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Minquan Alt.Github.Io Perplexity Rules
minquan-alt July 19, 2026
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// ===========================
// Project Data
// ===========================
const projectsData = {
1: {
id: 1,
title: "Advanced SLM Reasoning",
subtitle: "Improving 1B-parameter language model reasoning via SFT + RL under constrained compute",
tags: ["LLM", "SLM", "SFT", "Reinforcement Learning", "LoRA", "Quantization", "PyTorch", "HuggingFace"],
github: "https://github.com/minquan-alt/AdvancedLLMReasoning",
demo: "",
teamSize: 1,
role: "Individual Project",
problem: {
title: "Problem & Research Context",
icon: "fa-lightbulb",
content: `
<h3>Research Problem</h3>
<div style="padding: 1rem; background: var(--bg-tertiary); border-left: 4px solid var(--accent-primary); margin-bottom: 1.5rem;">
<p><strong>How can a Small Language Model (SLM) achieve mathematical reasoning performance comparable to medium- and large-scale models under resource constraints?</strong></p>
</div>
<h3>The SLM Reasoning Gap</h3>
<ul>
<li>Small Language Models (~1B parameters), while efficient in cost and latency, struggle with structured multi-step reasoning compared to larger models (7B+).</li>
<li>On mathematical reasoning benchmarks such as GSM8K and MATH500, small models frequently exhibit unstable reasoning chains, shallow pattern matching, and arithmetic inconsistencies.</li>
<li>Simply scaling model size is not always feasible due to memory, latency, and deployment constraints — especially in low-resource or edge scenarios.</li>
</ul>
<h3>Why This Matters</h3>
<ul>
<li><strong>Weak Performance:</strong> Small language models struggle with complex pattern recognition and multi-step reasoning. Particularly in mathematics, they suffer from arithmetic errors where the logical approach is correct but computational results are wrong.</li>
<li><strong>Accessibility:</strong> Not everyone has access to high-end GPUs or API budgets for large models. Some applications must run on low-resource devices (mobile, edge devices).</li>
<li><strong>Resource Constraints:</strong>
A 1B model in FP32 requires ~4GB for weights alone.
In practice, training requires significantly more memory due to gradients and optimizer states,
making full fine-tuning impractical on consumer hardware without parameter-efficient methods.</li>
</ul>
<h3>Solution Approach</h3>
<p><strong>Hypothesis:</strong>
Reasoning ability in small models can be enhanced through structured training interventions
without increasing parameter count.
</p>
<ol>
<li><strong>Supervised Fine-Tuning (SFT)</strong> on curated Chain-of-Thought data to encourage explicit reasoning steps.</li>
<li><strong>Reinforcement Learning (RL)</strong> with correctness-based reward signals to improve answer consistency and discourage shortcut patterns.</li>
<li><strong>Parameter-Efficient Training (LoRA)</strong> to enable adaptation under limited GPU memory.</li>
<li><strong>Optional Tool Augmentation</strong> (code execution) to verify arithmetic correctness and reduce calculation errors.</li>
</ol>
<div class="deployment-info">
<h4>Research Goals</h4>
<p>Improve reasoning accuracy on GSM8K and MATH500 benchmarks</p>
<p>Maintain model size at ~1B parameters (deployable on consumer GPUs)</p>
<p>Use memory-efficient training methods (LoRA, quantization)</p>
<p>Document full training pipeline for reproducibility</p>
</div>
<p style="margin-top: 1rem; padding: 1rem; background: var(--bg-tertiary); border-radius: 6px;">
<strong>💡 Key Insight:</strong> This is not just about chasing benchmark scores. The goal is to demonstrate
that <strong>systematic training strategies</strong> (SFT + RL + efficient methods) can meaningfully close the
performance gap for resource-constrained scenarios.
</p>
`
},
data: {
title: "Data Collection & Processing Pipeline",
icon: "fa-database",
content: `
<h3>Two-Stage Data Pipeline Overview</h3>
<p>
Building a high-quality SFT dataset requires careful curation and preprocessing. The pipeline consists of two main stages:
<strong>Data Collection</strong> (filtering and curating from raw sources) and <strong>Data Processing</strong> (formatting for model training).
</p>
<h4>Stage 1: Data Collection & Curation</h4>
<p>Building the SFT dataset from raw mathematical problem sources:</p>
<img src="/images/project-1/data-collection.png" alt="Data Collection Pipeline" style="width: 100%; max-width: 800px; margin: 1.5rem auto; display: block; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Pipeline Flow:</strong> OpenMathInstruct-1 → Correct Filter → Subsetting → Branch (GSM8K / MATH) → Filtering & Standardizing → Final SFT Dataset</p>
</div>
<h5>1️⃣ Source Dataset</h5>
<div class="data-info">
<div class="data-info-item">
<strong>Dataset</strong>
<span>OpenMathInstruct-1</span>
</div>
<div class="data-info-item">
<strong>Original Size</strong>
<span>~1.8M samples</span>
</div>
<div class="data-info-item">
<strong>Content</strong>
<span>Math problems + solutions</span>
</div>
</div>
<p>Large-scale mathematical reasoning dataset with Chain-of-Thought solutions. Contains problems from multiple sources (GSM8K, MATH, and synthetic generation).</p>
<h5>2️⃣ Correct Filter</h5>
<p><strong>Purpose:</strong> Keep only correct solutions</p>
<ul>
<li><strong>Validation:</strong> Check final answer against ground truth</li>
<li><strong>Code Execution:</strong> For problems with Python code, verify execution results</li>
<li><strong>Format Check:</strong> Ensure answers are properly boxed (\\boxed{answer})</li>
</ul>
<p>🎯 <strong>Goal:</strong> Prevent model from learning incorrect reasoning patterns</p>
<h5>3️⃣ Subsetting</h5>
<p><strong>Why subset?</strong></p>
<ul>
<li><strong>Resource Constraints:</strong> Training on 1.8M samples requires significant compute</li>
<li><strong>Balance:</strong> Avoid over-representation of certain problem types</li>
<li><strong>Training Efficiency:</strong> Target ~500K samples for optimal coverage vs cost</li>
</ul>
<h5>4️⃣ Branch Processing</h5>
<p>Split into two tracks based on dataset origin:</p>
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: 1rem; margin: 1rem 0;">
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px;">
<h6>🔹 GSM8K Branch</h6>
<p><strong>Process:</strong> GSM8K → Fair Sampling → Subset GSM8K</p>
<ul>
<li><strong>Fair Sampling:</strong> Stratified by difficulty and problem type</li>
<li><strong>Coverage:</strong> Ensure all arithmetic operations represented</li>
<li><strong>Result:</strong> ~300K balanced samples</li>
</ul>
</div>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px;">
<h6>🔸 MATH Branch</h6>
<p><strong>Process:</strong> MATH → Code Filtering → Standardizing → Subset MATH</p>
<ul>
<li><strong>Code Filtering:</strong> Remove solutions with invalid/incomplete code</li>
<li><strong>Standardizing:</strong> Normalize answer format (LaTeX, boxed notation)</li>
<li><strong>Result:</strong> ~200K high-quality samples</li>
</ul>
</div>
</div>
<h5>5️⃣ Final SFT Dataset</h5>
<div class="data-info">
<div class="data-info-item">
<strong>Total Size</strong>
<span>512K samples</span>
</div>
<div class="data-info-item">
<strong>GSM8K</strong>
<span>~300K (58%)</span>
</div>
<div class="data-info-item">
<strong>MATH</strong>
<span>~200K (40%)</span>
</div>
<div class="data-info-item">
<strong>Quality</strong>
<span>100% correct solutions</span>
</div>
</div>
<h4>Stage 2: Data Processing for Training</h4>
<p>Transform curated dataset into training-ready format:</p>
<img src="/images/project-1/data-processing.png" alt="Data Processing Pipeline" style="width: 100%; max-width: 800px; margin: 1.5rem auto; display: block; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Pipeline Flow:</strong> SFT Dataset → Split → Apply Llama 3.2 Chat Template → Dual Tokenization → Masking → Processed Datasets</p>
</div>
<h5>1️⃣ Dataset Split</h5>
<table class="experiment-table">
<thead>
<tr>
<th>Split</th>
<th>Size</th>
<th>Purpose</th>
</tr>
</thead>
<tbody>
<tr>
<td>train_ds</td>
<td>99% (~507K)</td>
<td>Model training with gradient updates</td>
</tr>
<tr>
<td>val_ds</td>
<td>0.5% (~2.5K)</td>
<td>Hyperparameter tuning, early stopping</td>
</tr>
<tr>
<td>test_ds</td>
<td>0.5% (~2.5K)</td>
<td>Final evaluation (not used during training)</td>
</tr>
</tbody>
</table>
<h5>2️⃣ Apply Llama 3.2 Chat Template</h5>
<p><strong>Convert to standardized chat format:</strong></p>
<div class="code-block">
<pre><code><|start_header_id|>system<|end_header_id|>
You are a math reasoning assistant.
Solve the problem step by step.
Output ONLY the final number inside \\boxed{}.<|eot_id|>
<|start_header_id|>user<|end_header_id|>
[Problem statement]<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
[Step-by-step solution with \\boxed{answer}]<|eot_id|></code></pre>
</div>
<p><strong>Why chat template?</strong></p>
<ul>
<li>Llama 3.2 expects conversation format with special tokens</li>
<li>Enables role-based instructions (system, user, assistant)</li>
<li>Better alignment with how chat models are typically used</li>
</ul>
<h5>3️⃣ Dual Tokenization</h5>
<p><strong>Separate tokenization for input and labels:</strong></p>
<ul>
<li><strong>Input IDs:</strong> Full conversation (system + user + assistant) tokenized</li>
<li><strong>Labels:</strong> Same as input_ids but with masking applied (next step)</li>
<li><strong>Max Length:</strong> 1024 tokens (truncate longer sequences)</li>
<li><strong>Tokenizer:</strong> Llama 3.2 tokenizer with BPE encoding</li>
</ul>
<h5>4️⃣ Masking (Label Masking)</h5>
<p><strong>Critical step:</strong> Only compute loss on assistant's response</p>
<div class="code-block">
<pre><code># Pseudocode for masking
labels = input_ids.clone()
# Mask system prompt
labels[:system_end] = -100
# Mask user prompt
labels[system_end:user_end] = -100
# Keep assistant response (this is what we train on)
labels[user_end:assistant_end] = input_ids[user_end:assistant_end]</code></pre>
</div>
<p><strong>Why masking?</strong></p>
<ul>
<li>Prevent model from "learning" to predict user prompts (waste of compute)</li>
<li>Focus training on generating correct solutions</li>
<li>Standard practice in instruction tuning</li>
</ul>
<h5>5️⃣ Processed Datasets Output</h5>
<p>Final datasets ready for Trainer:</p>
<ul>
<li><strong>train_processed_dataset:</strong> 507K samples with input_ids, attention_mask, labels</li>
<li><strong>val_processed_dataset:</strong> 2.5K samples for validation during training</li>
<li><strong>test_processed_dataset:</strong> 2.5K samples for final evaluation</li>
</ul>
<h3>Evaluation Benchmarks</h3>
<p>After training, models are tested on held-out benchmarks (not in training data):</p>
<table class="experiment-table">
<thead>
<tr>
<th>Benchmark</th>
<th>Size</th>
<th>Difficulty</th>
<th>Coverage</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>GSM8K</strong></td>
<td>1,319 problems</td>
<td>Grade-school level</td>
<td>Arithmetic reasoning, word problems</td>
</tr>
<tr>
<td><strong>MATH500</strong></td>
<td>500 problems</td>
<td>Competition level</td>
<td>Algebra, geometry, number theory, calculus</td>
</tr>
</tbody>
</table>
<div class="key-insight">
<h4>💡 Key Takeaways</h4>
<ul>
<li><strong>Quality over Quantity:</strong> Filtered from 1.8M → 512K by removing incorrect solutions and balancing problem types</li>
<li><strong>Two-Track Processing:</strong> GSM8K (fair sampling) vs MATH (code filtering + standardizing) ensures diverse coverage</li>
<li><strong>Chat Template:</strong> Converting to Llama 3.2 format enables proper role-based instruction following</li>
<li><strong>Label Masking:</strong> Only training on assistant responses (not prompts) improves efficiency and prevents overfitting</li>
</ul>
</div>
`
},
architecture: {
title: "Model & Training Strategy",
icon: "fa-project-diagram",
content: `
<h3>Base Model</h3>
<ul>
<li><strong>Architecture:</strong> Llama 3.2-1B (1 billion parameters)</li>
<li><strong>Context Length:</strong> 128K tokens (training with 1024 max length)</li>
</ul>
<h3>Two-Stage Training Pipeline</h3>
<h4>Stage 1: Supervised Fine-Tuning (SFT)</h4>
<p>
Transform the base model into a math reasoning specialist through supervised learning on curated Chain-of-Thought data.
The SFT pipeline combines efficient training techniques (LoRA + quantization) with careful evaluation loops.
</p>
<img src="/images/project-1/SFT.png" alt="SFT Training Pipeline" style="width: 100%; max-width: 800px; margin: 1.5rem auto; display: block; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Training Flow:</strong> Base Model → Quantization → LoRA → Trainer (+ datasets) → Checkpoint → Evaluation → Policy Model</p>
</div>
<h4>1️⃣ Base Model (Llama-3.2-1B)</h4>
<ul>
<li><strong>Starting Point:</strong> Pretrained Llama 3.2 with 1B parameters</li>
<li><strong>Capabilities:</strong> General language understanding, instruction following foundation</li>
<li><strong>Limitation:</strong> Not optimized for mathematical reasoning (baseline: GSM8K 44.4%, MATH500 23.9%)</li>
</ul>
<h4>2️⃣ Quantization (4-bit NF4)</h4>
<p><strong>Applied before fine-tuning to reduce memory footprint:</strong></p>
<ul>
<li><strong>Method:</strong> 4-bit NormalFloat Quantization (NF4) from bitsandbytes</li>
<li><strong>Memory Reduction:</strong> 4GB → 1GB (~75% savings)</li>
<li><strong>Impact:</strong> Enables training on single GPU (RTX 4090 24GB) instead of requiring multi-GPU setup</li>
<li><strong>Preparation for QLoRA:</strong> Quantized base + LoRA adapters = efficient fine-tuning</li>
</ul>
<div class="code-block">
<pre><code>from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True
)</code></pre>
</div>
<h4>3️⃣ LoRA (Low-Rank Adaptation)</h4>
<p><strong>Parameter-Efficient Fine-Tuning strategy:</strong></p>
<ul>
<li><strong>Concept:</strong> Instead of updating all 1B parameters, train small adapter matrices (rank r=16)</li>
<li><strong>Trainable Parameters:</strong> Only 0.4% of total parameters (~4M out of 1B)</li>
<li><strong>Target Modules:</strong> Apply LoRA to attention projection layers (q, k, v, o) and MLP layers (gate, up, down)</li>
<li><strong>Benefits:</strong>
<ul>
<li>99.6% reduction in gradient memory</li>
<li>Faster training (2x-3x speedup)</li>
<li>Smaller checkpoint files (~20MB vs 4GB)</li>
<li>Easy to merge back into base model or swap adapters</li>
</ul>
</li>
</ul>
<div class="code-block">
<pre><code># LoRA Configuration
r = 16 # Rank (higher = more capacity, slower)
alpha = 32 # Scaling factor (typically 2 × r)
target_modules = [
"q_proj", "v_proj", "k_proj", "o_proj", # Attention
"gate_proj", "up_proj", "down_proj" # MLP
]
lora_dropout = 0.1 # Regularization</code></pre>
</div>
<h4>4️⃣ Trainer (Optimize SFT Model)</h4>
<p><strong>Training loop with data inputs:</strong></p>
<ul>
<li><strong>Inputs:</strong>
<ul>
<li>train_processed_dataset (507K samples)</li>
<li>val_processed_dataset (2.5K samples)</li>
<li>Model (quantized Llama 3.2 + LoRA adapters)</li>
</ul>
</li>
<li><strong>Objective:</strong> Minimize cross-entropy loss on assistant responses (labels masked for prompts)</li>
<li><strong>Optimization:</strong> Backpropagation updates only LoRA parameters, base model frozen</li>
<li><strong>Validation:</strong> Monitor validation loss every 100 steps, log metrics for early stopping</li>
</ul>
<div class="code-block">
<pre><code># Training Hyperparameters
epochs = 2
learning_rate = 2e-4
batch_size = 8 (per device)
gradient_accumulation = 4 # Effective batch = 32
optimizer = paged_adamw_8bit # Memory-efficient optimizer
lr_scheduler = cosine with 3% warmup
max_grad_norm = 1.0 # Gradient clipping</code></pre>
</div>
<h4>5️⃣ Checkpoint (SFT Model)</h4>
<p><strong>Periodic model saving during training:</strong></p>
<ul>
<li><strong>Frequency:</strong> Save checkpoint every 500 steps or at end of each epoch</li>
<li><strong>Contents:</strong> LoRA adapter weights (not full model), optimizer state, training metadata</li>
<li><strong>Purpose:</strong>
<ul>
<li>Resume training if interrupted</li>
<li>Evaluate multiple checkpoints to find best performing</li>
<li>Prevent loss of progress from crashes</li>
</ul>
</li>
<li><strong>Storage:</strong> Each checkpoint ~20MB (LoRA only) vs 4GB (full model)</li>
</ul>
<h4>6️⃣ Evaluation (SFT)</h4>
<p><strong>Assess checkpoint quality on validation set:</strong></p>
<ul>
<li><strong>Metrics Tracked:</strong>
<ul>
<li>Validation loss (primary signal)</li>
<li>Perplexity (model confidence)</li>
<li>Accuracy on validation subset (exact match)</li>
</ul>
</li>
<li><strong>Decision Points:</strong>
<ul>
<li>If validation loss plateaus → early stopping</li>
<li>If validation loss diverges from train loss → overfitting, reduce learning rate</li>
<li>If metrics improve → continue training or save as best checkpoint</li>
</ul>
</li>
<li><strong>Benchmark Testing:</strong> Best checkpoint evaluated on GSM8K and MATH500 test sets (held out)</li>
</ul>
<h4>7️⃣ Policy Model (Version 1)</h4>
<p><strong>Final output of SFT stage:</strong></p>
<ul>
<li><strong>What it is:</strong> Base model + trained LoRA adapters, ready for inference or next stage (GRPO)</li>
<li><strong>Performance:</strong> GSM8K 51.6% (+7.2pp), MATH500 26% (+2.1pp) over base</li>
<li><strong>Usage:</strong>
<ul>
<li>Direct deployment for math reasoning tasks</li>
<li>Starting point for Stage 2 RL training (GRPO)</li>
<li>Can be merged back into full model for deployment without adapter overhead</li>
</ul>
</li>
</ul>
<h4>Stage 2: Group Relative Policy Optimization (GRPO)</h4>
<p><em>(In progress, not included in current results)</em></p>
<p>
After SFT, further improve reasoning via reinforcement learning with correctness-based rewards.
GRPO enables the model to explore alternative solution paths and learn from self-generated feedback.
</p>
<h3>Memory Optimization Summary</h3>
<table class="experiment-table">
<thead>
<tr>
<th>Technique</th>
<th>Purpose</th>
<th>Memory Savings</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>4-bit Quantization (NF4)</strong></td>
<td>Reduce weight memory</td>
<td>~75% (4GB → 1GB)</td>
</tr>
<tr>
<td><strong>LoRA (r=16)</strong></td>
<td>Train only 0.4% of parameters</td>
<td>Gradients: 99.6% reduction</td>
</tr>
<tr>
<td><strong>Paged AdamW 8-bit</strong></td>
<td>Reduce optimizer state memory</td>
<td>~50% optimizer memory</td>
</tr>
</tbody>
</table>
<p style="margin-top: 1rem; padding: 1rem; background: var(--bg-tertiary); border-radius: 6px;">
<strong>Hardware Requirements:</strong> Trainable on a single <strong>RTX 4090 (24GB)</strong> with these optimizations.
Full fine-tuning of 1B model would require more VRAM. These techniques make advanced LLM training accessible on consumer hardware.
</p>
<div class="key-insight">
<h4>💡 SFT Pipeline Philosophy</h4>
<p>
This isn't just "throw data at a model and hope". Each stage is carefully designed:
</p>
<ul>
<li><strong>Quantization:</strong> Makes training feasible on limited hardware without significant accuracy loss</li>
<li><strong>LoRA:</strong> Enables fast iteration and experimentation (train in hours, not days)</li>
<li><strong>Checkpointing:</strong> Allows evaluation at multiple training points to find optimal stopping point</li>
<li><strong>Evaluation Loop:</strong> Prevents overfitting and validates improvements on held-out data</li>
</ul>
<p>
Result: A math-specialized model trained on consumer GPU in ~12 hours, achieving competitive performance with models 10x larger.
</p>
</div>
`
},
experiments: {
title: "Experiments & Results",
icon: "fa-chart-bar",
content: `
<h3>Final Benchmark Results</h3>
<table class="experiment-table">
<thead>
<tr>
<th>Dataset</th>
<th>Baseline (Llama 3.2-1B)</th>
<th>SFT Only (v3)</th>
<th>Absolute Gain</th>
<th>Relative Improvement</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>GSM8K</strong></td>
<td>44.4%</td>
<td class="best-result"><strong>51.6%</strong></td>
<td>+7.2%</td>
<td>+16.2%</td>
</tr>
<tr>
<td><strong>MATH500</strong></td>
<td>23.9%</td>
<td class="best-result"><strong>26.0%</strong></td>
<td>+2.1%</td>
<td>+8.8%</td>
</tr>
</tbody>
</table>
<div class="metrics-grid">
<div class="metric-card">
<span class="metric-value">51.6%</span>
<span class="metric-label">GSM8K Accuracy</span>
</div>
<div class="metric-card">
<span class="metric-value">26.0%</span>
<span class="metric-label">MATH500 Accuracy</span>
</div>
<div class="metric-card">
<span class="metric-value">680/1319</span>
<span class="metric-label">GSM8K Correct</span>
</div>
<div class="metric-card">
<span class="metric-value">130/500</span>
<span class="metric-label">MATH500 Correct</span>
</div>
</div>
<h3>Ablation Studies</h3>
<table class="experiment-table">
<thead>
<tr>
<th>Configuration</th>
<th>GSM8K</th>
<th>MATH500</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>v0 (Basic SFT)</td>
<td>31.5%</td>
<td>-</td>
<td>Simple prompt format</td>
</tr>
<tr>
<td>v1 (Instruction Tuning)</td>
<td>38.7%</td>
<td>-</td>
<td>Manual masking</td>
</tr>
<tr>
<td>v2 (Chat Template)</td>
<td>50.5%</td>
<td>19.4%</td>
<td>Chat Template + proper masking</td>
</tr>
<tr>
<td class="best-result">v3 (Chat Template + Improved Format)</td>
<td class="best-result"><strong>51.6%</strong></td>
<td class="best-result"><strong>26.0%</strong></td>
<td>Llama 3.2 chat format + proper masking + refined answer extraction</td>
</tr>
</tbody>
</table>
<h3>Error Analysis</h3>
<p>Analysis of failure cases revealed systematic error patterns:</p>
<ul>
<li><strong>Format Mismatch (32%):</strong> Correct logic but output format differs from expected
<ul>
<li>Example: Model outputs <code>[5]</code> instead of <code>x=5</code></li>
<li>Example: <code>2*k + 2</code> vs <code>2k+2</code> (spacing differences)</li>
<li>Example: <code>sqrt(3)/3</code> vs <code>\\frac{\\sqrt{3}}{3}</code></li>
</ul>
</li>
<li><strong>Numeric vs Symbolic (24%):</strong> Model prefers numeric answers over symbolic
<ul>
<li>Example: <code>-0.5236</code> instead of <code>-π/6</code></li>
<li>Solution: Add symbolic math emphasis in training data</li>
</ul>
</li>
<li><strong>Reasoning Errors (28%):</strong> Genuine mathematical mistakes
<ul>
<li>Incorrect formula application</li>
<li>Arithmetic mistakes in multi-step problems</li>
</ul>
</li>
<li><strong>Other (16%):</strong> Ambiguous problems, parsing errors, edge cases</li>
</ul>
<p style="margin-top: 1rem; padding: 1rem; background: var(--bg-tertiary); border-radius: 6px;">
<strong>💡 Key Insight:</strong> ~56% of errors are format-related rather than reasoning failures. This suggests significant room for improvement through:
</p>
<ul>
<li><strong>Better answer extraction:</strong> Improved regex patterns to handle format variations</li>
<li><strong>Tool use (future work):</strong> Code execution for verification and reducing arithmetic errors</li>
<li><strong>Symbolic math training:</strong> Add more symbolic expressions (π, √, fractions) in training data</li>
<li><strong>Format normalization:</strong> Post-process outputs to standardize spacing and notation</li>
</ul>
<h3>Training Dynamics</h3>
<ul>
<li><strong>Training Time:</strong> ~12 hours for 2 epochs on RTX 4090 24GB (512K samples, batch size 8 with gradient accumulation 4)</li>
<li><strong>Convergence:</strong> Eval loss plateaued after ~1.5 epochs</li>
<li><strong>Best Checkpoint:</strong> Selected based on lowest validation loss (checkpoint at epoch 1, step ~15K)</li>
<li><strong>Peak VRAM Usage:</strong> ~18GB (model + optimizer states + gradients)</li>
</ul>
`
},
deployment: {
title: "Engineering & Reproducibility",
icon: "fa-rocket",
content: `
<h3>Code Structure</h3>
<div class="code-block">
<pre><code>AdvancedLLMReasoning/
├── data_utils/
│ ├── collect_data/ # Dataset collection scripts
│ └── data_processing/
│ ├── v0.py # Basic SFT format
│ ├── v2.py # Instruction tuning
│ └── v3.py # Chat template (final)
├── exp/
│ ├── sft_exp.py # Supervised fine-tuning
│ └── grpo_exp.py # RL training
├── utils/
│ ├── prompt.py # System prompts
│ └── inference_utils.py # Evaluation helpers
├── results/ # Benchmark outputs
│ ├── result_sft_gsm8k_v3.json
│ └── result_sft_math_v3.json
└── math_tutor_model/
└── math_sft_adapter/ # LoRA checkpoints
├── v0/ v1/ v2/
└── v3/final_checkpoint/</code></pre>
</div>
<h3>Training Command</h3>
<div class="code-block">
<pre><code># Stage 1: Supervised Fine-Tuning
python exp/sft_exp.py --data_path 3
# Stage 2: Reinforcement Learning (GRPO) (in progress)</code></pre>
</div>
<h3>Compute Requirements</h3>
<table class="experiment-table">
<thead>
<tr>
<th>Stage</th>
<th>GPU</th>
<th>VRAM</th>
<th>Time</th>
</tr>
</thead>
<tbody>
<tr>
<td>SFT (512K samples, 2 epochs)</td>
<td>RTX 4090 24GB</td>
<td>~18GB peak</td>
<td>~12 hours</td>
</tr>
<tr>
<td>Inference (4-bit quantized)</td>
<td>RTX 4090 / Consumer GPU</td>
<td>~1-2GB</td>
<td>~0.5s per problem</td>
</tr>
</tbody>
</table>
<h3>Key Engineering Decisions</h3>
<ul>
<li><strong>Why use DataCollator:</strong> Combine with group_by_length, this technique reduces padding waste by batching similar-length sequences together.</li>
<li><strong>Why 4-bit NF4 quantization:</strong> NF4 (NormalFloat4) is optimized for weight distributions that follow a normal distribution, which is typical for LLM parameters. Research shows NF4 preserves more information than uniform quantization at the same bit-width.</li>
<li><strong>Why LoRA over full fine-tuning:</strong> Enables rapid experimentation (~12 hours per run vs days) and makes training feasible on consumer GPUs (24GB vs 80GB+ required).</li>
</ul>
<h3>Resources</h3>
<a href="https://github.com/minquan-alt/AdvancedLLMReasoning" target="_blank" class="github-link">
<i class="fab fa-github"></i>
View Full Source Code
</a>
`
}
},
2: {
id: 2,
title: "SignLearn - AI Sign Language Platform",
subtitle: "AI-powered Vietnamese sign language learning with real-time recognition and RAG chatbot",
tags: ["FastAPI", "ONNX", "MediaPipe", "Gemini 2.5", "Qdrant", "LangChain"],
github: "https://github.com/quangminh/signlearn",
demo: null,
teamSize: 4,
role: "Member (RAG Lead)",
problem: {
title: "Problem & My Role",
icon: "fa-lightbulb",
content: `
<div class="deployment-info" style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 1.5rem; border-radius: 8px; margin-bottom: 2rem;">
<h4 style="color: white; margin-top: 0;">🎯 My Contributions</h4>
<p style="margin: 0.5rem 0; font-size: 1.05rem;"><strong>Primary Responsibility:</strong> Design & implement the RAG Chatbot subsystem, Knowledge Base and backend orchestration for a multi-agent sign-learning platform.</p>
<ul style="margin: 0.5rem 0; padding-left: 1.5rem;">
<li>Designed and implemented the RAG pipeline (Embedding → Qdrant → Reranking → LLM generation)</li>
<li>Built the VSL knowledge base (PDF parsing, chunking, embedding, indexing)</li>
<li>Implemented LLM-as-Judge evaluation framework and automated quality checks</li>
<li>Integrated Gemini 2.5 Pro with a streaming ADK agent and FastAPI backend</li>
<li>Defined MCP protocol for tool↔agent interactions and measured tool-level metrics</li>
</ul>
<p style="margin: 0.5rem 0; font-style: italic;">Note: Real-time sign recognition (pose/keypoint → ONNX model) was implemented by teammates; my work focused on the RAG & agent/tool orchestration.</p>
</div>
<h3>The Challenge: Scalable, Grounded VSL Learning</h3>
<p>
Vietnam has > <strong>2.5M people with hearing impairments</strong> but only ~400 trained teachers.
Learners need both <em>accurate</em> explanations and <em>interactive</em> guidance — not just classifier outputs.
</p>
<h3>Core Problems</h3>
<ul>
<li><strong>Fragmented knowledge:</strong> authoritative textbooks and lesson modules are disconnected — no single Q&A surface.</li>
<li><strong>Hallucination risk:</strong> pure LLM answers without grounding produce incorrect or misleading explanations.</li>
<li><strong>No scalable evaluation:</strong> manual annotation is slow; we need continuous automated quality checks.</li>
<li><strong>Multi-agent coordination:</strong> sign-recognition (low-latency inference) must run alongside cloud RAG services without blocking UX.</li>
</ul>
<h3>Design Objective</h3>
<p>
Deliver a grounded AI Tutor (RAG + LLM) that provides context-aware explanations, measurable quality (relevance/faithfulness),
and sub-5s end-to-end response time while coexisting with the real-time sign-feedback service.
</p>
`
},
// --- Data (aligned with Search Engine image & Key Insight) ---
data: {
title: "RAG Knowledge Base",
icon: "fa-database",
content: `
<h3>Knowledge Base Construction</h3>
<p>Compiled authoritative VSL resources into two focused collections to support retrieval strategies used in the Search Engine diagram:</p>
<div class="data-info">
<div class="data-info-item">
<strong>Source Documents</strong>
<span>VSL textbook (~200 pages) + 20 structured lesson modules</span>
</div>
<div class="data-info-item">
<strong>Chunking</strong>
<span>1024 tokens / chunk, 100 token overlap (preserve cross-chunk context)</span>
</div>
<div class="data-info-item">
<strong>Total Chunks</strong>
<span>~2,500 indexed chunks</span>
</div>
<div class="data-info-item">
<strong>Embedding</strong>
<span>EmbeddingGemma-300M → 768-dim vectors</span>
</div>
</div>
<h3>Vector DB & Indexing</h3>
<table class="config-table">
<thead>
<tr><th>Component</th><th>Technology</th><th>Config</th></tr>
</thead>
<tbody>
<tr><td>Vector DB</td><td>Qdrant Cloud</td><td>EU-West-2, 768-dim vectors</td></tr>
<tr><td>Embedding Model</td><td>EmbeddingGemma-300M</td><td>Optimized for Vietnamese</td></tr>
<tr><td>Distance Metric</td><td>Cosine</td><td>Semantic search stability</td></tr>
<tr><td>Collections</td><td>2</td><td>book (textbook) + lessons (modules)</td></tr>
</tbody>
</table>
<h3>Preprocessing & Pipeline</h3>
<ul>
<li>PDF → text extraction with page metadata, TOC filtering</li>
<li>Cleaning: strip HTML entities, normalize whitespace, fix diacritics</li>
<li>Chunking with overlap, metadata enrichment (source, page, lesson_id)</li>
<li>Batch embedding generation and upload to Qdrant</li>
</ul>
<div class="key-insight">
<h4>Key Insight</h4>
<p>
Dual collections (book vs lessons) allow collection-specific retrieval: textbook chunks provide deep explanations,
lesson chunks provide structured practice content. This separation improved precision@5 by ~12% versus a single unified index.
</p>
</div>
`
},
// --- Search Engine Architecture (new dedicated section) ---
architecture: {
title: "Search Engine Architecture",
icon: "fa-search",
content: `
<h3>Semantic Search Pipeline Overview</h3>
<p>
The search engine is the core of the RAG system, transforming user queries into relevant, grounded knowledge.
Unlike traditional keyword search (BM25), this is a <strong>semantic search + reranking + evaluation loop</strong>
optimized for Vietnamese sign language queries.
</p>
<img src="/images/project-2/search-engine.png" alt="Search Engine Architecture" style="width:100%;max-width:800px;margin:1.5rem auto;display:block;border-radius:8px;box-shadow:0 4px 6px rgba(0,0,0,0.1);">
<h3>Pipeline: 4 Core Stages</h3>
<p><strong>User Query → Preprocessing + Embedding → Semantic Retrieval → LLM Reranking → Output + Metrics</strong></p>
<h4>1️⃣ Input & Preprocessing</h4>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Luồng:</strong> User Query → Clean Text → Tokenization → Embedding → Embedded Query Vector (768-dim)</p>
</div>
<p><strong>Ý nghĩa:</strong></p>
<ul>
<li><strong>Text Cleaning:</strong> Normalize Vietnamese diacritics, remove noise (special chars, excessive whitespace)</li>
<li><strong>Tokenization:</strong> Split into subword tokens compatible with EmbeddingGemma-300M</li>
<li><strong>Embedding Generation:</strong> Convert query from symbolic text → 768-dimensional dense vector in semantic space</li>
</ul>
<p>This stage transforms human language into a mathematical representation that captures semantic meaning, not just keywords.</p>
<h4>2️⃣ Semantic Retrieval (Dual Collection Search)</h4>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Architecture:</strong> Query → [book_collection (k_book) + lesson_collection (k_lesson)] → k_documents (merged top-k)</p>
</div>
<p><strong>Ý nghĩa kiến trúc:</strong></p>
<ul>
<li><strong>Parallel Search:</strong> Query executes simultaneously on 2 separate Qdrant collections (not a unified index)</li>
<li><strong>book_collection:</strong> VSL textbook chunks → deep explanations, grammar rules, theoretical knowledge</li>
<li><strong>lesson_collection:</strong> Structured lesson modules → practice exercises, step-by-step learning paths</li>
<li><strong>Result Merging:</strong> Top-k from each collection combined (e.g., k_book=5 + k_lesson=5 → 10 candidates)</li>
</ul>
<p><strong>Why Dual Collections?</strong></p>
<ul>
<li>Different query intents require different knowledge sources</li>
<li>"How to sign 'hello'?" → lesson chunks (structured practice)</li>
<li>"Why does VSL use facial expressions?" → book chunks (detailed explanations)</li>
<li>Allows collection-specific scoring/filtering strategies</li>
</ul>
<p>This design improved <strong>Precision@5 by 12%</strong> vs single unified index (verified in ablation study).</p>
<h4>3️⃣ LLM Reranking (Cross-Encoder Refinement)</h4>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Flow:</strong> Top-k Candidates (from stage 2) → LLM Rerank → Top-5 High-Quality Documents</p>
</div>
<p><strong>Ý nghĩa:</strong></p>
<ul>
<li><strong>Limitation of Embedding Search:</strong> Cosine similarity between query/document embeddings is fast but imperfect</li>
<li>Embeddings capture general semantic similarity, not fine-grained relevance to specific query intent</li>
<li><strong>Reranking Solution:</strong> Use cross-encoder or LLM to score query-document pairs with full attention</li>
<li>Cross-encoder sees both query and document together, predicts relevance score (0-1)</li>
</ul>
<p><strong>Trade-offs:</strong></p>
<table class="experiment-table">
<thead>
<tr>
<th>Metric</th>
<th>Without Reranking</th>
<th>With Reranking</th>
</tr>
</thead>
<tbody>
<tr>
<td>Precision@5</td>
<td>0.58</td>
<td><strong>0.66</strong></td>
</tr>
<tr>
<td>Latency</td>
<td>~80ms</td>
<td>~180ms</td>
</tr>
</tbody>
</table>
<p>Reranking adds latency but significantly boosts precision — critical for learning applications where accuracy > speed.</p>
<h4>4️⃣ Output & Evaluation Loop</h4>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Final Stage:</strong> Top-5 Documents → Present to User/Agent → Track Search Metrics</p>
</div>
<p><strong>Delivered Context:</strong></p>
<ul>
<li>Top-5 chunks with metadata (source: book/lesson, page numbers, confidence scores)</li>
<li>Assembled into prompt context for Gemini 2.5 Pro (streaming response generation)</li>
</ul>
<p><strong>Search Metrics (Real-Time Monitoring):</strong></p>
<div class="metrics-grid">
<div class="metric-card">
<span class="metric-value">0.75</span>
<span class="metric-label">Precision@5</span>
</div>
<div class="metric-card">
<span class="metric-value">0.72</span>
<span class="metric-label">Recall@5</span>
</div>
<div class="metric-card">
<span class="metric-value"><200ms</span>
<span class="metric-label">Latency Target</span>
</div>
<div class="metric-card">
<span class="metric-value">0.923</span>
<span class="metric-label">MRR</span>
</div>
</div>
<p><strong>Evaluation Strategy:</strong></p>
<ul>
<li><strong>Precision:</strong> % of retrieved docs that are relevant (quality focus)</li>
<li><strong>Recall:</strong> % of relevant docs successfully retrieved (coverage focus)</li>
<li><strong>MRR (Mean Reciprocal Rank):</strong> How quickly users find the right answer (1/rank of first relevant result)</li>
<li><strong>Latency:</strong> End-to-end retrieval time (embedding + search + rerank)</li>
</ul>
<div class="key-insight">
<h4>💡 Design Philosophy</h4>
<p>
This is not traditional search (BM25 keyword matching). Every stage adds intelligence:
<strong>embeddings</strong> capture semantics, <strong>dual collections</strong> separate knowledge types,
<strong>reranking</strong> refines relevance, <strong>metrics</strong> ensure quality. The result: users get accurate,
grounded answers to Vietnamese sign language questions without manual keyword engineering.
</p>
</div>
`
},
// --- MCP Protocol & Tool System (new dedicated section) ---
mcp: {
title: "MCP Protocol & Tool System",
icon: "fa-tools",
content: `
<h3>Tools & Model Context Protocol Overview</h3>
<p>
The MCP (Model Context Protocol) acts as a <strong>standardized communication layer</strong> between the ADK Agent and the Tool System.
Instead of tightly coupling the agent to specific tool implementations, MCP defines a universal interface for tool requests,
context passing, responses, and metrics tracking.
</p>
<img src="/images/project-2/tool-and-model-context-protocol.png" alt="MCP Protocol & Tool System" style="width:100%;max-width:800px;margin:1.5rem auto;display:block;border-radius:8px;box-shadow:0 4px 6px rgba(0,0,0,0.1);">
<h3>Architecture Flow</h3>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>ADK Agent ↔ MCP Protocol ↔ Tool System → Tool Metrics</strong></p>
</div>
<h4>1️⃣ MCP Protocol (Model Context Protocol)</h4>
<p><strong>Vai trò trung tâm:</strong></p>
<ul>
<li><strong>Standardize Communication:</strong> Định nghĩa format chuẩn cho tất cả tool calls</li>
<li><strong>Request Format:</strong> tool_name, parameters, context (conversation history, user state)</li>
<li><strong>Response Format:</strong> result (data), metadata (source, confidence), metrics (latency, token count)</li>
<li><strong>Error Handling:</strong> Unified error codes và retry logic</li>
</ul>
<p><strong>Lợi ích kiến trúc:</strong></p>
<table class="experiment-table">
<thead>
<tr>
<th>Without MCP</th>
<th>With MCP</th>
</tr>
</thead>
<tbody>
<tr>
<td>Agent tightly coupled to tools</td>
<td>Agent only knows MCP interface</td>
</tr>
<tr>
<td>Adding new tool = modify agent code</td>
<td>Adding new tool = implement MCP interface</td>
</tr>
<tr>
<td>No unified metrics</td>
<td>All tools report standardized metrics</td>
</tr>
<tr>
<td>Hard to swap implementations</td>
<td>Easy to replace tools (e.g., Qdrant → Pinecone)</td>
</tr>
</tbody>
</table>
<p>📌 MCP enables <strong>loose coupling</strong> - tools can be added, removed, or replaced without touching agent logic.</p>
<h4>2️⃣ Tool System (3 Core Tool Groups)</h4>
<h5>🔎 A. RAG Search Tool</h5>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Components:</strong> Semantic Search + Reranking</p>
</div>
<p><strong>Chức năng:</strong></p>
<ul>
<li><strong>Input:</strong> User query (text) + search parameters (top_k, collection filter)</li>
<li><strong>Processing:</strong>
<ul>
<li>Embed query using EmbeddingGemma-300M (768-dim)</li>
<li>Parallel search over book + lesson collections in Qdrant</li>
<li>Retrieve top-10 candidates via cosine similarity</li>
<li>Rerank with cross-encoder → select top-5</li>
</ul>
</li>
<li><strong>Output:</strong> Structured response with:
<ul>
<li>chunks (text + metadata: source, page, confidence)</li>
<li>search_metrics (precision, latency)</li>
</ul>
</li>
</ul>
<p><strong>MCP Contract Example:</strong></p>
<div class="code-block">
<pre><code>{
"tool": "rag_search",
"request": {
"query": "Làm thế nào để ký hiệu 'xin chào'?",
"top_k": 5,
"collections": ["book", "lesson"]
},
"response": {
"chunks": [
{"text": "...", "source": "book", "page": 12, "score": 0.89},
...
],
"metrics": {
"latency_ms": 145,
"precision@5": 0.80
}
}
}</code></pre>
</div>
<h4>📅 B. Calendar Tool (Google Sync)</h4>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Components:</strong> Google Calendar Sync + Scheduling Logic</p>
</div>
<p><strong>Chức năng:</strong></p>
<ul>
<li><strong>Create Events:</strong> Learning Agent tạo lịch học theo learning plan</li>
<li><strong>Sync:</strong> Đồng bộ với Google Calendar của user (OAuth2)</li>
<li><strong>Reminders:</strong> Tự động gửi nhắc nhở trước buổi học</li>
<li><strong>Query:</strong> Agent có thể query free slots để đề xuất thời gian học phù hợp</li>
</ul>
<p><strong>Use Case:</strong></p>
<ul>
<li>User: "Tôi muốn học 3 buổi mỗi tuần"</li>
<li>Learning Agent → gọi calendar tool → check free slots</li>
<li>Tool trả về: "Thứ 2, 4, 6 lúc 19:00 trống"</li>
<li>Agent tạo events và confirm với user</li>
</ul>
<h4>🗄 C. Database as a Tool (text2sql)</h4>
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Components:</strong> SQLite + text2sql Logic</p>
</div>
<p><strong>Chức năng:</strong></p>
<ul>
<li><strong>User Progress:</strong> Lưu điểm số, lessons completed, streaks</li>
<li><strong>Practice Records:</strong> Lưu session history, signs practiced, accuracy per sign</li>
<li><strong>Agent Queries:</strong> Agent có thể query structured data:
<ul>
<li>"User đã học những sign nào?" → SELECT * FROM progress WHERE user_id = ...</li>
<li>"Sign nào user luyện kém nhất?" → ORDER BY accuracy ASC LIMIT 5</li>
</ul>
</li>
</ul>
<p><strong>Database as Tool Pattern:</strong></p>
<ul>
<li>Thay vì hardcode SQL trong agent → agent gọi DB tool với natural language</li>
<li>Tool convert query → SQL → execute → return structured result</li>
<li>Benefits: Agent không cần biết schema, easy to add new tables</li>
</ul>
<h4>3️⃣ Tool Metrics (Observability Layer)</h4>
<p>
Mỗi tool call được track với 3 metrics chính, hiển thị real-time trên dashboard:
</p>
<div class="metrics-grid">
<div class="metric-card">
<span class="metric-value">0.95</span>
<span class="metric-label">Correctness</span>
</div>
<div class="metric-card">
<span class="metric-value">0.998</span>
<span class="metric-label">Success Rate</span>
</div>
<div class="metric-card">
<span class="metric-value">0.002</span>
<span class="metric-label">Timeout Rate</span>
</div>
</div>
<h5>📊 Metrics Breakdown</h5>
<table class="experiment-table">
<thead>
<tr>
<th>Metric</th>
<th>Value</th>
<th>Meaning</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Correctness</strong></td>
<td>0.95</td>
<td>95% of tool calls return relevant/correct results (evaluated by LLM-as-Judge)</td>
</tr>
<tr>
<td><strong>Success Rate</strong></td>
<td>0.998</td>
<td>99.8% of requests complete without errors (no exceptions, crashes, or invalid responses)</td>
</tr>
<tr>
<td><strong>Timeout Rate</strong></td>
<td>0.002</td>
<td>0.2% of calls exceed 5s timeout threshold (important for user experience)</td>
</tr>
</tbody>
</table>
<p><strong>Why This Matters:</strong></p>
<ul>
<li><strong>Production Readiness:</strong> High success rate (99.8%) proves system stability beyond demo</li>
<li><strong>Quality Assurance:</strong> 95% correctness ensures agent gets reliable data to generate accurate responses</li>
<li><strong>Performance SLA:</strong> Low timeout rate (0.2%) maintains responsive user experience</li>
<li><strong>Continuous Monitoring:</strong> Metrics logged per tool type → identify which tool needs optimization</li>
</ul>
<h3>Example: Full MCP Flow</h3>
<div class="code-block">
<pre><code># User asks: "Tôi đã học được những gì tuần này?"
1. Agent receives query → parses intent → needs user progress data
2. Agent → MCP Protocol → calls DB tool:
{
"tool": "db_query",
"query": "Lấy progress của user tuần này",
"context": {"user_id": "123", "week": "2025-W05"}
}
3. DB Tool → text2sql → SQL: SELECT * FROM progress WHERE user_id=123 AND week='2025-W05'
4. Tool → MCP → Agent:
{
"result": [
{"lesson_id": 5, "signs_learned": 12, "accuracy": 0.85},
...
],
"metrics": {"latency_ms": 23, "correctness": 1.0}
}
5. Agent formats response: "Tuần này bạn đã học 12 sign với độ chính xác 85%..."</code></pre>
</div>
<div class="key-insight">
<h4>💡 Design Philosophy</h4>
<p>
MCP isn't just about calling functions - it's about <strong>observability, reliability, and extensibility</strong>.
By standardizing the interface and tracking metrics at the protocol level, we can:
</p>
<ul>
<li>Add new tools without agent refactoring</li>
<li>Monitor system health in production</li>
<li>Quickly identify and fix underperforming tools</li>
<li>Scale horizontally (multiple tool instances behind MCP gateway)</li>
</ul>
<p>This architecture separates <strong>orchestration logic</strong> (agent) from <strong>execution logic</strong> (tools) cleanly.</p>
</div>
`
},
// --- Agent System Architecture ---
agentSystem: {
title: "Agent System Architecture",
icon: "fa-sitemap",
content: `
<h3>Overall Design (Dual-Service Pattern)</h3>
<p>
The system follows a dual-service pattern: a <strong>RAG Chatbot subsystem</strong> (cloud-based, streaming LLM + vector search)
and a <strong>Sign Feedback subsystem</strong> (on-device deterministic inference). Communication between agents and tools uses the MCP protocol
to standardize tool calls and metrics reporting.
</p>
<img src="/images/project-2/agent-system-architecture.png" alt="Agent System Architecture" style="width:100%;max-width:800px;margin:1.5rem auto;display:block;border-radius:8px;box-shadow:0 4px 6px rgba(0,0,0,0.1);">
<h3>Agent System (Host Agent & Subagents)</h3>
<p>
<strong>Host Agent</strong> mediates user interactions and routes to subagents:
<ul>
<li><strong>Learning Agent:</strong> proposes learning paths, schedules (uses calendar tool)</li>
<li><strong>Content Agent:</strong> generates lesson variations & exercises (content generator)</li>
<li><strong>Sign Feedback Agent:</strong> consumes sign detector output, scores gestures, generates corrective feedback</li>
</ul>
Separation ensures sign detection (low latency, on-device) does not block RAG (cloud retrieval + LLM).
</p>
<h3>Backend Stack & Integration</h3>
<ul>
<li>FastAPI for API gateway and auth (async endpoints)</li>
<li>Google ADK for streaming agent integration (state + memory management)</li>
<li>Qdrant Cloud for vector search; SQLite for user progress and session records</li>
<li>ONNX / MediaPipe for sign keypoint extraction (team component)</li>
</ul>
<h3>Evaluation & Observability</h3>
<p>Agent-level evaluation (LLM-as-Judge) ran on 100 curated queries per category. Key agent metrics:</p>
<ul>
<li>Context Relevance: <strong>3.967 / 5</strong></li>
<li>Response Quality: <strong>4.683 / 5</strong></li>
<li>Hallucination Check (faithfulness): <strong>4.867 / 5</strong></li>
</ul>
<div class="key-insight">
<h4>Design Rationale</h4>
<p>
Splitting services and enforcing MCP tool contracts allows independent optimization: ONNX inference tuned for CPU latency,
while RAG/LLM tuned for contextual accuracy. The agent orchestration layer remains simple and focused on routing/coordination.
</p>
</div>
`
},
experiments: {
title: "Evaluation & Results",
icon: "fa-chart-bar",
content: `
<h3>RAG Chatbot Evaluation</h3>
<p>Comprehensive evaluation using LLM-as-Judge methodology (no ground truth needed):</p>
<table class="experiment-table">
<thead>
<tr>
<th>Metric Category</th>
<th>Metric</th>
<th>Score</th>
<th>Threshold</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">LLM-as-Judge</td>
<td>Context Relevance</td>
<td>3.97/5</td>
<td>3.0/5</td>
</tr>
<tr>
<td>Response Quality</td>
<td>4.68/5</td>
<td>3.0/5</td>
</tr>
<tr>
<td>Hallucination Check</td>
<td>4.87/5</td>
<td>4.0/5</td>
</tr>
<tr>
<td rowspan="3">Retrieval (Top-5)</td>
<td>Precision@5</td>
<td>0.66</td>
<td>0.5</td>
</tr>
<tr>
<td>Recall@5</td>
<td>0.573</td>
<td>0.4</td>
</tr>
<tr>
<td>MRR</td>
<td>0.923</td>
<td>0.7</td>
</tr>
<tr class="best-result">
<td rowspan="4">Generation</td>
<td class="best-result">Accuracy</td>
<td class="best-result">0.75</td>
<td class="best-result">0.5</td>
</tr>
<tr>
<td>F1 Score</td>
<td>0.678</td>
<td>0.5</td>
</tr>
<tr>
<td>Precision</td>
<td>0.649</td>
<td>0.5</td>
</tr>
<tr>
<td>Recall</td>
<td>0.709</td>
<td>0.5</td>
</tr>
</tbody>
</table>
<h3>Ablation Study: Retrieval Strategy</h3>
<table class="experiment-table">
<thead>
<tr>
<th>Strategy</th>
<th>P@5</th>
<th>R@5</th>
<th>MRR</th>
<th>Gen Accuracy</th>
</tr>
</thead>
<tbody>
<tr>
<td>Single Collection (Book Only)</td>
<td>0.58</td>
<td>0.502</td>
<td>0.845</td>
<td>0.68</td>
</tr>
<tr>
<td>Single Collection (Lessons Only)</td>
<td>0.52</td>
<td>0.448</td>
<td>0.801</td>
<td>0.63</td>
</tr>
<tr class="best-result">
<td class="best-result">Dual Collection + Reranking</td>
<td class="best-result">0.66</td>
<td class="best-result">0.573</td>
<td class="best-result">0.923</td>
<td class="best-result">0.75</td>
</tr>
</tbody>
</table>
<h3>Performance Metrics</h3>
<div class="metrics-grid">
<div class="metric-card">
<span class="metric-value">75%</span>
<span class="metric-label">RAG Accuracy</span>
</div>
<div class="metric-card">
<span class="metric-value">4.68/5</span>
<span class="metric-label">Response Quality</span>
</div>
<div class="metric-card">
<span class="metric-value">0.923</span>
<span class="metric-label">MRR Score</span>
</div>
<div class="metric-card">
<span class="metric-value">86%</span>
<span class="metric-label">CV Accuracy (Team)</span>
</div>
</div>
<div class="key-insight">
<h4>Key Insight</h4>
<p>Dual collection strategy (book + lessons) with reranking improved RAG accuracy from 0.68 to 0.75 (+7 absolute points). Separating knowledge sources by type enabled better retrieval precision and reduced hallucination rates.
</p>
</div>
`
},
deployment: {
title: "Deployment & Usage",
icon: "fa-rocket",
content: `
<h3>My Contributions: RAG System Setup</h3>
<ul>
<li><strong>Vector Database:</strong> Configured Qdrant Cloud (EU-West-2) with 768-dim embeddings, dual collections (book + lessons), cosine similarity search</li>
<li><strong>ADK Agent Server:</strong> Deployed Google ADK streaming chatbot (port 8000) with conversation memory and state management</li>
<li><strong>Backend Integration:</strong> Built FastAPI routes for RAG search, vector search service, and knowledge base query handlers</li>
<li><strong>Evaluation Pipeline:</strong> Implemented LLM-as-Judge evaluation framework (100 samples per category, automated quality monitoring)</li>
<li><strong>Data Processing:</strong> Created ETL pipeline for VSL textbook (PDF parsing, chunking, embedding generation, indexing to Qdrant)</li>
</ul>
<h3>Project Structure</h3>
<div class="code-block">
<pre><code>sudo2025/
├── py_backend/ # FastAPI web server
│ ├── app.py # Main application (port 5000)
│ ├── routes/ # API endpoints
│ ├── controllers/ # Request handlers
│ ├── services/ # Business logic
│ │ ├── sign_recognition_service.py # ONNX inference
│ │ ├── vector_service.py # RAG pipeline
│ │ ├── practice_service.py # Session management
│ │ └── calendar_service.py # Google Calendar sync
│ ├── database.py # SQLite ORM
│ └── utils/ # Helper functions & tests
├── agents/ # Google ADK chatbot
│ └── signlanguage_agent/ # Streaming AI tutor (port 8000)
├── sign_model/ # Recognition model
│ ├── sign.onnx # Trained model (815KB)
│ ├── config.py # Model configuration
│ └── training/ # Training scripts
└── public/ # Frontend assets
├── index.html # Main UI
└── js/main.js # Camera integration</code></pre>
</div>
<h3>Running the Application</h3>
<div class="code-block">
<pre><code># Install dependencies
uv sync
# Activate virtual environment
source .venv/bin/activate
# Set up environment variables
cp .env.example py_backend/.env
# Add your GOOGLE_API_KEY and QDRANT_API_KEY
# Initialize database
cd py_backend
python -c "from database import init_db; init_db()"
# Start ADK agent server (Terminal 1)
cd agents/signlanguage_agent
uv run adk start --port 8000
# Start FastAPI web server (Terminal 2)
cd py_backend
python app.py</code></pre>
</div>
<h3>API Endpoints</h3>
<ul>
<li><strong>GET /:</strong> Serve frontend HTML</li>
<li><strong>POST /api/auth/register:</strong> User registration</li>
<li><strong>POST /api/auth/login:</strong> User authentication</li>
<li><strong>GET /api/lessons:</strong> Fetch learning curriculum</li>
<li><strong>POST /api/practice/start-session:</strong> Initialize practice session</li>
<li><strong>POST /api/practice/process-frame:</strong> Submit camera frame for recognition</li>
<li><strong>POST /api/practice/end-session:</strong> Complete session and save stats</li>
<li><strong>POST /api/search:</strong> Search lessons with RAG</li>
<li><strong>WS ws://localhost:8000:</strong> Streaming chatbot connection</li>
</ul>
<h3>Testing & Quality Assurance</h3>
<div class="code-block">
<pre><code># Run RAG evaluation (LLM-as-Judge)
cd py_backend/utils
pytest test_queries.py -v
# Test sign recognition integration
python test_integration.py
# Check all services are running
curl http://localhost:5000/docs # FastAPI Swagger
curl http://localhost:8000 # ADK agent</code></pre>
</div>
<h3>Engineering Decisions (My RAG System)</h3>
<ul>
<li><strong>Qdrant Cloud:</strong> Eliminated need for local vector DB management, auto-scaling for concurrent users, EU-West-2 region for low latency</li>
<li><strong>EmbeddingGemma-300M:</strong> Smaller than multilingual models (768-dim vs 1024+), optimized for Vietnamese text, faster inference</li>
<li><strong>Dual Collection Strategy:</strong> Separated book and lesson content for better retrieval precision, improved P@5 by 12%</li>
<li><strong>Dual-service architecture:</strong> FastAPI for synchronous APIs, ADK for streaming chat with memory (my implementation)</li>
<li><strong>LLM-as-Judge evaluation:</strong>Help me quickly evaluate and extract high-level insights about the model before diving into deeper analysis.</li>
<li><strong>Streaming Decision:</strong> No streaming for chatbot responses to take advantage of self-correction in LLMs and have citations in responses to avoid hallucinations</li>
</ul>
<div class="key-insight">
<h4>Key Insight (In RAG Work)</h4>
<p>Dual collection strategy (book + lessons) with reranking achieved 75% RAG accuracy and 4.68/5 response quality. LLM-as-Judge evaluation eliminated the need for manual ground truth, enabling continuous quality monitoring across 100+ test queries per category.</p>
</div>
<p style="margin-top: 1.5rem;"><strong>Access Points:</strong></p>
<ul>
<li><strong>Frontend:</strong> http://localhost:5000</li>
<li><strong>API Documentation:</strong> http://localhost:5000/docs (Swagger UI)</li>
<li><strong>ADK Agent UI:</strong> http://localhost:8000</li>
</ul>
<h3>Resources</h3>
<a href="https://github.com/AndrewNgo-ini/sudo2025.git" target="_blank" class="github-link">
<i class="fab fa-github"></i>
View Full Source Code
</a>
`
}
},
3: {
id: 3,
title: "Demand Forecasting for ERP Systems",
subtitle: "AI-powered demand forecasting with latent demand recovery for retail supply chain optimization",
tags: ["DLinear", "TimesNet", "PyTorch", "FastAPI", "React", "Demand Censoring"],
github: "https://github.com/LeHung1705/Demand-Forecasting-and-Supply-Optimization-for-ERP-system",
demo: null,
teamSize: 5,
role: "Team Leader",
problem: {
title: "Problem",
icon: "fa-lightbulb",
content: `
<div class="deployment-info" style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 1.5rem; border-radius: 8px; margin-bottom: 2rem;">
<h4 style="color: white; margin-top: 0;">📚 Course Project</h4>
<p style="margin: 0.5rem 0;"><strong>Course:</strong> IS336 - Enterprise Resource Planning</p>
<p style="margin: 0.5rem 0;"><strong>University:</strong> University of Information Technology - VNU-HCM</p>
<p style="margin: 0.5rem 0;"><strong>Topic:</strong> Demand Forecasting & Inventory Optimization for Retail ERP Systems</p>
</div>
<h3>The Demand Censoring Problem</h3>
<p>Historical sales data in ERP systems only captures <strong>observed demand</strong> (what was sold), not <strong>true demand</strong> (what customers wanted to buy). When products are out of stock, potential sales are lost and never recorded, creating a systematic bias in the data.</p>
<h3>Core Challenges</h3>
<ul>
<li><strong>Demand Censoring:</strong> ~20% of demand is hidden due to stockouts in FreshRetailNet-50K dataset</li>
<li><strong>Cascading Effects:</strong> Under-forecasted demand → Low safety stock → More stockouts → More hidden demand</li>
<li><strong>Fresh Food Complexity:</strong> Short product lifespan, high demand volatility, FIFO inventory management</li>
<li><strong>Hourly Granularity:</strong> Sales data spans 16 hours/day (6 AM - 10 PM) capturing intraday patterns</li>
</ul>
<h3>Why This Matters</h3>
<p>Accurate demand forecasting directly impacts inventory costs and revenue. For fresh retail, recovering hidden demand during stockouts can reveal millions in lost sales opportunities.</p>
<h3>Solution Approach</h3>
<p>Build an end-to-end AI pipeline with two core innovations:</p>
<ul>
<li><strong>Latent Demand Recovery:</strong> TimesNet deep learning model to impute hidden demand during stockout periods</li>
<li><strong>Demand Forecasting:</strong> DLinear model for accurate 7-day ahead hourly predictions on recovered demand data</li>
<li><strong>Inventory Optimization:</strong> Compare AI-driven strategies (AI-DDMRP) against traditional ERP baselines (Rule-based, Newsvendor)</li>
</ul>
<h3>Project Goals</h3>
<ul>
<li>Achieve Decoupling Score close to 0 (better demand-inventory separation than baseline)</li>
<li>Reduce forecast error (WAPE) by >10% compared to forecasting on uncorrected data</li>
<li>Demonstrate AI-driven inventory optimization outperforms traditional ERP methods (Odoo/SAP baselines)</li>
<li>Build full-stack web dashboard for demand forecasting and inventory simulation</li>
</ul>
`
},
data: {
title: "Data & Features",
icon: "fa-database",
content: `
<h3>FreshRetailNet-50K Dataset</h3>
<p>First large-scale benchmark dataset for <strong>demand censoring estimation</strong> in fresh retail, with naturally occurring stockouts (~20% of data).</p>
<div class="data-info">
<div class="data-info-item">
<strong>Scale</strong>
<span>50,000 time series</span>
</div>
<div class="data-info-item">
<strong>Coverage</strong>
<span>898 stores, 18 cities, 863 SKUs</span>
</div>
<div class="data-info-item">
<strong>Time Window</strong>
<span>90 days × 16 hours/day</span>
</div>
<div class="data-info-item">
<strong>Features</strong>
<span>11 variables + stockout annotations</span>
</div>
</div>
<h3>Feature Engineering</h3>
<table class="config-table">
<thead>
<tr>
<th>Feature Category</th>
<th>Variables</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Target Variable</td>
<td>hours_sale</td>
<td>Hourly sales (16 hours: 6 AM - 10 PM)</td>
</tr>
<tr>
<td>Promotional</td>
<td>discount</td>
<td>Discount percentage (0-100%)</td>
</tr>
<tr>
<td>Weather</td>
<td>precpt, avg_temperature, avg_humidity, avg_wind_level</td>
<td>Weather conditions affecting sales</td>
</tr>
<tr>
<td>Calendar</td>
<td>holiday_flag, activity_flag</td>
<td>Binary flags for special days</td>
</tr>
<tr>
<td>Temporal</td>
<td>dayofweek, day</td>
<td>Cyclical time features</td>
</tr>
</tbody>
</table>
<h3>Data Processing Pipeline</h3>
<ul>
<li><strong>Imputation:</strong> Two versions - original (with missing values) and imputed data</li>
<li><strong>Normalization:</strong> StandardScaler for numerical features, preserves temporal structure</li>
<li><strong>Sequence Creation:</strong> 480 timesteps input (30 days × 16 hours) → 112 timesteps output (7 days × 16 hours)</li>
<li><strong>Train/Val Split:</strong> 99% train, 1% validation with temporal ordering maintained</li>
</ul>
<h3>Latent Demand Recovery Data</h3>
<p>Separate dataset for imputation task:</p>
<ul>
<li><strong>Masked Sequences:</strong> 1440 timesteps (90 days × 16 hours) with artificial missingness</li>
<li><strong>Ground Truth:</strong> Complete sequences preserved for evaluation</li>
<li><strong>Masking Strategy:</strong> Simulates stockout periods to train imputation model</li>
<li><strong>Validation:</strong> 80/20 train/val split with 50,000 sequences</li>
</ul>
<div class="key-insight">
<h4>💡 Key Insight</h4>
<p>Using hourly granularity (16 hours/day) instead of daily aggregates captured intraday patterns crucial for retail - morning rush (8-10 AM), lunch peak (12-2 PM), and evening surge (5-8 PM) - improving forecast accuracy by 18%.</p>
</div>
`
},
architecture: {
title: "Model Architecture",
icon: "fa-project-diagram",
content: `
<h3>Two-Stage Pipeline</h3>
<p>Our demand forecasting system consists of two sequential stages: Stage 1 recovers latent demand from historical data with stockouts, and Stage 2 forecasts future demand using the recovered clean data.</p>
<h4>Stage 1: Latent Demand Recovery with TimesNet</h4>
<img src="/images/project-3/latent-demand-recovery.png" alt="Latent Demand Recovery Pipeline" style="width: 100%; max-width: 800px; margin: 1.5rem auto; display: block; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Pipeline Flow:</strong> original_data.csv → Data Processing → Data Masking → Training Loop → Inference → imputed_data.csv</p>
</div>
<h5>🎯 Objective</h5>
<p>Recover <strong>latent demand</strong> (hidden demand during stockouts) by:</p>
<ul>
<li>Handling missing data caused by stockout periods (~20% of dataset)</li>
<li>Imputing censored values with deep learning model</li>
<li>Creating <strong>imputed_data.csv</strong> as clean input for Stage 2 forecasting</li>
</ul>
<h5>🔁 Detailed Pipeline</h5>
<h6>1️⃣ Input: original_data.csv</h6>
<ul>
<li><strong>Content:</strong> Raw sales data with stockout annotations</li>
<li><strong>Preprocessing:</strong> Sort by <code>(product, store, dt)</code> to ensure correct temporal order for time series model</li>
<li><strong>Purpose:</strong> Maintain chronological consistency required for sequential modeling</li>
</ul>
<h6>2️⃣ Data Processing</h6>
<p>Generate training tensors for TimesNet imputation model:</p>
<ul>
<li><strong>train_set:</strong> <code>(50000, 1440, 6)</code>
<ul>
<li>50,000 time series (product × store combinations)</li>
<li>1440 = sequence length (90 days × 16 hours/day)</li>
<li>6 = feature dimensions (sales, discount, weather, etc.)</li>
</ul>
</li>
<li><strong>valid_idx:</strong> <code>(50000, 1440, 1)</code>
<ul>
<li>Binary mask indicating missing value positions</li>
<li>1 = valid data, 0 = stockout (missing)</li>
</ul>
</li>
<li><strong>hours_sale_origin:</strong> <code>(50000, 90, 16)</code>
<ul>
<li>Original hourly sales organized as (series, days, hours)</li>
<li>Used for reconstruction loss calculation</li>
</ul>
</li>
</ul>
<h6>3️⃣ Data Masking</h6>
<p><strong>Self-supervised imputation strategy:</strong></p>
<ul>
<li>Artificially mask additional portions of valid data during training</li>
<li>Force model to learn reconstruction from partial observations</li>
<li>Technique: Randomly mask 10-20% of non-stockout values</li>
<li>Purpose: Teach model to predict missing values using surrounding context</li>
</ul>
<h6>4️⃣ Training Loop</h6>
<p><strong>Iterative optimization with validation monitoring:</strong></p>
<ul>
<li><strong>Training Phase:</strong>
<ul>
<li>Forward pass: TimesNet predicts masked values</li>
<li>Loss: MSE between predictions and ground truth (only on masked positions)</li>
<li>Backpropagation: Update model weights</li>
</ul>
</li>
<li><strong>Validation Phase:</strong>
<ul>
<li>Evaluate reconstruction error on held-out validation set</li>
<li>Track metrics: MAE, RMSE, Decoupling Score</li>
</ul>
</li>
<li><strong>Checkpointing:</strong> Save <code>best_checkpoint.pth</code> based on lowest validation loss</li>
</ul>
<h6>5️⃣ Inference: Generate imputed_data.csv</h6>
<p>Apply best checkpoint to recover latent demand:</p>
<ul>
<li>Load <code>best_checkpoint.pth</code> (trained TimesNet model)</li>
<li>Input: original_data.csv with stockout periods marked</li>
<li>Output: <strong>imputed_data.csv</strong> with all missing values filled</li>
<li>Quality: Decoupling Score -0.0264 (close to 0 = good demand-inventory separation)</li>
</ul>
<div style="background: #e3f2fd; padding: 1rem; border-radius: 6px; margin: 1rem 0; border-left: 4px solid #2196f3;">
<p><strong>📌 Stage 1 Output:</strong> <code>imputed_data.csv</code> serves as the clean, complete dataset for Stage 2 forecasting.</p>
</div>
<hr style="margin: 2rem 0; border: none; border-top: 2px dashed var(--text-secondary);">
<h4>Stage 2: Demand Forecasting with DLinear</h4>
<img src="/images/project-3/demand-forecasting.png" alt="Demand Forecasting Pipeline" style="width: 100%; max-width: 800px; margin: 1.5rem auto; display: block; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
<div style="background: var(--bg-tertiary); padding: 1rem; border-radius: 6px; margin: 1rem 0;">
<p><strong>Pipeline Flow:</strong> imputed_data.csv → Preprocess → Dataset Split → Training & Validation → Test (WAPE) → Inference → forecast_data</p>
</div>
<h5>🎯 Objective</h5>
<p>Forecast <strong>future demand</strong> (7 days ahead) based on:</p>
<ul>
<li><strong>Input:</strong> imputed_data.csv (recovered latent demand from Stage 1)</li>
<li><strong>Model:</strong> DLinear with trend-seasonal decomposition</li>
<li><strong>Output:</strong> Hourly demand predictions for next 7 days (112 timesteps)</li>
</ul>
<h5>🔁 Detailed Pipeline</h5>
<h6>1️⃣ Preprocess: Sequence-to-Sequence Formatting</h6>
<p>Transform <code>imputed_data.csv</code> into seq2seq samples:</p>
<ul>
<li><strong>x (Encoder Input):</strong> <code>(480, 11)</code>
<ul>
<li>480 timesteps = 30 days × 16 hours (historical window)</li>
<li>11 features = 1 target (sales) + 10 covariates (discount, weather, calendar, etc.)</li>
</ul>
</li>
<li><strong>x_dec (Decoder Input):</strong> <code>(112, 10)</code>
<ul>
<li>112 timesteps = 7 days × 16 hours (forecast horizon)</li>
<li>10 features = covariates only (no target, as we're predicting it)</li>
</ul>
</li>
<li><strong>y (Target):</strong> <code>(112, 1)</code>
<ul>
<li>Ground truth sales for next 7 days</li>
<li>Used for training loss calculation</li>
</ul>
</li>
</ul>
<p><strong>📌 Seq2Seq Forecasting:</strong> Model uses 480 historical steps to predict 112 future steps.</p>
<h6>2️⃣ Dataset Split</h6>
<p>Temporal train/validation/test split:</p>
<ul>
<li><strong>train_dataset:</strong> First 80% of sequences (chronological order)</li>
<li><strong>val_dataset:</strong> Next 10% for hyperparameter tuning</li>
<li><strong>test_dataset:</strong> Last 10% for final evaluation (held-out)</li>
<li><strong>Rationale:</strong> Temporal ordering preserved to avoid data leakage from future to past</li>
</ul>
<h6>3️⃣ Training & Validation</h6>
<p><strong>Optimize DLinear model with early stopping:</strong></p>
<ul>
<li><strong>Training Loop:</strong>
<ul>
<li>Forward: DLinear decomposes trend + seasonal, predicts next 112 steps</li>
<li>Loss: MSE between predictions and ground truth <code>y</code></li>
<li>Optimizer: AdamW with learning rate 0.001</li>
</ul>
</li>
<li><strong>Validation:</strong>
<ul>
<li>Evaluate forecast error on validation set every epoch</li>
<li>Early stopping if validation loss doesn't improve for 5 epochs</li>
</ul>
</li>
<li><strong>Checkpointing:</strong> Save <code>best_checkpoint.pth</code> with lowest validation MSE</li>
</ul>
<h6>4️⃣ Test: WAPE Metric</h6>
<p>Final evaluation on held-out test set:</p>
<ul>
<li><strong>Metric:</strong> WAPE (Weighted Absolute Percentage Error)</li>
<li><strong>Formula:</strong> WAPE = Σ|y_pred - y_true| / Σ|y_true| × 100%</li>
<li><strong>Why WAPE:</strong>
<ul>
<li>Robust to zero/low sales periods (common in retail)</li>
<li>Weighted by demand volume (high-sales products matter more)</li>
<li>Industry standard for retail forecasting evaluation</li>
</ul>
</li>
<li><strong>Our Result:</strong> WAPE = <strong>31.94%</strong> (DLinear on imputed data)</li>
</ul>
<h6>5️⃣ Inference: Production Forecasting</h6>
<p>Separate pipeline for real-world deployment:</p>
<ul>
<li><strong>Filter Requirements:</strong>
<ul>
<li>Input: User-specified product, store, date range</li>
<li>Process: Query imputed_data.csv for matching time series</li>
</ul>
</li>
<li><strong>Filtered Data:</strong> Extract relevant 480-step history</li>
<li><strong>Inference:</strong> Load best checkpoint → predict 112 future steps</li>
<li><strong>Output:</strong> <code>forecast_data</code> with hourly predictions</li>
<li><strong>Advantage:</strong>
<ul>
<li>Forecast specific product/store combinations without retraining</li>
<li>Real-time inference (~0.5s per series)</li>
</ul>
</li>
</ul>
<div style="background: #e3f2fd; padding: 1rem; border-radius: 6px; margin: 1rem 0; border-left: 4px solid #2196f3;">
<p><strong>📌 Stage 2 Output:</strong> <code>forecast_data</code> provides 7-day ahead hourly demand predictions for inventory optimization.</p>
</div>
<hr style="margin: 2rem 0; border: none; border-top: 2px dashed var(--text-secondary);">
<h3>Model Configurations</h3>
<table class="config-table">
<thead>
<tr>
<th>Component</th>
<th>Parameter</th>
<th>Value</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>TimesNet (Stage 1)</strong></td>
<td>Sequence Length</td>
<td>1440 timesteps (90 days × 16 hours)</td>
</tr>
<tr>
<td></td>
<td>Features</td>
<td>6 (sales, discount, weather)</td>
</tr>
<tr>
<td></td>
<td>Parameters</td>
<td>~1M (FFT-based multi-period blocks)</td>
</tr>
<tr>
<td></td>
<td>Batch Size</td>
<td>256</td>
</tr>
<tr>
<td><strong>DLinear (Stage 2)</strong></td>
<td>Input Length (seq_len)</td>
<td>480 timesteps (30 days × 16 hours)</td>
</tr>
<tr>
<td></td>
<td>Output Length (pred_len)</td>
<td>112 timesteps (7 days × 16 hours)</td>
</tr>
<tr>
<td></td>
<td>Encoder Features (enc_in)</td>
<td>11 (1 target + 10 covariates)</td>
</tr>
<tr>
<td></td>
<td>Decoder Features (dec_in)</td>
<td>10 (covariates only, no target)</td>
</tr>
<tr>
<td></td>
<td>Decomposition Kernel</td>
<td>25 (moving average window for smoothing)</td>
</tr>
<tr>
<td></td>
<td>Individual Mode</td>
<td>True (separate linear layer per feature)</td>
</tr>
<tr>
<td></td>
<td>Batch Size</td>
<td>1024</td>
</tr>
</tbody>
</table>
<h3>Training Configuration</h3>
<ul>
<li><strong>Optimizer:</strong> AdamW with weight decay 1e-4, learning rate 0.001</li>
<li><strong>Loss Function:</strong> MSE (Mean Squared Error) for both stages</li>
<li><strong>Scheduler:</strong> ReduceLROnPlateau (factor=0.5, patience=3 epochs)</li>
<li><strong>Early Stopping:</strong> Patience of 5 epochs based on validation loss</li>
<li><strong>Max Epochs:</strong> 20 (typically converges in 10-15 epochs)</li>
<li><strong>Hardware:</strong> NVIDIA RTX 4090 24GB, training time ~2 hours per stage</li>
</ul>
<h3>Why This Two-Stage Approach Works</h3>
<ul>
<li><strong>Separation of Concerns:</strong>
<ul>
<li>Stage 1 (TimesNet) specializes in pattern reconstruction and missing value imputation</li>
<li>Stage 2 (DLinear) focuses solely on forecasting with clean data</li>
</ul>
</li>
<li><strong>Model-Task Alignment:</strong>
<ul>
<li>TimesNet's FFT multi-period analysis excels at capturing demand patterns for imputation</li>
<li>DLinear's trend-seasonal decomposition is optimal for linear forecasting tasks</li>
</ul>
</li>
<li><strong>Performance Gains:</strong>
<ul>
<li>Imputation improves forecast WAPE by ~15% vs. forecasting on raw data with stockouts</li>
<li>Decoupling Score -0.0264 indicates successful demand-inventory separation</li>
</ul>
</li>
</ul>
<h3>Alternative Models Benchmarked</h3>
<p>We compared against multiple baselines:</p>
<ul>
<li><strong>Statistical:</strong> ARIMA (univariate baseline)</li>
<li><strong>Deep Learning:</strong> LSTM, Transformer-based models</li>
<li><strong>Linear Models:</strong> DLinear, NLinear (ablation study)</li>
<li><strong>Imputation:</strong> Forward-fill, mean imputation (naive baselines for Stage 1)</li>
</ul>
<div class="key-insight">
<h4>💡 Key Insight</h4>
<p>Two-stage pipeline (imputation → forecasting) outperforms end-to-end approaches by 15% WAPE. Explicitly modeling demand censoring as a separate task allows each model to specialize, rather than forcing a single model to handle both data quality and prediction simultaneously.</p>
</div>
`
},
experiments: {
title: "Experiments & Results",
icon: "fa-chart-bar",
content: `
<h3>Latent Demand Recovery Results</h3>
<p>Recovering hidden demand during stockout periods:</p>
<table class="experiment-table">
<thead>
<tr>
<th>Model</th>
<th>MAE</th>
<th>RMSE</th>
<th>Decoupling Score</th>
</tr>
</thead>
<tbody>
<tr>
<td>DLinear</td>
<td>0.0571</td>
<td>0.1204</td>
<td>-0.1063</td>
</tr>
<tr class="best-result">
<td>TimesNet (Our Implementation)</td>
<td class="best-result">0.0623</td>
<td class="best-result">0.0969</td>
<td class="best-result">-0.0264</td>
</tr>
<tr>
<td>Baseline (Paper)</td>
<td>-</td>
<td>-</td>
<td>0.07</td>
</tr>
</tbody>
</table>
<p><em>Note: Decoupling Score closer to 0 indicates better separation between demand and inventory. Our TimesNet achieves -0.0264, outperforming the paper baseline of 0.07.</em></p>
<h3>Demand Forecasting Performance (DLinear)</h3>
<p>7-day ahead forecast after latent demand recovery:</p>
<table class="experiment-table">
<thead>
<tr>
<th>Configuration</th>
<th>WAPE (%)</th>
<th>Improvement</th>
</tr>
</thead>
<tbody>
<tr>
<td>Original Data (No Decoder)</td>
<td>37.93%</td>
<td>Baseline</td>
</tr>
<tr>
<td>Original Data (With Decoder)</td>
<td>37.55%</td>
<td>+1.0%</td>
</tr>
<tr>
<td>Imputed Data (No Decoder)</td>
<td>32.62%</td>
<td>+13.9%</td>
</tr>
<tr class="best-result">
<td>Imputed Data + Decoder</td>
<td class="best-result">31.94%</td>
<td class="best-result">+15.7%</td>
</tr>
</tbody>
</table>
<div class="metrics-grid">
<div class="metric-card">
<span class="metric-value">31.94%</span>
<span class="metric-label">WAPE (Best)</span>
</div>
<div class="metric-card">
<span class="metric-value">-0.0264</span>
<span class="metric-label">Decoupling Score</span>
</div>
<div class="metric-card">
<span class="metric-value">15.7%</span>
<span class="metric-label">Improvement</span>
</div>
<div class="metric-card">
<span class="metric-value">50K</span>
<span class="metric-label">Time Series</span>
</div>
</div>
<img src="/images/project-3/demand-forecasting.png" alt="Demand Forecasting Performance Comparison" style="width: 100%; max-width: 800px; margin: 1.5rem auto; display: block; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
<h3>Key Findings</h3>
<ul>
<li><strong>Imputation Matters:</strong> Using latent demand recovery improved WAPE by 2.1% - filling missing values with ML beats forward fill</li>
<li><strong>Decoder Helps:</strong> Future covariates via decoder improved accuracy by 0.8%, worth the small overhead</li>
<li><strong>Speed vs Accuracy:</strong> DLinear matched TimesNet accuracy while being 12x faster to train</li>
<li><strong>Hourly Granularity:</strong> 16-hour daily granularity captured intraday patterns that daily aggregates miss</li>
<li><strong>Weather Features:</strong> Temperature and precipitation were surprisingly important (15% feature importance)</li>
</ul>
<div class="key-insight">
<h4>Key Insight</h4>
<p>Latent demand recovery improved forecast accuracy by 15.7%. This validates the hypothesis that correcting demand censoring bias is more impactful than just selecting better forecasting models. The combination of TimesNet (recovery) + DLinear (forecasting) outperforms using raw historical data with any model.</p>
</div>
`
},
deployment: {
title: "Deployment & Integration",
icon: "fa-rocket",
content: `
<h3>System Architecture</h3>
<div class=\"deployment-info\">
<h4>Three-Tier Architecture</h4>
<p><strong>AI/ML Layer:</strong> PyTorch models with ONNX export for production inference</p>
<p><strong>Backend API:</strong> FastAPI server exposing REST endpoints</p>
<p><strong>Frontend Dashboard:</strong> React application for visualization and monitoring</p>
</div>
<h3>Project Structure</h3>
<div class=\"code-block\">
<pre><code>Demand-Forecasting-ERP/
├── ai/
│ ├── demand_forecasting/
│ │ ├── exp/ # Training experiments
│ │ │ └── exp_dlinear.py # DLinear training script
│ │ ├── checkpoints/ # Saved models
│ │ ├── main.py # Training entry point
│ │ ├── inference_dlinear.py # Production inference
│ │ └── test_wape.py # Evaluation script
│ ├── latent_demand_recovery/
│ │ ├── exp/ # Imputation experiments
│ │ ├── checkpoints/ # Imputation models
│ │ └── impute.py # Inference script
│ ├── inventory_optimization_module/
│ │ └── main_demo.py # Reorder point optimization
│ ├── model/
│ │ ├── dlinear.py # DLinear implementation
│ │ ├── timesnet.py # TimesNet implementation
│ │ └── baseline_models.py # ARIMA, LSTM baselines
│ └── data_utils/
│ └── load_data.py # Data preprocessing
├── backend/
│ ├── app/
│ │ ├── main.py # FastAPI application
│ │ ├── routes/ # API endpoints
│ │ ├── services/ # Business logic
│ │ └── models/ # SQLAlchemy ORM
│ └── requirements.txt
└── frontend/
├── src/
│ ├── components/ # React components
│ ├── pages/ # Dashboard, Analytics
│ └── services/ # API clients
└── package.json</code></pre>
</div>
<h3>Running the System</h3>
<div class=\"code-block\">
<pre><code># Train models
cd ai
uv sync
source .venv/bin/activate
# Train DLinear forecasting model
bash demand_forecasting/train_all.sh
# Train imputation model
cd latent_demand_recovery/exp
python dlinear.py
# Start backend API
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000
# Start frontend
cd frontend
npm install
npm start</code></pre>
</div>
<h3>API Endpoints</h3>
<ul>
<li><strong>POST /api/v1/forecasts/predict:</strong> Generate 7-day demand forecast for product</li>
<li><strong>POST /api/v1/optimize/supply:</strong> Calculate optimal reorder points and safety stock</li>
<li><strong>GET /api/v1/analytics/dashboard:</strong> Fetch aggregated metrics for dashboard</li>
<li><strong>GET /api/v1/analytics/accuracy:</strong> Model performance metrics over time</li>
</ul>
<h3>Inventory Optimization Strategies</h3>
<p>Comparing AI-driven approach against traditional ERP baselines:</p>
<table class="config-table">
<thead>
<tr>
<th>Strategy</th>
<th>Method</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr>
<td>Rule-Based (Min/Max)</td>
<td>Static thresholds</td>
<td>Odoo/SAP baseline</td>
</tr>
<tr>
<td>Newsvendor Model</td>
<td>Probabilistic optimization</td>
<td>Classical OR baseline</td>
</tr>
<tr>
<td>AI-DDMRP (Ours)</td>
<td>Dynamic buffer + AI forecast</td>
<td>Proposed method</td>
</tr>
</tbody>
</table>
<h3>Adaptive Retraining Process</h3>
<p>Three-strategy approach based on WAPE degradation:</p>
<img src="/images/project-3/adaptive-inference-algorithm.png" alt="Adaptive Inference Algorithm" style="width: 100%; max-width: 800px; margin: 1.5rem auto; display: block; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
<ul>
<li><strong>Strategy A (Full Retraining):</strong> Retrain both TimesNet (imputation) + DLinear (forecasting) - highest cost, best accuracy</li>
<li><strong>Strategy B (Partial):</strong> Reuse imputation model, only retrain DLinear - balanced approach</li>
<li><strong>Strategy C (Direct Inference):</strong> Use existing models - fastest, lowest cost, limited accuracy</li>
</ul>
<p>System monitors WAPE on new data weekly and automatically selects the appropriate strategy.</p>
<h3>Simulation Engine</h3>
<p>Monte Carlo simulation comparing three inventory strategies:</p>
<ul>
<li><strong>Rule-Based:</strong> Fixed reorder point (ROP = d × L + SS) - static thresholds like Odoo/SAP</li>
<li><strong>Newsvendor:</strong> Probabilistic optimization based on normal distribution - classical OR approach</li>
<li><strong>AI-DDMRP:</strong> Dynamic buffer zones updated with AI forecasts - our proposed method</li>
</ul>
<p>Simulation includes: lead time, FIFO spoilage, holding cost, stockout cost, order cost</p>
<h3>Technical Achievements</h3>
<ul>
<li><strong>Latent Demand Recovery:</strong> Decoupling Score -0.0264 (beats paper baseline 0.07 by 131%)</li>
<li><strong>Forecast Accuracy:</strong> WAPE 31.94% (15.7% improvement over raw censored data)</li>
<li><strong>Model Efficiency:</strong> TimesNet with ~1M parameters balanced accuracy and training cost</li>
<li><strong>Ablation Insights:</strong> Decoder improves WAPE by 2.1% when combined with imputed data</li>
<li><strong>Full-Stack System:</strong> AI models + FastAPI backend + React dashboard + simulation engine</li>
</ul>
<div class="key-insight">
<h4>Key Insight</h4>
<p>The two-stage pipeline (TimesNet recovery → DLinear forecasting) proved essential. Attempting to forecast directly on censored data, even with advanced models, systematically underestimates true demand. Correcting the data first beats optimizing the model second.</p>
</div>
<p style="margin-top: 1.5rem;"><strong>Tech Stack:</strong></p>
<ul>
<li><strong>AI/ML:</strong> PyTorch, TimesNet, DLinear, pandas, scikit-learn</li>
<li><strong>Backend:</strong> FastAPI (Python 3.11), uvicorn</li>
<li><strong>Frontend:</strong> React, Chart.js, Material-UI</li>
<li><strong>Tools:</strong> uv (package manager), Git</li>
</ul>
<h3>Resources</h3>
<a href="https://github.com/LeHung1705/Demand-Forecasting-and-Supply-Optimization-for-ERP-system.git" target="_blank" class="github-link">
<i class="fab fa-github"></i>
View Full Source Code
</a>
`
}
}
};
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