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aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification

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/ ── Code ── / .mc pre { background: #2a2240; padding: 14px 16px; margin: 0 0 18px; border: 1px solid var(--accent3); border-left: 3px solid var(--accent); overflow-x: auto; color: #d4cce8; box-shadow: 0 2px 12px var(--glow); } .mc pre:last-child { margin-bottom: 0; } .mc pre code { font-family: var(--mono); font-size: 0.74rem; line-height: 1.6; background: none; color: inherit; padding: 0; display: block; border: none; } .mc code { font-family: var(--mono); font-size: 0.85em; color: var(--bright); background: rgba(139,123,198,0.08); padding: 2px 6px; border: 1px solid rgba(139,123,198,0.12); } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>KomdigiITS-0.8B-DFK-MultimodalClassification</title> <link rel="preconnect" href="https://fonts.googleapis.com"> <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800;900&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> </head> <body> <div class="mc">

<div class="mc-hero"> <img src="dfkherobanner.png" alt="image"> </div> <div class="mc-title"> <div class="mc-card"> <h1 class="mc-name">KomdigiITS-0.8B-DFK<br>Multimodal Classification</h1> <span class="mc-base">Qwen3.5-0.8B · LoRA · Vision-Language</span> </div> </div>

<div class="mc-section"> <div class="mc-shead"> <div class="mc-emblem"><span class="mc-glyph">✺</span></div> <span class="mc-stitle">Overview</span> </div> <div class="mc-sbody"> <p>A LoRA adapter fine-tuned on <a href="https://huggingface.co/unsloth/Qwen3.5-0.8B">Qwen3.5-0.8B</a> as a Vision-Language Model for multimodal content classification. The model analyzes social media screenshots and classifies them into four categories: <code>netral</code>, <code>disinformasi</code>, <code>fitnah</code>, and <code>ujaran kebencian</code>.</p> <p>Trained using the <a href="https://github.com/aitf-its-tim3-dfk/SITA">SITA</a> framework with Unsloth's SFT pipeline. Given an image, the model produces a structured analysis with a classification label and a detailed Indonesian-language reasoning of any violations found.</p> <div class="mc-note"> <strong>♦ Note:</strong> This is the final checkpoint from Workshop 3 (<code>final-qwen35-0.8b-ws3</code>), trained on the DFK VLM Dataset V3 with augmented train/val splits. </div> </div> </div>

<div class="mc-section"> <div class="mc-shead"> <div class="mc-emblem"><span class="mc-glyph">✺</span></div> <span class="mc-stitle">Model Details</span> </div> <div class="mc-sbody"> <div class="mc-grid"> <div> <h3 class="mc-sub">Identity</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Developed by:</span><span class="mc-val">DFK Tim 3 ITS</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Model type:</span><span class="mc-val">VLM — LoRA adapter</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Language:</span><span class="mc-val">Indonesian</span></div> </div> </div> <div> <h3 class="mc-sub">Architecture</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Base model:</span><span class="mc-val"><a href="https://huggingface.co/unsloth/Qwen3.5-0.8B">unsloth/Qwen3.5-0.8B</a></span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Arch:</span><span class="mc-val">Qwen3_5ForCausalLM</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Parameters:</span><span class="mc-val">0.8B (base)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Precision:</span><span class="mc-val">bfloat16</span></div> </div> </div> </div> </div> </div>

<div class="mc-section"> <div class="mc-shead"> <div class="mc-emblem"><span class="mc-glyph">✺</span></div> <span class="mc-stitle">Uses</span> </div> <div class="mc-sbody"> <h3 class="mc-sub">Direct Use</h3> <div class="mc-data"> <p style="margin:0;">Image-based content moderation classification for Indonesian social media. Given a screenshot, the model produces a structured analysis with a classification label (<code>netral</code>, <code>disinformasi</code>, <code>fitnah</code>, or <code>ujaran kebencian</code>) and a detailed reasoning in Indonesian.</p> </div> <h3 class="mc-sub">Out-of-Scope Use</h3> <div class="mc-data mc-data--peach"> <p style="margin:0;">This model is not intended for general-purpose vision-language tasks. It is specialized for the DFK disinformation detection pipeline and should not be used for content moderation in other languages or domains without further fine-tuning.</p> </div> </div> </div>

<div class="mc-section"> <div class="mc-shead"> <div class="mc-emblem"><span class="mc-glyph">✺</span></div> <span class="mc-stitle">Evaluation</span> </div> <div class="mc-sbody"> <p>Evaluated on the held-out validation split using greedy decoding (<code>temperature=0.0</code>) and BERTScore (<code>bert-base-multilingual-cased</code>).</p> <div class="mc-metrics"> <div class="mc-metric mc-metric--highlight"> <div class="mc-metric-val">92.5</div> <div class="mc-metric-label">Accuracy</div> </div> <div class="mc-metric"> <div class="mc-metric-val">89.3</div> <div class="mc-metric-label">F1 Macro</div> </div> <div class="mc-metric"> <div class="mc-metric-val">92.8</div> <div class="mc-metric-label">F1 Weighted</div> </div> <div class="mc-metric"> <div class="mc-metric-val">79.5</div> <div class="mc-metric-label">BERTScore F1</div> </div> </div> <div class="mc-drop"> <details> <summary>Per-Class Breakdown</summary> <div class="mc-drop-body"> <div class="mc-data" style="margin-bottom:0;"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Netral:</span><span class="mc-val">P 0.954 · R 0.941 · F1 0.948 · n=970</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Ujaran Kbnci:</span><span class="mc-val">P 0.982 · R 0.930 · F1 0.955 · n=867</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Disinformasi:</span><span class="mc-val">P 0.943 · R 0.888 · F1 0.915 · n=392</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Fitnah:</span><span class="mc-val">P 0.651 · R 0.901 · F1 0.756 · n=213</span></div> </div> </div> </details> </div> <div class="mc-drop"> <details> <summary>BERTScore Details</summary> <div class="mc-drop-body"> <div class="mc-data" style="margin-bottom:0;"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Precision:</span><span class="mc-val">0.797</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Recall:</span><span class="mc-val">0.793</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">F1:</span><span class="mc-val">0.795</span></div> </div> </div> </details> </div> </div> </div>

<div class="mc-section"> <div class="mc-shead"> <div class="mc-emblem"><span class="mc-glyph">✺</span></div> <span class="mc-stitle">Training Details</span> </div> <div class="mc-sbody"> <h3 class="mc-sub">Training Data</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Dataset:</span><span class="mc-val"><code>dfkvlmdatasetv3</code> (augmented on <code>fitnah</code> class)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Split mode:</span><span class="mc-val">Fixed splits (trainaug.csv / valaug.csv)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Train size:</span><span class="mc-val">14,293 samples</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Val size:</span><span class="mc-val">2,831 samples</span></div> </div> <h3 class="mc-sub">Label Classes</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Netral:</span><span class="mc-val">Factual content or non-DFK material — no violation detected</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Disinformasi:</span><span class="mc-val">Claims that contradict established facts, not directed at a specific person</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Fitnah:</span><span class="mc-val">False claims directed at a specific individual (defamation)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Ujaran Kbnci:</span><span class="mc-val">Hate speech targeting ethnicity, religion, race, or intergroup identity (SARA)</span></div> </div> <div class="mc-drop"> <details> <summary>Dataset Distribution</summary> <div class="mc-drop-body"> <div class="mc-data"> <div class="mc-row"><span class="mc-mark mc-mark--peach"></span><span class="mc-label" style="min-width:140px;">Train (aug)</span><span class="mc-val" style="font-family:var(--mono);font-size:0.82rem;">14,293 total</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label" style="min-width:140px;">Netral:</span><span class="mc-val">3,883 (27.2%)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label" style="min-width:140px;">Fitnah:</span><span class="mc-val">3,846 (26.9%)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label" style="min-width:140px;">Ujaran Kbnci:</span><span class="mc-val">3,484 (24.4%)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label" style="min-width:140px;">Disinformasi:</span><span class="mc-val">3,080 (21.6%)</span></div> </div> <div class="mc-data" style="margin-top:12px;"> <div class="mc-row"><span class="mc-mark mc-mark--peach"></span><span class="mc-label" style="min-width:140px;">Val (aug)</span><span class="mc-val" style="font-family:var(--mono);font-size:0.82rem;">2,831 total</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label" style="min-width:140px;">Netral:</span><span class="mc-val">970 (34.3%)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label" style="min-width:140px;">Ujaran Kbnci:</span><span class="mc-val">867 (30.6%)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label" style="min-width:140px;">Disinformasi:</span><span class="mc-val">765 (27.0%)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label" style="min-width:140px;">Fitnah:</span><span class="mc-val">229 (8.1%)</span></div> </div> </div> </details> </div> <div class="mc-grid"> <div> <h3 class="mc-sub">LoRA Configuration</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">r:</span><span class="mc-val">16</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Alpha:</span><span class="mc-val">16</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Dropout:</span><span class="mc-val">0.1</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Targets:</span><span class="mc-val">all-linear</span></div> <div class="mc-row"><span class="mc-mark mc-mark--peach"></span><span class="mc-label">Vision:</span><span class="mc-val">✓ finetuned</span></div> <div class="mc-row"><span class="mc-mark mc-mark--peach"></span><span class="mc-label">Language:</span><span class="mc-val">✓ finetuned</span></div> <div class="mc-row"><span class="mc-mark mc-mark--peach"></span><span class="mc-label">Attention:</span><span class="mc-val">✓ finetuned</span></div> <div class="mc-row"><span class="mc-mark mc-mark--peach"></span><span class="mc-label">MLP:</span><span class="mc-val">✓ finetuned</span></div> </div> </div> <div> <h3 class="mc-sub">Hyperparameters</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Epochs:</span><span class="mc-val">3</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Batch size:</span><span class="mc-val">32</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">LR:</span><span class="mc-val">2e-4</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Optimizer:</span><span class="mc-val">AdamW 8-bit</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Max seq len:</span><span class="mc-val">2048</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Grad accum:</span><span class="mc-val">1</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Grad ckpt:</span><span class="mc-val">unsloth</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Seed:</span><span class="mc-val">3407</span></div> </div> </div> </div> <h3 class="mc-sub">Trainer</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Type:</span><span class="mc-val"><code>unslothvlmsft</code> (Unsloth VLM SFT trainer)</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Train on:</span><span class="mc-val">Responses only</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Instr part:</span><span class="mc-val"><code><|imstart|>user\n</code></span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Resp part:</span><span class="mc-val"><code><|imstart|>assistant\n</code></span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Best model:</span><span class="mc-val">Selected by <code>evalloss</code> (lower is better)</span></div> </div> <div class="mc-drop"> <details> <summary>Prompt Template</summary> <div class="mc-drop-body"> <p>Each sample is formatted as a multi-turn conversation using <code>qwen3.5chatml</code>:</p> <pre><code><|imstart|>user Anda adalah seorang analis konten media sosial ahli. Diberikan tangkapan layar dari sebuah konten, tentukan label kategori pelanggaran dan berikan analisis detail mengenai pelanggaran yang ditemukan. Ringkasan: {ringkasan} Klaim: {klaim} Fakta: {fakta} <image> <|imend|> <|imstart|>assistant Label: {label}

Analisis: {analisis} <|im_end|></code></pre> <h3 class="mc-sub" style="margin-top:20px;">Input Fields</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Ringkasan:</span><span class="mc-val">Content summary. In the RAG pipeline this is the concatenation of the image caption (from a captioning model) and any user-provided text (e.g. post caption, tweet text). Effectively holds all available textual context about the content.</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Klaim:</span><span class="mc-val">The core claim extracted from the content, used as a web search query for fact-checking. Generated by an LLM from the ringkasan. Can also be a direct caption or user-provided text in simpler setups.</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Fakta:</span><span class="mc-val">Verification context retrieved via web search. Contains numbered search results with titles, descriptions, and source URLs. If no relevant sources are found, defaults to <code>"Tidak ditemukan sumber yang valid."</code></span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label"><image>:</span><span class="mc-val">Screenshot of the social media post being analyzed.</span></div> </div> <h3 class="mc-sub">Output Fields</h3> <div class="mc-data"> <div class="mc-row"><span class="mc-mark mc-mark--peach"></span><span class="mc-label">Label:</span><span class="mc-val">One of <code>netral</code>, <code>disinformasi</code>, <code>fitnah</code>, or <code>ujaran kebencian</code>.</span></div> <div class="mc-row"><span class="mc-mark mc-mark--peach"></span><span class="mc-label">Analisis:</span><span class="mc-val">Free-form Indonesian-language explanation of why the content was assigned its label, referencing the image, context, and any retrieved facts.</span></div> </div> </div> </details> </div>

<div class="mc-drop"> <details> <summary>Full Training Config</summary> <div class="mc-drop-body"> <pre><code>experiment_name: final-qwen35-0.8b-ws3 seed: 3407

reporting: wandb: true wandb_project: "DFK3"

model: name: unslothvlm pretrained: unsloth/Qwen3.5-0.8B kwargs: loadin4bit: false chattemplate: "sita/templates/qwen3.5_chatml.jinja"

adapter: name: unslothvlmlora kwargs: finetunevisionlayers: true finetunelanguagelayers: true finetuneattentionmodules: true finetunemlpmodules: true r: 16 loraalpha: 16 loradropout: 0.1 bias: "none" targetmodules: "all-linear" usegradientcheckpointing: "unsloth" randomstate: 3407

dataset: name: dfkvlmdataset_v3

training: numepochs: 3 batchsize: 32 learningrate: 2e-4 gradientaccumulationsteps: 1 loggingsteps: 1 savesteps: 100 evalsteps: 50 extra: seed: 3407 maxlength: 2048 loadbestmodelatend: true metricforbestmodel: evalloss greateris_better: false

trainer: name: unslothvlmsft kwargs: trainonresponsesonly: true instructionpart: "<|imstart|>user\n" responsepart: "<|imstart|>assistant\n" optim: adamw8bit

evaluation: name: vlmgen kwargs: maxnewtokens: 512 temperature: 0.0 bertmodel: bert-base-multilingual-cased batchsize: 16 numworkers: 11</code></pre>

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<div class="mc-section"> <div class="mc-shead"> <div class="mc-emblem"><span class="mc-glyph">✺</span></div> <span class="mc-stitle">Model Sources</span> </div> <div class="mc-sbody"> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Framework:</span><span class="mc-val"><a href="https://github.com/aitf-its-tim3-dfk/SITA">SITA</a></span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">W&B Run:</span><span class="mc-val"><a href="https://wandb.ai/aitfits2026-kementerian-komdigi/DFK3/runs/9ygdkx50">DFK3 / final-qwen35-0.8b-ws3</a></span></div> </div> </div> </div>

<div class="mc-section"> <div class="mc-shead"> <div class="mc-emblem"><span class="mc-glyph">✺</span></div> <span class="mc-stitle">Framework Versions</span> </div> <div class="mc-sbody"> <div class="mc-data"> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">TRL:</span><span class="mc-val">0.22.2</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Transformers:</span><span class="mc-val">5.3.0</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">PyTorch:</span><span class="mc-val">2.11.0+cu128</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Datasets:</span><span class="mc-val">4.3.0</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">PEFT:</span><span class="mc-val">0.19.0</span></div> <div class="mc-row"><span class="mc-mark"></span><span class="mc-label">Tokenizers:</span><span class="mc-val">0.22.2</span></div> </div> </div> </div>

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