+    "outputId": "ff0b55d3-b0a3-45ce-f377-8c460684af8e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b811e26e7524bb4baba26f713332410",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/408 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5ca88c6eb384283a7a5d13ed1474514",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/272 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vision_encoder_ckpt = \"google/siglip2-base-patch16-256\"\n",
+    "language_model_ckpt = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
+    "\n",
+    "vlm = VisionLanguageModel(vision_encoder_ckpt, language_model_ckpt).to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lux-u_uCrVBJ",
+    "outputId": "79411cab-c7f2-4d67-fceb-e9b14c9611e2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 256, 576])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[[15.0000,  6.1875,  9.3750,  ...,  6.9375, 10.7500,  2.0469],\n",
+       "          [22.8750, 11.6875, 14.8750,  ..., 17.8750, 19.1250, 16.8750],\n",
+       "          [20.7500,  5.8125,  7.5312,  ..., 12.5000, 15.8750, 10.5000],\n",
+       "          ...,\n",
+       "          [13.9375,  2.1250,  3.8906,  ..., 10.4375, 12.1250,  8.1250],\n",
+       "          [13.6250,  3.0625,  6.1875,  ...,  8.0625, 12.3750,  5.6562],\n",
+       "          [14.3125,  5.4062, 10.1250,  ...,  9.1250, 12.5625,  6.8750]]],\n",
+       "        device='cuda:0', dtype=torch.bfloat16, grad_fn=<UnsafeViewBackward0>),\n",
+       " tensor(4.8125, device='cuda:0', dtype=torch.bfloat16,\n",
+       "        grad_fn=<NllLossBackward0>))"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers.image_utils import load_image\n",
+    "\n",
+    "image = load_image(\"https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg\")\n",
+    "text = \"What does this say?\"\n",
+    "vlm(text, image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "jAMaabaNr7JS"
+   },
+   "outputs": [],
+   "source": [
+    "sample = next(iter(dataset))\n",
+    "\n",
+    "def apply_chat_template_to_sample(sample):\n",
+    "  messages_list = []\n",
+    "  for data_dict in sample['texts']:\n",
+    "      messages_list.append({'role': 'user', 'content': data_dict['user']})\n",
+    "      messages_list.append({'role': 'assistant', 'content': data_dict['assistant']})\n",
+    "  text = vlm.tokenizer.apply_chat_template(messages_list, tokenize=False)\n",
+    "  return text\n",
+    "\n",
+    "text = apply_chat_template_to_sample(sample)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 141
+    },
+    "id": "280WsrwW1Ets",
+    "outputId": "2024d829-7240-4861-ccd9-4ce04dbd390d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1506x1102>],\n",
+       " 'texts': [{'user': 'What is the primary key of the Equipment entity?',\n",
+       "   'assistant': 'The primary key of the Equipment entity is EquipmentID, which uniquely identifies each piece of equipment in the diagram.\\nAnswer: EquipmentID'},\n",
+       "  {'user': 'What is the relationship between Equipment and Maintenance Record?',\n",
+       "   'assistant': 'The relationship between Equipment and Maintenance Record is labeled as \"Maintained By\", indicating that one piece of equipment can have multiple maintenance records associated with it.\\nAnswer: Maintained By'},\n",
+       "  {'user': 'How many relationships does the Supplier entity have?',\n",
+       "   'assistant': 'The Supplier entity has two relationships: \"Supplied By\" with Equipment and \"Delivered By\" with Logistics Company, indicating it serves as a midpoint in both flows.\\nAnswer: 2'},\n",
+       "  {'user': 'What is the cardinality of the relationship between Equipment and Supplier?',\n",
+       "   'assistant': 'The cardinality of the relationship between Equipment and Supplier is M:1, which means many pieces of equipment can be supplied by one supplier.\\nAnswer: M:1'},\n",
+       "  {'user': 'Which entity is connected to Logistics Company through the Delivered By relationship?',\n",
+       "   'assistant': 'The entity connected to Logistics Company through the Delivered By relationship is Supplier, indicating that the supplier relies on the logistics company for delivering equipment.\\nAnswer: Supplier'},\n",
+       "  {'user': 'How many attributes does the Maintenance Record entity have?',\n",
+       "   'assistant': 'The Maintenance Record entity has five attributes: RecordID, Date, Description, ServiceCost, and ServiceProvider, detailing the specifics of each maintenance entry.\\nAnswer: 5'},\n",
+       "  {'user': 'What type of diagram is represented in this illustration?',\n",
+       "   'assistant': 'This illustration represents an Entity Relationship Diagram, which shows entities, their attributes, and how they are related to one another within a system, particularly for equipment maintenance and service records.\\nAnswer: Entity Relationship Diagram'},\n",
+       "  {'user': 'Which attribute of Supplier is related to contact information?',\n",
+       "   'assistant': 'The contact information attribute related to Supplier is ContactNumber which provides the phone number for communication.\\nAnswer: ContactNumber'},\n",
+       "  {'user': 'Who maintains the Equipment records?',\n",
+       "   'assistant': 'The Equipment records are maintained through the relationship labeled \"Maintained By\" with the Maintenance Record, indicating a direct link between equipment details and their records of maintenance.\\nAnswer: Maintenance Record'}],\n",
+       " 'source': 'CoSyn-400k-diagram',\n",
+       " 'relevance_ratings': [5, 5, 5, 5, 5, 5, 5, 5, 2],\n",
+       " 'relevance_min': 2,\n",
+       " 'visual_dependency_ratings': [1, 2, 4, 4, 4, 4, 5, 2, 2],\n",
+       " 'visual_dependency_min': 1,\n",
+       " 'image_correspondence_ratings': [3, 5, 4, 4, 5, 5, 5, 3, 5],\n",
+       " 'image_correspondence_min': 3,\n",
+       " 'formatting_ratings': [4, 4, 4, 4, 4, 5, 4, 4, 4],\n",
+       " 'formatting_min': 4}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<|im_start|>system\\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\\n<|im_start|>user\\nWhat is the primary key of the Equipment entity?<|im_end|>\\n<|im_start|>assistant\\nThe primary key of the Equipment entity is EquipmentID, which uniquely identifies each piece of equipment in the diagram.\\nAnswer: EquipmentID<|im_end|>\\n<|im_start|>user\\nWhat is the relationship between Equipment and Maintenance Record?<|im_end|>\\n<|im_start|>assistant\\nThe relationship between Equipment and Maintenance Record is labeled as \"Maintained By\", indicating that one piece of equipment can have multiple maintenance records associated with it.\\nAnswer: Maintained By<|im_end|>\\n<|im_start|>user\\nHow many relationships does the Supplier entity have?<|im_end|>\\n<|im_start|>assistant\\nThe Supplier entity has two relationships: \"Supplied By\" with Equipment and \"Delivered By\" with Logistics Company, indicating it serves as a midpoint in both flows.\\nAnswer: 2<|im_end|>\\n<|im_start|>user\\nWhat is the cardinality of the relationship between Equipment and Supplier?<|im_end|>\\n<|im_start|>assistant\\nThe cardinality of the relationship between Equipment and Supplier is M:1, which means many pieces of equipment can be supplied by one supplier.\\nAnswer: M:1<|im_end|>\\n<|im_start|>user\\nWhich entity is connected to Logistics Company through the Delivered By relationship?<|im_end|>\\n<|im_start|>assistant\\nThe entity connected to Logistics Company through the Delivered By relationship is Supplier, indicating that the supplier relies on the logistics company for delivering equipment.\\nAnswer: Supplier<|im_end|>\\n<|im_start|>user\\nHow many attributes does the Maintenance Record entity have?<|im_end|>\\n<|im_start|>assistant\\nThe Maintenance Record entity has five attributes: RecordID, Date, Description, ServiceCost, and ServiceProvider, detailing the specifics of each maintenance entry.\\nAnswer: 5<|im_end|>\\n<|im_start|>user\\nWhat type of diagram is represented in this illustration?<|im_end|>\\n<|im_start|>assistant\\nThis illustration represents an Entity Relationship Diagram, which shows entities, their attributes, and how they are related to one another within a system, particularly for equipment maintenance and service records.\\nAnswer: Entity Relationship Diagram<|im_end|>\\n<|im_start|>user\\nWhich attribute of Supplier is related to contact information?<|im_end|>\\n<|im_start|>assistant\\nThe contact information attribute related to Supplier is ContactNumber which provides the phone number for communication.\\nAnswer: ContactNumber<|im_end|>\\n<|im_start|>user\\nWho maintains the Equipment records?<|im_end|>\\n<|im_start|>assistant\\nThe Equipment records are maintained through the relationship labeled \"Maintained By\" with the Maintenance Record, indicating a direct link between equipment details and their records of maintenance.\\nAnswer: Maintenance Record<|im_end|>\\n'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "PFNRdFus5ZfM"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 256, 576])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[[18.7500, 11.0000, 15.1250,  ..., 12.6875, 15.3750,  8.4375],\n",
+       "          [19.5000, 10.0625, 14.5625,  ..., 17.5000, 16.5000, 12.5625],\n",
+       "          [16.7500,  9.6875, 13.6250,  ..., 11.4375, 14.0000, 10.3750],\n",
+       "          ...,\n",
+       "          [17.8750, 25.5000, 32.7500,  ..., 16.0000, 11.5000,  9.9375],\n",
+       "          [24.6250, 27.6250, 29.8750,  ..., 13.6875, 13.0000, 11.3750],\n",
+       "          [13.5000, 31.8750, 31.6250,  ..., 10.7500,  8.9375,  8.5625]]],\n",
+       "        device='cuda:0', dtype=torch.bfloat16, grad_fn=<UnsafeViewBackward0>),\n",
+       " tensor(1.9062, device='cuda:0', dtype=torch.bfloat16,\n",
+       "        grad_fn=<NllLossBackward0>))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vlm(text, sample['images'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AT9eCHhU2cSe",
+    "outputId": "e1e8e953-c40b-4177-ff62-1c49ad2cafdd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 100 | loss 1.9766\n",
+      "step 200 | loss 2.7969\n",
+      "step 300 | loss 2.0938\n",
+      "step 400 | loss 5.1875\n",
+      "step 500 | loss 2.5625\n",
+      "step 600 | loss 2.5000\n",
+      "step 700 | loss 1.3516\n",
+      "step 800 | loss 1.0000\n",
+      "step 900 | loss 1.5625\n",
+      "step 1000 | loss 1.7812\n",
+      "step 1100 | loss 2.4688\n",
+      "step 1200 | loss 1.6250\n",
+      "step 1300 | loss 2.1094\n",
+      "step 1400 | loss 1.1641\n",
+      "step 1500 | loss 0.9219\n",
+      "step 1600 | loss 1.5000\n",
+      "step 1700 | loss 2.2969\n",
+      "step 1800 | loss 1.4219\n",
+      "step 1900 | loss 2.5781\n",
+      "step 2000 | loss 1.8828\n",
+      "step 2100 | loss 0.6445\n",
+      "step 2200 | loss 0.7930\n",
+      "step 2300 | loss 1.5078\n",
+      "step 2400 | loss 2.2969\n",
+      "step 2500 | loss 2.2812\n",
+      "step 2600 | loss 1.7031\n",
+      "step 2700 | loss 1.9531\n",
+      "step 2800 | loss 2.4375\n",
+      "step 2900 | loss 0.7578\n",
+      "step 3000 | loss 3.6562\n",
+      "step 3100 | loss 4.1250\n",
+      "step 3200 | loss 1.8281\n",
+      "step 3300 | loss 2.3125\n",
+      "step 3400 | loss 1.6406\n",
+      "step 3500 | loss 2.4375\n",
+      "step 3600 | loss 1.8047\n",
+      "step 3700 | loss 1.9531\n",
+      "step 3800 | loss 2.0000\n",
+      "step 3900 | loss 1.7812\n",
+      "step 4000 | loss 1.2891\n",
+      "step 4100 | loss 1.8516\n",
+      "step 4200 | loss 1.8438\n",
+      "step 4300 | loss 0.8086\n",
+      "step 4400 | loss 2.0156\n",
+      "step 4500 | loss 2.1719\n",
+      "step 4600 | loss 2.1875\n",
+      "step 4700 | loss 2.2500\n",
+      "step 4800 | loss 2.4688\n",
+      "step 4900 | loss 1.4453\n",
+      "step 5000 | loss 1.3672\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import torch.optim as optim\n",
+    "optimizer = optim.AdamW(vlm.parameters(), lr=1e-5)\n",
+    "\n",
+    "step = 0\n",
+    "losses = []\n",
+    "for sample in dataset:\n",
+    "    text = apply_chat_template_to_sample(sample)\n",
+    "    if len(text) > 3000 or len(sample['images']) != 1:\n",
+    "      continue\n",
+    "    images = [sample['images'][0].convert('RGB')]\n",
+    "    step += 1\n",
+    "\n",
+    "    optimizer.zero_grad()\n",
+    "    logits, loss = vlm(text, images)\n",
+    "    losses.append(loss)\n",
+    "\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    if step%100 == 0:\n",
+    "        print(f\"step {step} | loss {loss.item():.4f}\")\n",
+    "\n",
+    "    if step > 5000:\n",
+    "      json.dump([round(l.item(), 4) for l in losses], open(\"chapter_3_loss.json\", \"w\"))\n",
+    "      break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 226
+    },
+    "id": "1nLOpTCs4q4F",
+    "outputId": "ea6a05a6-cfc3-483a-ac22-1ca31f7be664"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58cd5e73761a416a851346fc23dff3cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Resolving data files:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "dataset = load_dataset(\"HuggingFaceM4/FineVisionMax\", split=\"train\", streaming=True)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"HuggingFaceTB/SmolLM2-135M-Instruct\")\n",
+    "\n",
+    "def tokenize_sample(sample):\n",
+    "    messages_list = []\n",
+    "    for data_dict in sample['texts']:\n",
+    "        messages_list.append({'role': 'user', 'content': data_dict['user']})\n",
+    "        messages_list.append({'role': 'assistant', 'content': data_dict['assistant']})\n",
+    "    tokens = tokenizer.apply_chat_template(messages_list, tokenize=True)\n",
+    "    return tokens['input_ids']\n",
+    "\n",
+    "max_length = 2048\n",
+    "curr_sample = []\n",
+    "batch = []\n",
+    "max_batch_size = 8\n",
+    "for sample in dataset:\n",
+    "    tokens = tokenize_sample(sample)\n",
+    "    if len(tokens) > max_length:\n",
+    "        continue  # Skip samples that are too long\n",
+    "    if len(curr_sample) + len(tokens) < max_length:\n",
+    "        curr_sample.extend(tokens)\n",
+    "    else:\n",
+    "        if len(batch) == max_batch_size:\n",
+    "            break # We've reached the max batch size, so we need to break\n",
+    "        batch.append(curr_sample)\n",
+    "        curr_sample = tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "TQ7S--kRFAc-"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "a81sI4oFFA5d"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from transformers import AutoModel, AutoProcessor, AutoTokenizer, AutoModelForCausalLM\n",
+    "\n",
+    "class VisionLanguageModel(nn.Module):\n",
+    "    def __init__(self, vision_encoder_ckpt, language_model_ckpt, tokenizer, modality_input_dim=768, modality_output_dim=576):\n",
+    "        super().__init__()\n",
+    "        self.vision_encoder = AutoModel.from_pretrained(vision_encoder_ckpt).vision_model\n",
+    "        self.vision_processor = AutoProcessor.from_pretrained(vision_encoder_ckpt)\n",
+    "        self.modality_projector = nn.Linear(modality_input_dim, modality_output_dim, bias=False)\n",
+    "        self.tokenizer = tokenizer # AutoTokenizer.from_pretrained(language_model_ckpt, extra_special_tokens={\"image_token\": \"<|image|>\"})\n",
+    "        self.llm = AutoModelForCausalLM.from_pretrained(language_model_ckpt)\n",
+    "        self.llm.resize_token_embeddings(len(self.tokenizer))  # Resize the LLM's token embeddings to match the tokenizer's new vocab size\n",
+    "\n",
+    "    def _replace_img_tokens_with_embd(self, input_ids, token_embd, image_embd):\n",
+    "        \"\"\"\n",
+    "        Replace every image-token placeholder in `input_ids` with the corresponding slice\n",
+    "        from `image_embd`. Supports an arbitrary number of image-token placeholders per sample.\n",
+    "        The first example in the batch might have 2 images and the second none.\n",
+    "        \"\"\"\n",
+    "        # Clone the original embeddings to avoid in-place issues\n",
+    "        updated_token_embd = token_embd.clone()\n",
+    "\n",
+    "        # Build a mask of all image-token positions: shape [B, T_seq]\n",
+    "        mask = (input_ids == self.tokenizer.image_token_id)\n",
+    "        updated_token_embd[mask] = image_embd.view(-1, image_embd.size(-1)).to(updated_token_embd.dtype) # torch flattens before assigning\n",
+    "\n",
+    "        return updated_token_embd\n",
+    "\n",
+    "    def forward(self, input_ids, image):\n",
+    "        processed_img = self.vision_processor(images=[image], return_tensors=\"pt\").to(self.llm.device)\n",
+    "        image_embd = self.vision_encoder(**processed_img).last_hidden_state\n",
+    "        image_embd = self.modality_projector(image_embd)\n",
+    "\n",
+    "        token_embd = self.llm.model.embed_tokens(input_ids)\n",
+    "        combined_embd = self._replace_img_tokens_with_embd(input_ids, token_embd, image_embd)\n",
+    "\n",
+    "        logits = self.llm(inputs_embeds=combined_embd).logits\n",
+    "\n",
+    "        # 1. Create labels from the input_ids\n",
+    "        labels = input_ids.clone()\n",
+    "\n",
+    "        # 2. We don't want to compute loss on image tokens.\n",
+    "        #    Set them to the ignore_index (which is pad_token_id).\n",
+    "        labels[labels == self.tokenizer.image_token_id] = self.tokenizer.pad_token_id\n",
+    "\n",
+    "        # 3. Standard next-token-prediction shifting:\n",
+    "        #    The logit at index `i` predicts the token at index `i+1`\n",
+    "        shift_logits = logits[..., :-1, :].contiguous()\n",
+    "        shift_labels = labels[..., 1:].contiguous()\n",
+    "\n",
+    "        # 4. Compute loss, ignoring all tokens marked with pad_token_id\n",
+    "        #    (this now includes both padding AND image tokens)\n",
+    "        loss = nn.functional.cross_entropy(\n",
+    "            shift_logits.reshape(-1, shift_logits.size(-1)),\n",
+    "            shift_labels.reshape(-1),\n",
+    "            ignore_index=self.tokenizer.pad_token_id\n",
+    "        )\n",
+    "\n",
+    "        return logits, loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "id": "rdfs-3Q-Gf9u"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3e4c194362d4f9dbd2861ab057850c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Resolving data files:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "003b90cf7584444398ae733d2b8cd547",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/408 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d882c51b85d74a3c9a3426bddee36b8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/272 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (28674 > 8192). Running this sequence through the model will result in indexing errors\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 100 | loss 1.4531 | smoothed loss: 1.5258\n",
+      "The last 100 steps took: 91.58 seconds.\n",
+      "step 200 | loss 1.4609 | smoothed loss: 1.4172\n",
+      "The last 100 steps took: 93.71 seconds.\n",
+      "step 300 | loss 1.5703 | smoothed loss: 1.3359\n",
+      "The last 100 steps took: 93.15 seconds.\n",
+      "step 400 | loss 1.5156 | smoothed loss: 1.3262\n",
+      "The last 100 steps took: 93.01 seconds.\n",
+      "step 500 | loss 1.0859 | smoothed loss: 1.2203\n",
+      "The last 100 steps took: 73.74 seconds.\n",
+      "step 600 | loss 1.3281 | smoothed loss: 1.3719\n",
+      "The last 100 steps took: 96.48 seconds.\n",
+      "step 700 | loss 0.6992 | smoothed loss: 1.2375\n",
+      "The last 100 steps took: 92.60 seconds.\n",
+      "step 800 | loss 1.2656 | smoothed loss: 1.3758\n",
+      "The last 100 steps took: 95.97 seconds.\n",
+      "step 900 | loss 1.1484 | smoothed loss: 1.2699\n",
+      "The last 100 steps took: 84.72 seconds.\n",
+      "step 1000 | loss 1.5000 | smoothed loss: 1.3703\n",
+      "The last 100 steps took: 75.39 seconds.\n",
+      "step 1100 | loss 0.8594 | smoothed loss: 1.0289\n",
+      "The last 100 steps took: 85.08 seconds.\n",
+      "step 1200 | loss 1.4141 | smoothed loss: 1.1086\n",
+      "The last 100 steps took: 84.17 seconds.\n",
+      "step 1300 | loss 0.8047 | smoothed loss: 1.1398\n",
+      "The last 100 steps took: 90.32 seconds.\n",
+      "step 1400 | loss 1.7266 | smoothed loss: 1.3914\n",
+      "The last 100 steps took: 76.20 seconds.\n",
+      "step 1500 | loss 0.8711 | smoothed loss: 1.1715\n",
+      "The last 100 steps took: 91.56 seconds.\n",
+      "step 1600 | loss 1.1641 | smoothed loss: 1.1652\n",
+      "The last 100 steps took: 90.52 seconds.\n",
+      "step 1700 | loss 1.3750 | smoothed loss: 1.4680\n",
+      "The last 100 steps took: 91.58 seconds.\n",
+      "step 1800 | loss 1.2969 | smoothed loss: 1.2055\n",
+      "The last 100 steps took: 93.18 seconds.\n",
+      "step 1900 | loss 1.3594 | smoothed loss: 1.2840\n",
+      "The last 100 steps took: 75.23 seconds.\n",
+      "step 2000 | loss 1.1875 | smoothed loss: 1.2492\n",
+      "The last 100 steps took: 93.22 seconds.\n",
+      "step 2100 | loss 1.5312 | smoothed loss: 1.2500\n",
+      "The last 100 steps took: 89.42 seconds.\n",
+      "step 2200 | loss 1.2578 | smoothed loss: 1.0488\n",
+      "The last 100 steps took: 88.84 seconds.\n",
+      "step 2300 | loss 1.3516 | smoothed loss: 1.1641\n",
+      "The last 100 steps took: 76.10 seconds.\n",
+      "step 2400 | loss 0.7969 | smoothed loss: 1.1305\n",
+      "The last 100 steps took: 94.21 seconds.\n",
+      "step 2500 | loss 1.4297 | smoothed loss: 1.2496\n",
+      "The last 100 steps took: 92.31 seconds.\n",
+      "step 2600 | loss 1.1406 | smoothed loss: 1.2168\n",
+      "The last 100 steps took: 91.25 seconds.\n",
+      "step 2700 | loss 1.2031 | smoothed loss: 1.2645\n",
+      "The last 100 steps took: 82.86 seconds.\n",
+      "step 2800 | loss 0.8164 | smoothed loss: 1.0625\n",
+      "The last 100 steps took: 75.41 seconds.\n",
+      "step 2900 | loss 1.0547 | smoothed loss: 1.3023\n",
+      "The last 100 steps took: 91.18 seconds.\n",
+      "step 3000 | loss 0.7578 | smoothed loss: 1.2715\n",
+      "The last 100 steps took: 89.79 seconds.\n",
+      "step 3100 | loss 1.0391 | smoothed loss: 1.2273\n",
+      "The last 100 steps took: 85.47 seconds.\n",
+      "step 3200 | loss 0.9609 | smoothed loss: 1.0578\n",
+      "The last 100 steps took: 92.26 seconds.\n",
+      "step 3300 | loss 1.5469 | smoothed loss: 1.1762\n",
+      "The last 100 steps took: 74.85 seconds.\n",
+      "step 3400 | loss 1.8828 | smoothed loss: 1.2742\n",
+      "The last 100 steps took: 90.77 seconds.\n",
+      "step 3500 | loss 1.6094 | smoothed loss: 1.3133\n",
+      "The last 100 steps took: 95.12 seconds.\n",
+      "step 3600 | loss 1.2031 | smoothed loss: 1.1922\n",
+      "The last 100 steps took: 91.85 seconds.\n",
+      "step 3700 | loss 1.6250 | smoothed loss: 1.3082\n",
+      "The last 100 steps took: 92.74 seconds.\n",
+      "step 3800 | loss 1.4688 | smoothed loss: 1.2098\n",
+      "The last 100 steps took: 75.41 seconds.\n",
+      "step 3900 | loss 1.2578 | smoothed loss: 1.0668\n",
+      "The last 100 steps took: 90.95 seconds.\n",
+      "step 4000 | loss 1.1562 | smoothed loss: 1.1832\n",
+      "The last 100 steps took: 92.12 seconds.\n",
+      "step 4100 | loss 1.2969 | smoothed loss: 1.2406\n",
+      "The last 100 steps took: 91.43 seconds.\n",
+      "step 4200 | loss 1.5234 | smoothed loss: 1.1359\n",
+      "The last 100 steps took: 89.33 seconds.\n",
+      "step 4300 | loss 0.9453 | smoothed loss: 1.0145\n",
+      "The last 100 steps took: 75.67 seconds.\n",
+      "step 4400 | loss 1.6562 | smoothed loss: 1.1062\n",
+      "The last 100 steps took: 91.63 seconds.\n",
+      "step 4500 | loss 0.7305 | smoothed loss: 1.1695\n",
+      "The last 100 steps took: 90.45 seconds.\n",
+      "step 4600 | loss 1.3906 | smoothed loss: 1.1898\n",
+      "The last 100 steps took: 83.71 seconds.\n",
+      "step 4700 | loss 1.1328 | smoothed loss: 1.0383\n",
+      "The last 100 steps took: 75.69 seconds.\n",
+      "step 4800 | loss 0.6484 | smoothed loss: 1.0418\n",
+      "The last 100 steps took: 88.35 seconds.\n",
+      "step 4900 | loss 1.5859 | smoothed loss: 1.1891\n",
+      "The last 100 steps took: 90.32 seconds.\n",
+      "step 5000 | loss 0.8711 | smoothed loss: 1.2063\n",
+      "The last 100 steps took: 91.77 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from transformers import AutoTokenizer\n",
+    "import torch.optim as optim\n",
+    "import time\n",
+    "import json\n",
+    "import numpy as np\n",
+    "\n",
+    "vision_encoder_ckpt = \"google/siglip2-base-patch16-256\"\n",
+    "language_model_ckpt = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
+    "dataset = load_dataset(\"HuggingFaceM4/FineVisionMax\", split=\"train\", streaming=True)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(language_model_ckpt, extra_special_tokens={\"image_token\": \"<|image|>\"})\n",
+    "vlm = VisionLanguageModel(vision_encoder_ckpt, language_model_ckpt, tokenizer).to(\"cuda\")\n",
+    "optimizer = optim.AdamW(vlm.parameters(), lr=1e-4)\n",
+    "\n",
+    "def tokenize_sample(sample):\n",
+    "    messages_list = []\n",
+    "    for data_dict in sample['texts']:\n",
+    "        messages_list.append({'role': 'user', 'content': data_dict['user']})\n",
+    "        messages_list.append({'role': 'assistant', 'content': data_dict['assistant']})\n",
+    "    messages_list[0]['content'] = tokenizer.image_token*256 + messages_list[0]['content']  # add image tokens\n",
+    "    tokens = tokenizer.apply_chat_template(messages_list, tokenize=True)\n",
+    "    return tokens['input_ids']\n",
+    "\n",
+    "step = 0\n",
+    "losses = []\n",
+    "max_length = 2048\n",
+    "curr_sample = []\n",
+    "curr_image = []\n",
+    "batch = []\n",
+    "image_batch = []\n",
+    "max_batch_size = 4\n",
+    "batch_complete = False\n",
+    "start = time.time()\n",
+    "for sample in dataset:\n",
+    "    if len(sample['images']) != 1:\n",
+    "      continue\n",
+    "    tokens = tokenize_sample(sample)\n",
+    "    if len(tokens) > max_length:\n",
+    "        continue  # Skip samples that are too long\n",
+    "    if len(curr_sample) + len(tokens) < max_length:\n",
+    "        curr_sample.extend(tokens)\n",
+    "        curr_image.extend([sample['images'][0].convert('RGB')])\n",
+    "    else:\n",
+    "        batch.append(torch.nn.functional.pad(torch.Tensor(curr_sample), (max_length - len(curr_sample),0), 'constant', tokenizer.pad_token_id))\n",
+    "        image_batch.extend(curr_image)\n",
+    "        if len(batch) == max_batch_size:\n",
+    "          batch_complete = True # We've reached the max batch size, so we can continue\n",
+    "        curr_sample = tokens\n",
+    "        curr_image = [sample['images'][0].convert('RGB')]\n",
+    "    if batch_complete:\n",
+    "      step += 1\n",
+    "      if step%100 == 0:\n",
+    "        print(f\"step {step} | loss {loss.item():.4f} | smoothed loss: {np.mean(losses[-10:]):.4f}\")\n",
+    "        print(f\"The last 100 steps took: {time.time()-start:.2f} seconds.\")\n",
+    "        start = time.time()\n",
+    "\n",
+    "      optimizer.zero_grad()\n",
+    "      logits, loss = vlm(torch.stack(batch).long().cuda(), image_batch)\n",
+    "      losses.append(loss.item())\n",
+    "\n",
+    "      loss.backward()\n",
+    "      optimizer.step()\n",
+    "\n",
+    "      batch_complete = False\n",
+    "      batch = []\n",
+    "      image_batch = []\n",
+    "      if step > 5000:\n",
+    "        break\n",
+    "\n",
+    "json.dump([round(l, 4) for l in losses], open(\"chapter_3_batched_loss.json\", \"w\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "json.dump([round(l, 4) for l in losses], open(\"chapter_3_batched_loss.json\", \"w\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8uhcUAcSzWo8"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers.image_utils import load_image\n",
+    "\n",
+    "image = load_image(\"https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000002006.jpg\")\n",
+    "\n",
+    "def generate(trained_vlm, tokenizer, input_text, image, max_new_tokens):\n",
+    "    text = [{\"role\": \"user\", \"content\": tokenizer.image_token*256 + input_text}]\n",
+    "    text_str = tokenizer.apply_chat_template(text, add_generation_prompt=True, tokenize=False)\n",
+    "    input_tokens = tokenizer(text_str, return_tensors=\"pt\").input_ids.cuda()    \n",
+    "    next_token = None\n",
+    "    output = []\n",
+    "    end_of_sentence_token = tokenizer.eos_token_id\n",
+    "\n",
+    "    while len(output) < max_new_tokens:\n",
+    "        prediction = trained_vlm(input_tokens, image)[0]\n",
+    "        next_token = torch.argmax(prediction[:, -1, :])\n",
+    "        if next_token == end_of_sentence_token:\n",
+    "            break\n",
+    "        output.append(next_token)\n",
+    "        input_tokens = torch.cat([input_tokens, next_token[None, None]], dim=1)\n",
+    "\n",
+    "    return ''.join(tokenizer.batch_decode(output))\n",
+    "\n",
+    "print(generate(vlm, tokenizer, \"What do you see here?\", image, 32))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.19"
+  },
+  "widgets": {
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+      "_view_module_version": "1.5.0",
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