"""Builds Knowledge_Distillation_Report.docx summarizing design, implementation, and evaluation."""
import datetime
from docx import Document
from docx.shared import Pt, Inches, RGBColor, Cm
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
from docx.oxml.ns import qn
from docx.oxml import OxmlElement

ASSETS = "/Users/reevechaitanya/Documents/2_Experimentation_n_Research/demo/report_assets"

ACCENT = RGBColor(0x1F, 0x4E, 0x79)
GREY = RGBColor(0x40, 0x40, 0x40)

doc = Document()

# ---------------------------------------------------------------------------
# Global style setup
# ---------------------------------------------------------------------------
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add_page_number_footer(doc.sections[0])


def h(text, level=1):
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def p(text="", bold=False, italic=False, size=None, color=None, align=None, space_after=None):
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# ---------------------------------------------------------------------------
# Title page
# ---------------------------------------------------------------------------
title_p = doc.add_paragraph()
title_p.alignment = WD_ALIGN_PARAGRAPH.CENTER
title_p.paragraph_format.space_before = Pt(120)
run = title_p.add_run("Knowledge Distillation on the Banking77 Intent Dataset")
run.bold = True
run.font.size = Pt(26)
run.font.color.rgb = ACCENT

sub_p = doc.add_paragraph()
sub_p.alignment = WD_ALIGN_PARAGRAPH.CENTER
sub_p.paragraph_format.space_before = Pt(10)
run = sub_p.add_run("Design, Implementation, and Evaluation Report")
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run = sub2.add_run("Compressing a Fine-Tuned BERT Teacher into a Compact CPU-Deployable Transformer Student")
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run.italic = True
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meta_p.paragraph_format.space_before = Pt(60)
meta_lines = [
    f"Generated: {datetime.date.today().strftime('%B %d, %Y')}",
    "Source notebook: knowledge_distillation_assignment.ipynb",
    "Dataset: PolyAI/banking77 (77-class banking intent classification)",
    "Environment: conda env agn_env, Python 3.12, Apple Silicon (MPS + CPU)",
]
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doc.add_page_break()

# ---------------------------------------------------------------------------
# 1. Introduction
# ---------------------------------------------------------------------------
h("1. Introduction and Objective", 1)
p(
    "Large fine-tuned Transformers such as BERT deliver strong accuracy on text classification tasks but "
    "are frequently too large, too slow, and too memory-hungry to deploy on edge devices, mobile "
    "applications, or latency-sensitive services. Knowledge distillation addresses this by transferring "
    "the behaviour of a large “Teacher” model into a much smaller “Student” model, using the "
    "Teacher's full output probability distribution — not just its predicted class — as a richer training "
    "signal."
)
p(
    "This report documents the design, implementation, and evaluation of an end-to-end knowledge "
    "distillation pipeline built for the 77-class Banking77 intent-classification dataset, structured "
    "around three modules and six tasks:"
)
add_table(
    ["Module", "Tasks"],
    [
        ["1. Teacher Labeling & Student Setup", "Task 1: Teacher fine-tuning & soft-label generation\nTask 2: Student tokenizer alignment"],
        ["2. Distillation Architecture & Training", "Task 3: Compact student transformer\nTask 4: Distillation loss function & training"],
        ["3. Comparative Analysis & Benchmarking", "Task 5: Accuracy vs. compression evaluation\nTask 6: Deployment metrics analysis"],
    ],
    col_widths=[2.6, 3.7],
)
p(
    "All code was implemented and executed end-to-end in a single Jupyter notebook "
    "(knowledge_distillation_assignment.ipynb) inside the conda environment agn_env (Python 3.12) on an "
    "Apple Silicon machine. The Teacher was fine-tuned using MPS acceleration; the Student was deliberately "
    "trained and benchmarked entirely on CPU, since CPU-only inference is the target deployment profile "
    "this exercise is optimizing for."
)

# ---------------------------------------------------------------------------
# 2. Dataset
# ---------------------------------------------------------------------------
h("2. Dataset", 1)
p(
    "Banking77 is a fine-grained intent-classification dataset of customer-support utterances for a "
    "banking app, labelled with one of 77 narrow, often semantically overlapping intents (e.g. "
    "card_arrival vs. card_delivery_estimate, declined_card_payment vs. declined_cash_withdrawal). This "
    "makes it a good stress test for distillation: the fine granularity of the label space means the "
    "relative similarity between classes — the information that soft labels carry and hard labels do "
    "not — is directly relevant to classification accuracy."
)
add_table(
    ["Property", "Value"],
    [
        ["Source", "PolyAI/banking77 (loaded via a verified parquet mirror, legacy-datasets/banking77, after the original repository's script-based loader was found incompatible with the installed datasets library version)"],
        ["Training examples", "10,003"],
        ["Test examples", "3,080"],
        ["Number of classes", "77"],
        ["Example text", "“I am still waiting on my card?” → label: card_arrival"],
    ],
    col_widths=[2.0, 4.3],
)

doc.add_page_break()

# ---------------------------------------------------------------------------
# Module 1
# ---------------------------------------------------------------------------
h("3. Module 1: Teacher Labeling and Student Setup", 1)

h("3.1 Task 1 — Teacher Integration and Soft-Label Generation", 2)

h("Design", 3)
p(
    "bert-base-uncased (110M parameters) was selected as the Teacher for its strong general-purpose "
    "language representations and its established track record on intent-classification benchmarks. "
    "The design goal was twofold: (1) fine-tune it into a strong classifier for banking77, and (2) cache "
    "its full 77-way probability distribution — not just its hard prediction — for every training "
    "example, so that the Student never needs the (comparatively expensive) Teacher to run again during "
    "its own training."
)

h("Implementation", 3)
bullets([
    "Tokenization: bert-base-uncased's own WordPiece tokenizer, sequences truncated to 64 tokens.",
    "Model: transformers.BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=77) — the pretrained encoder weights are kept; a new randomly-initialized 77-way classification head is trained from scratch.",
    "Training: Hugging Face Trainer, 3 epochs, batch size 32 (train) / 64 (eval), learning rate 3e-5, weight decay 0.01, evaluated each epoch on the test set, run on Apple Silicon MPS.",
    "Soft-label caching: one no-gradient forward pass over the full, unshuffled training set (and separately the test set) immediately after fine-tuning, producing a [10003 × 77] and a [3080 × 77] logits tensor respectively. Because the pass is unshuffled, teacher_train_logits[i] corresponds exactly to training example i by index — this index-based join is what Task 2 relies on.",
])

h("Results", 3)
add_table(
    ["Metric (test set)", "Value"],
    [
        ["Evaluation loss", "0.839"],
        ["Accuracy", "87.6%"],
        ["Macro F1", "0.868"],
        ["Total parameters", "109,541,453"],
        ["Cached train logits shape", "[10,003, 77]"],
        ["Cached test logits shape", "[3,080, 77]"],
    ],
    col_widths=[3.0, 3.0],
)

h("Interpretation — Why Soft Labels Carry “Dark Knowledge”", 3)
p(
    "A one-hot hard label for “I am still waiting on my card?” states only that the correct class is "
    "card_arrival and that all 76 other classes are equally, absolutely wrong. That is not what the "
    "Teacher actually believes: its softmax distribution might place 62% probability on card_arrival, "
    "21% on the closely related card_delivery_estimate, and small residual mass on a handful of other "
    "intents. Training against the full distribution rather than the arg-max alone:"
)
bullets([
    ["Transfers inter-class similarity structure. ", True],
    "The relative magnitude of non-target probabilities encodes which intents the Teacher finds confusable with which — a signal entirely absent from a one-hot vector, and especially valuable on a taxonomy with many near-duplicate intents like banking77's.",
])
bullets([
    ["Acts as an implicit regularizer. ", True],
    "A smoother, higher-entropy target does not force the Student's logits toward extreme values to satisfy a one-hot target, which tends to improve generalization — particularly important for a Student with a very small parameter budget.",
])
bullets([
    ["Supplies more effective supervision per example. ", True],
    "A hard label carries at most log₂(77) ≈ 6.3 bits of information (which class); a full probability vector carries substantially more, letting a smaller, more data-constrained Student recover more of the Teacher's decision surface from the same training set.",
])
bullets([
    ["Is amplified by temperature scaling. ", True],
    "Raising the softmax temperature T before distillation (used in Task 4) inflates the small probabilities on non-target classes — exactly where most of this structural information lives, since at T=1 those probabilities are too close to zero to produce a useful gradient.",
])

h("3.2 Task 2 — Student Tokenizer Alignment", 2)

h("Design", 3)
p(
    "Rather than reusing the Teacher's ~30k-token BERT vocabulary, the Student is given its own compact "
    "WordPiece tokenizer trained from scratch, directly on the banking77 training corpus. This keeps the "
    "Student's vocabulary small (a major lever on parameter count, since embedding-table size scales with "
    "vocab_size × hidden_size) while concentrating that small vocabulary on the words that actually appear "
    "in this domain."
)

h("Implementation", 3)
bullets([
    "Backend: tokenizers.Tokenizer with a WordPiece model, BERT-style lowercasing normalizer, whitespace pre-tokenizer, and [CLS]/[SEP] template post-processing.",
    "Trained via WordPieceTrainer with a target vocabulary size of 3,000 tokens and special tokens [PAD], [UNK], [CLS], [SEP], directly on the 10,003 raw training utterances.",
    "A helper (student_encode_batch) pads/truncates every example to a fixed 32-token sequence length for the Student's fixed-size batched forward pass.",
])

h("Results — Tokenizer Comparison", 3)
add_table(
    ["Property", "Teacher (BERT)", "Student (custom WordPiece)"],
    [
        ["Vocabulary size", "30,522", "3,000"],
        ["Vocabulary compression", "—", "10.2x smaller"],
    ],
    col_widths=[2.4, 2.0, 2.4],
)
p("Side-by-side tokenization of five sample utterances:", bold=False)
add_table(
    ["Utterance (truncated)", "Teacher tokens", "Student tokens (incl. [CLS]/[SEP])"],
    [
        ["I am still waiting on my card?", "8", "10"],
        ["What can I do if my card still hasn't arrived...", "16", "18"],
        ["I have been waiting over a week. Is the card...", "14", "16"],
        ["Can I track my card while it is in the process...", "14", "16"],
        ["How do I know if I will get my card, or if it...", "17", "19"],
    ],
    col_widths=[3.6, 1.4, 1.9],
)

h("Interpretation — Handling the Vocabulary Mismatch", 3)
p(
    "On these five common, in-domain examples, the Student's token count is exactly the Teacher's count "
    "plus two — the [CLS]/[SEP] markers the Student's tokens include and the Teacher's tokenize() call "
    "does not. In other words, for frequent, in-vocabulary phrasing the domain-trained 3,000-token "
    "vocabulary segments text about as coarsely as BERT's 30,522-token vocabulary; the cost of a much "
    "smaller vocabulary shows up on rarer or compound words not well represented in the 10,003-example "
    "training corpus, via more aggressive subword splitting and a higher effective [UNK] rate, rather "
    "than on everyday vocabulary."
)
p(
    "A more fundamental question this task addresses is how to align Teacher and Student when they "
    "tokenize the same text differently. A naive token-level distillation scheme — as used in "
    "sequence-to-sequence or token-classification distillation — requires the Teacher's and Student's "
    "output sequences to line up position by position, which breaks immediately once the two tokenizers "
    "produce different token counts for the same input. That problem does not apply here, because "
    "distillation in this project is over sequence classification: the Teacher emits exactly one 77-way "
    "probability vector per example, regardless of how many tokens that example was split into "
    "internally. The only alignment that matters is therefore at the example level, not the token level:"
)
bullets([
    ["Strategy used: ", True],
    "Teacher logits are computed once per raw-text example (Task 1) and cached indexed by the example's position in the unshuffled training set. The same raw text is independently re-tokenized with the Student's own tokenizer for the Student's forward pass. The two are joined purely by row index inside BankingStudentDataset — teacher_train_logits[i] always corresponds to train_raw[i], irrespective of how differently each tokenizer segmented that row's text.",
])
bullets([
    ["Residual risk and mitigation: ", True],
    "A much smaller vocabulary does risk losing lexical signal on rare words the Teacher could represent more precisely. This is mitigated by training the Student tokenizer directly on in-domain banking77 text, so its limited token budget is spent on the vocabulary that actually matters for this task rather than a generic corpus.",
])

doc.add_page_break()

# ---------------------------------------------------------------------------
# Module 2
# ---------------------------------------------------------------------------
h("4. Module 2: Distillation Architecture and Training", 1)

h("4.1 Task 3 — Compact Student Transformer Construction", 2)

h("Design", 3)
p(
    "The Student is a small, hand-built encoder-only Transformer — assembled directly from PyTorch "
    "nn.Module / nn.TransformerEncoderLayer primitives rather than reusing a pretrained architecture — "
    "sized deliberately small enough to train and run comfortably on CPU."
)
add_table(
    ["Architecture parameter", "Value"],
    [
        ["Vocabulary size", "3,000 (Task 2 tokenizer)"],
        ["Hidden size", "256"],
        ["Encoder layers", "4"],
        ["Attention heads", "4"],
        ["Feed-forward size", "512"],
        ["Max sequence length", "32"],
        ["Dropout", "0.1"],
        ["Pooling", "Mean-pooling over non-padding token positions"],
        ["Output head", "Linear layer to 77 classes"],
    ],
    col_widths=[2.6, 3.7],
)

h("Implementation", 3)
p(
    "Forward pass: token embeddings and learned positional embeddings are summed, passed through 4 "
    "stacked TransformerEncoderLayers with a padding mask (src_key_padding_mask) derived from the "
    "attention mask so padded positions are ignored by self-attention, mean-pooled over valid (non-pad) "
    "token positions, and projected through a linear classifier to 77 logits."
)

h("Results — Parameter Breakdown", 3)
add_table(
    ["Component", "Parameters"],
    [
        ["Token + position embeddings", "776,192"],
        ["Transformer encoder (4 layers)", "2,108,416"],
        ["Classification head", "19,789"],
        ["Student total", "2,904,397"],
        ["Teacher total (bert-base-uncased)", "109,541,453"],
        ["Compression ratio", "37.7x fewer parameters"],
    ],
    col_widths=[3.4, 2.9],
)

h("Interpretation", 3)
p(
    "The Student uses 37.7x fewer parameters than the Teacher. Roughly 27% of that budget sits in the "
    "embedding table alone — the direct payoff of Task 2's small, domain-specific vocabulary (3,000 vs. "
    "BERT's 30,522 tokens). Because a Transformer's parameter count for short-sequence classification "
    "scales with vocab_size × hidden_size, shrinking the vocabulary is one of the single highest-leverage "
    "compression decisions available, independent of how many encoder layers are ultimately kept."
)

h("4.2 Task 4 — Distillation Loss Function", 2)

h("Design", 3)
p("The Student is trained with a combined objective:")
formula_block(
    "Loss = α · T² · KL(Pᵨtudent, Pᵀeacher at temperature T)  +  (1 − α) · CE(yᵨtudent, yᵀrue)"
)
p(
    "with temperature T = 4.0 and weighting α = 0.7. The KL term is computed between the Student's and "
    "Teacher's softmax distributions after both are divided by T (which softens both distributions and "
    "amplifies the small, informative probabilities on non-target classes); the T² multiplier (Hinton et "
    "al., 2015) compensates for the fact that raising T shrinks the magnitude of the gradients coming from "
    "the soft-label term by roughly 1/T² relative to the hard-label term, so without it the KD loss would "
    "be under-weighted once a large T is introduced. The CE term is ordinary cross-entropy against the "
    "true ground-truth label, ensuring the Student never loses sight of the actual classification "
    "objective even while learning to mimic the Teacher's distribution."
)

h("Implementation", 3)
bullets([
    "DistillationLoss(nn.Module): computes log_softmax(student_logits / T), softmax(teacher_logits / T), combines them via nn.KLDivLoss(reduction=\"batchmean\") scaled by T², and blends with nn.CrossEntropyLoss(student_logits, true_labels) using the α / (1−α) weights above.",
    "BankingStudentDataset joins each example's Student-tokenized input with its cached Teacher logits and true label by row index (the alignment strategy from Task 2).",
    "Training loop: AdamW optimizer, learning rate 3e-4, batch size 32, 8 epochs, executed entirely on CPU.",
])

h("Results", 3)
add_table(
    ["Epoch", "Total loss", "KD component", "CE component"],
    [
        ["1", "1.142", "0.556", "2.508"],
        ["2", "0.559", "0.306", "1.150"],
        ["3", "0.387", "0.217", "0.786"],
        ["4", "0.301", "0.174", "0.598"],
        ["5", "0.248", "0.148", "0.482"],
        ["6", "0.211", "0.132", "0.396"],
        ["7", "0.184", "0.118", "0.337"],
        ["8", "0.166", "0.110", "0.297"],
    ],
    col_widths=[1.0, 1.8, 1.9, 1.9],
)
p(f"Total distilled-student training time: 130.8 seconds on CPU (8 epochs, 10,003 examples).")
add_image(f"{ASSETS}/distill_loss_curves.png", width=6.2,
           caption="Figure 1. Distilled student training: total loss (left) and its KD vs. CE components (right) across 8 epochs.")

h("Interpretation", 3)
p(
    "The CE component drops faster and further than the KD component throughout training. This is "
    "expected: with only 3,000 vocabulary tokens and 4 layers, the Student can quickly memorize the "
    "single correct class for a small, reasonably well-separated training set, whereas matching the "
    "Teacher's full smoothed distribution over 77 classes at T=4 is a strictly harder target. The KD term "
    "keeps supplying a non-trivial gradient signal well after the CE term has largely converged — this is "
    "exactly the regime in which distillation contributes information beyond what hard labels alone would "
    "teach the Student."
)

doc.add_page_break()

# ---------------------------------------------------------------------------
# Module 3
# ---------------------------------------------------------------------------
h("5. Module 3: Comparative Analysis and Benchmarking", 1)

h("5.1 Task 5 — Accuracy vs. Compression Evaluation", 2)

h("Design", 3)
p(
    "To isolate the effect of distillation itself from the effect of the compact architecture, a second "
    "“baseline” Student is trained: identical architecture, identical tokenizer, identical optimizer "
    "and epoch budget as the distilled Student, but trained with plain cross-entropy against ground-truth "
    "labels only, with no Teacher signal at all. Any accuracy gap between the two Students is then "
    "attributable to distillation alone."
)

h("Implementation", 3)
p(
    "The baseline Student is trained with the same train_student() routine used for the distilled Student, "
    "but with distill=False, for 8 epochs, batch size 32, learning rate 3e-4, AdamW, on CPU (127.6 seconds "
    "total). All three models — Teacher, baseline Student, distilled Student — are then evaluated on "
    "the same held-out 3,080-example test set using scikit-learn's accuracy_score and f1_score (macro and "
    "weighted)."
)
add_image(f"{ASSETS}/baseline_vs_distilled_loss.png", width=5.0,
           caption="Figure 2. Training loss: baseline student (cross-entropy only) vs. distilled student (KD + CE). Note the two loss compositions are not directly comparable in scale.")

h("Results — Comparison Table", 3)
add_table(
    ["Model", "Accuracy", "Macro F1", "Weighted F1"],
    [
        ["Teacher (bert-base-uncased)", "87.56%", "0.868", "0.868"],
        ["Student — without distillation", "83.47%", "0.836", "0.836"],
        ["Student — with distillation", "87.44%", "0.874", "0.874"],
    ],
    col_widths=[3.0, 1.6, 1.4, 1.5],
)

h("Interpretation", 3)
p(
    "The undistilled Student, trained only on hard labels with a 37.7x smaller architecture, reaches "
    "83.5% accuracy — a substantial (4.1-point) gap below the Teacher's 87.6%, as expected given how "
    "much capacity was removed. Adding the Teacher's soft labels — with the architecture, data, and "
    "epoch budget held fixed, changing only the loss function — raises the Student to 87.4% accuracy, "
    "closing 97% of the accuracy gap between the undistilled Student and the Teacher. In this run, the "
    "distilled Student retains 99.9% of the Teacher's accuracy (and slightly exceeds it on Macro/Weighted "
    "F1) at a fraction of the parameter count. This is the central empirical claim of knowledge "
    "distillation being demonstrated directly: dark knowledge in the Teacher's soft labels lets a small "
    "model recover far more of a large model's decision surface than the same small model could learn "
    "from hard labels alone. It is worth noting the closeness of Teacher and distilled-Student accuracy "
    "also reflects that the Teacher itself is a modestly fine-tuned (3-epoch) model and the test set is a "
    "few thousand examples — a couple of points either way is within normal run-to-run variance, and this "
    "particular run should be read as “distillation closed essentially all of the accuracy gap,” not as "
    "proof the Student's internal representation matches the Teacher's."
)

h("5.2 Task 6 — Deployment Metrics Analysis", 2)

h("Design", 3)
p(
    "Accuracy alone does not determine deployability. Three deployment-relevant metrics are measured for "
    "the Teacher and the distilled Student: on-disk model size, CPU inference latency, and peak process "
    "RAM — the three resource axes that typically gate whether a model fits on an edge or mobile device."
)

h("Implementation", 3)
bullets([
    ["Disk size: ", True],
    "each model's state_dict is serialized with torch.save to a temporary file and measured with os.path.getsize.",
])
bullets([
    ["CPU latency: ", True],
    "both models are moved to CPU; after a short warm-up, single-example (batch size 1) forward passes are timed with time.perf_counter over 50 runs, reporting mean ± standard deviation in milliseconds per query.",
])
bullets([
    ["Peak RAM: ", True],
    "measured per model in an isolated subprocess (via resource.getrusage(RUSAGE_SELF).ru_maxrss) rather than in the shared notebook kernel. This is a deliberate methodological choice: peak RSS is monotonically non-decreasing for the life of a process, so if both models were loaded into the same long-lived kernel, the Teacher's much larger footprint would contaminate any “peak RAM” reading taken afterward for the Student. Running each model's load-and-infer cycle in its own fresh subprocess gives a fair, isolated reading for each.",
])

h("Results — Deployment Metrics", 3)
add_table(
    ["Metric", "Teacher", "Student", "Reduction"],
    [
        ["Disk size (MB)", "417.9", "11.1", "37.7x"],
        ["CPU latency (ms/query)", "27.76 ± 3.07", "1.18 ± 0.09", "23.5x"],
        ["Peak RAM (MB, isolated process)", "893.8", "237.0", "3.8x"],
    ],
    col_widths=[3.0, 1.7, 1.7, 1.3],
)

h("Interpretation — Deployment Readiness", 3)
p(
    "The distilled Student is 37.7x smaller on disk, 23.5x faster per CPU query, and uses 3.8x less peak "
    "RAM than the Teacher, while retaining 99.9% of its accuracy on the same 77-way classification task. "
    "In this run there is essentially no accuracy trade-off to weigh against those savings — which is a "
    "favourable outcome rather than a guarantee, since it partly reflects a lightly fine-tuned Teacher and "
    "a modest-size test set; a production rollout should still monitor accuracy on live traffic rather "
    "than assuming this margin holds indefinitely as the input distribution drifts."
)
bullets([
    ["Size and RAM. ", True],
    "At ~11MB on disk and ~237MB of peak RAM, the Student comfortably fits within the memory budgets of edge devices and mobile apps, where a 418MB+ BERT-base checkpoint is frequently a non-starter (app-store bundle-size limits, low-RAM Android devices, on-device model caches).",
])
bullets([
    ["Latency. ", True],
    "1.2ms/query on CPU is well within the range needed for a responsive, synchronous UI interaction (e.g. intent routing as a user types), whereas the Teacher's 27.8ms/query, multiplied across a request queue on a resource-constrained device, would noticeably degrade perceived responsiveness.",
])
bullets([
    ["Practical recommendation. ", True],
    "Deploy the distilled Student as the default path, and route low-confidence predictions (small margin between the top-2 softmax probabilities) to the Teacher or a human reviewer. This captures most of the demonstrated size/latency/RAM benefits while bounding accuracy risk to only the genuinely ambiguous cases — exactly the scenario dark-knowledge distillation is suited for, since the Student was trained to mimic the Teacher's confidence structure, not just its arg-max.",
])
bullets([
    ["When the Student alone would not suffice. ", True],
    "For a fully autonomous decision with no fallback path (e.g. auto-approving a refund), any residual gap to the Teacher — even a small one — may still argue for keeping the Teacher, or a human, in the loop.",
])

doc.add_page_break()

# ---------------------------------------------------------------------------
# 6. Conclusion
# ---------------------------------------------------------------------------
h("6. Conclusion and Key Takeaways", 1)
bullets([
    ["Dark knowledge transfers real signal. ", True],
    "Distillation closed roughly 97% of the accuracy gap between an undistilled and a distilled Student sharing the same 37.7x-smaller architecture, using only a change of loss function — no additional data, parameters, or training time.",
])
bullets([
    ["Vocabulary size is a first-order compression lever. ", True],
    "A domain-trained, 10x-smaller Student tokenizer removed roughly a quarter of the Student's total parameter budget on its own, independent of encoder depth or width.",
])
bullets([
    ["Sequence-classification distillation avoids the hardest alignment problem. ", True],
    "Because the Teacher produces one probability vector per example rather than per token, Teacher/Student tokenizer mismatch only requires row-level index alignment, not token-level alignment — a substantially simpler engineering problem than seq2seq or token-classification distillation would pose.",
])
bullets([
    ["The compression/latency/RAM payoff is large and the accuracy cost, in this run, is negligible. ", True],
    "37.7x smaller on disk, 23.5x faster on CPU, 3.8x less peak RAM, for 99.9% of the Teacher's test accuracy — making the distilled Student a strong candidate for edge or mobile deployment, ideally paired with a confidence-based fallback to the Teacher for the hardest cases.",
])

# ---------------------------------------------------------------------------
# 7. Environment & Reproducibility
# ---------------------------------------------------------------------------
h("7. Environment and Reproducibility", 1)
add_table(
    ["Item", "Value"],
    [
        ["Conda environment", "agn_env"],
        ["Python version", "3.12.8"],
        ["Key libraries", "torch, transformers, datasets, tokenizers, scikit-learn, psutil, accelerate, evaluate"],
        ["Hardware", "Apple Silicon (M-series), MPS acceleration for Teacher fine-tuning"],
        ["Teacher training device", "MPS"],
        ["Student training/inference device", "CPU (by design, matching the deployment target)"],
        ["Random seed", "42 (Python, NumPy, PyTorch)"],
        ["Source artifact", "knowledge_distillation_assignment.ipynb (single, end-to-end executed notebook)"],
    ],
    col_widths=[2.4, 3.9],
)
p(
    "The notebook can be re-executed top-to-bottom via jupyter nbconvert --to notebook --execute --inplace "
    "knowledge_distillation_assignment.ipynb inside the agn_env environment. Minor variation (typically "
    "within 1–2 accuracy points) between runs is expected due to non-deterministic operations in "
    "MPS-accelerated training and dataloader shuffling order."
)

doc.save("/Users/reevechaitanya/Documents/2_Experimentation_n_Research/demo/Knowledge_Distillation_Report.docx")
print("Report written.")