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aitf-its-tim3-dfk/ministral-8b-merged-ws3

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/ ── Mobile ── / @media (max-width: 640px) { .fo-hero img { height: 200px; } .fo-name { font-size: 1.9rem; } .fo-ident { padding: 70px 24px 24px; } .fo-shead { padding: 20px 24px 0; } .fo-sbody { padding: 0 24px 32px; } .fo-grid { grid-template-columns: 1fr; } .fo-metrics { grid-template-columns: repeat(2, 1fr); } .fo-row { grid-template-columns: 1fr; gap: 2px; } .fo-k::after { content: ''; } .fo-sep, .fo-div { margin-left: 24px; margin-right: 24px; padding-left: 24px; padding-right: 24px; } } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>KomdigiITS-8B-DFK-MergedClassification</title> </head> <body> <div class="fo">

<div class="fo-hero"> <img src="dfkherobanner.png" alt="image"> <div class="fo-ident"> <h1 class="fo-name">KomdigiITS-8B-DFK<br>Merged Classification</h1> <span class="fo-tagline">Ministral-3-8B &middot; SLERP Merge &middot; Multimodal + Text</span> <div class="fo-badge">SLERP &middot; 0.30 multimodal &middot; 0.70 text</div> </div> </div>

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<!-- ─── 01 · Overview ─── --> <div class="fo-section"> <div class="fo-shead"> <span class="fo-snum">01</span> <span class="fo-stitle">Overview</span> </div> <div class="fo-sbody"> <p>A <strong>SLERP-merged</strong> full model combining two specialized LoRA adapters into a single unified checkpoint. The merge blends the <a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification">8B Multimodal Multimodal classifier</a> with the <a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-TextClassification">text-only DFK classifier</a>, both built on <a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-CPT">KomdigiITS-8B-DFK-CPT</a> (Ministral-3-8B-Base-2512).</p> <p>The result is a model that operates in <strong>two modes</strong>: image-based multimodal classification and text-only classification &mdash; strong performance on both tasks from a single set of weights.</p> <div class="fo-note"> <strong>&#10038; Note:</strong> Best SLERP blend from a parameter sweep (<code>w=0.30</code> multimodal, <code>w=0.70</code> text). Parent adapters were merged into base model weights, producing a standalone model with no adapter overhead. </div> </div> </div>

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<!-- ─── 02 · Model Details ─── --> <div class="fo-section"> <div class="fo-shead"> <span class="fo-snum">02</span> <span class="fo-stitle">Model Details</span> </div> <div class="fo-sbody"> <div class="fo-grid"> <div class="fo-card"> <div class="fo-chead">Identity</div> <div class="fo-row"><span class="fo-k">Developed</span><span class="fo-v">DFK Tim 1 & Tim 3 ITS</span></div> <div class="fo-row"><span class="fo-k">Type</span><span class="fo-v">Merged model (SLERP)</span></div> <div class="fo-row"><span class="fo-k">Language</span><span class="fo-v">Indonesian</span></div> <div class="fo-row"><span class="fo-k">Modes</span><span class="fo-v">Multimodal + Text-only</span></div> </div> <div class="fo-card fo-card--sage"> <div class="fo-chead">Architecture</div> <div class="fo-row"><span class="fo-k">Base</span><span class="fo-v"><a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-CPT">KomdigiITS-8B-DFK-CPT</a></span></div> <div class="fo-row"><span class="fo-k">Arch</span><span class="fo-v">Mistral3ForConditionalGeneration</span></div> <div class="fo-row"><span class="fo-k">Params</span><span class="fo-v">8B (full merged weights)</span></div> <div class="fo-row"><span class="fo-k">Precision</span><span class="fo-v">float16</span></div> </div> </div> <h3 class="fo-sub">Parent Models</h3> <div class="fo-grid"> <div class="fo-card"> <div class="fo-chead">Multimodal Parent &middot; w=0.30</div> <div class="fo-row"><span class="fo-k">Model</span><span class="fo-v"><a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification">8B-DFK-MultimodalClassification</a></span></div> <div class="fo-row"><span class="fo-k">Task</span><span class="fo-v">Multimodal &mdash; image + text</span></div> <div class="fo-row"><span class="fo-k">Dataset</span><span class="fo-v"><code>dfkvlmdatasetv3</code></span></div> </div> <div class="fo-card fo-card--sage"> <div class="fo-chead">Text Parent &middot; w=0.70</div> <div class="fo-row"><span class="fo-k">Model</span><span class="fo-v"><a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-TextClassification">8B-DFK-TextClassification</a></span></div> <div class="fo-row"><span class="fo-k">Task</span><span class="fo-v">Text-only DFK classification</span></div> <div class="fo-row"><span class="fo-k">Dataset</span><span class="fo-v"><code>dfktext_dataset</code></span></div> </div> </div> </div> </div>

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<!-- ─── 03 · Uses ─── --> <div class="fo-section"> <div class="fo-shead"> <span class="fo-snum">03</span> <span class="fo-stitle">Uses</span> </div> <div class="fo-sbody"> <h3 class="fo-sub">Direct Use</h3> <div class="fo-card fo-card--muted" style="margin-bottom:14px;"> <div class="fo-row"><span class="fo-v">This model supports <strong>two input modes</strong> from a single checkpoint:</span></div> </div> <div class="fo-grid"> <div class="fo-card"> <div class="fo-chead">Mode 1 &middot; Multimodal</div> <div class="fo-row"><span class="fo-v">Image-based content moderation. Given a social media screenshot with contextual metadata, classifies into 4 labels: <code>netral</code>, <code>disinformasi</code>, <code>fitnah</code>, or <code>ujaran kebencian</code>.</span></div> </div> <div class="fo-card fo-card--sage"> <div class="fo-chead">Mode 2 &middot; Text-Only</div> <div class="fo-row"><span class="fo-v">Text-based DFK detection using article references. Classifies into 5 labels: <code>Fakta</code>, <code>Disinformasi</code>, <code>Fitnah</code>, <code>Ujaran Kebencian</code>, or <code>Non-DFK</code>.</span></div> </div> </div> <h3 class="fo-sub">Out-of-Scope Use</h3> <div class="fo-card fo-card--warn"> <div class="fo-row"><span class="fo-v">Not intended for general-purpose vision-language or text generation tasks. Specialized for the DFK detection pipeline &mdash; should not be used for content moderation in other languages or domains without further fine-tuning.</span></div> </div> </div> </div>

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<!-- ─── 04 · Evaluation ─── --> <div class="fo-section"> <div class="fo-shead"> <span class="fo-snum">04</span> <span class="fo-stitle">Evaluation</span> </div> <div class="fo-sbody"> <p>Evaluated on held-out validation splits using greedy decoding (<code>temperature=0.0</code>) and BERTScore (<code>bert-base-multilingual-cased</code>).</p>

<h3 class="fo-sub">Multimodal Task (Image + Text)</h3> <div class="fo-metrics"> <div class="fo-metric fo-metric--sage"> <div class="fo-mval">88.5</div> <div class="fo-mlbl">Accuracy</div> </div> <div class="fo-metric"> <div class="fo-mval">89.4</div> <div class="fo-mlbl">F1 Weighted</div> </div> <div class="fo-metric"> <div class="fo-mval">77.3</div> <div class="fo-mlbl">BERTScore F1</div> </div> </div>

<h3 class="fo-sub">Text-Only Task</h3> <div class="fo-metrics"> <div class="fo-metric fo-metric--sage"> <div class="fo-mval">91.0</div> <div class="fo-mlbl">Accuracy</div> </div> <div class="fo-metric"> <div class="fo-mval">92.9</div> <div class="fo-mlbl">F1 Weighted</div> </div> <div class="fo-metric"> <div class="fo-mval">77.6</div> <div class="fo-mlbl">BERTScore F1</div> </div> </div>

<details> <summary>Parent Model Comparison</summary> <div class="fo-drop"> <p>The merge trades a small amount of Multimodal accuracy for a <strong>massive improvement</strong> in text-only performance &mdash; turning a Multimodal-only model into a genuinely dual-mode classifier.</p> <table class="fo-cmp"> <thead> <tr> <th>Model</th> <th>MM Acc</th> <th>MM F1w</th> <th>MM BERT</th> <th>MM ROUGE</th> <th>Text Acc</th> <th>Text F1w</th> <th>Text BERT</th> <th>Text ROUGE</th> </tr> </thead> <tbody> <tr> <td>Multimodal</td> <td class="fo-cmp-warm">92.5</td> <td class="fo-cmp-warm">92.3</td> <td class="fo-cmp-warm">80.0</td> <td class="fo-cmp-warm">38.7</td> <td class="fo-cmp-dim">77.5</td> <td class="fo-cmp-dim">70.8</td> <td class="fo-cmp-dim">73.1</td> <td class="fo-cmp-dim">19.0</td> </tr> <tr> <td>Text Adapter</td> <td class="fo-cmp-dim">1.5</td> <td class="fo-cmp-dim">2.8</td> <td class="fo-cmp-dim">69.6</td> <td class="fo-cmp-dim">16.2</td> <td class="fo-cmp-warm">84.0</td> <td class="fo-cmp-warm">89.0</td> <td class="fo-cmp-warm">80.7</td> <td class="fo-cmp-warm">41.6</td> </tr> <tr class="fo-cmp-row-merge"> <td><strong>SLERP Merge</strong></td> <td class="fo-cmp-sage">88.5</td> <td class="fo-cmp-sage">89.4</td> <td class="fo-cmp-sage">77.3</td> <td class="fo-cmp-sage">31.6</td> <td class="fo-cmp-sage">91.0</td> <td class="fo-cmp-sage">92.9</td> <td class="fo-cmp-sage">77.6</td> <td class="fo-cmp-sage">31.5</td> </tr> </tbody> </table> <div class="fo-note fo-note--sage"> <strong>&#10038; Key takeaway:</strong> The merge <em>surpasses</em> the text adapter's own accuracy (91.0 vs 84.0) while retaining 95.7% of the Multimodal model's classification accuracy. </div> </div> </details> </div> </div>

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<!-- ─── 05 · Merge Details ─── --> <div class="fo-section"> <div class="fo-shead"> <span class="fo-snum">05</span> <span class="fo-stitle">Merge Details</span> </div> <div class="fo-sbody"> <h3 class="fo-sub">Method</h3> <div class="fo-card" style="margin-bottom:14px;"> <div class="fo-row"><span class="fo-k">Method</span><span class="fo-v">SLERP (Spherical Linear Interpolation)</span></div> <div class="fo-row"><span class="fo-k">Weight</span><span class="fo-v">0.30 multimodal / 0.70 text</span></div> <div class="fo-row"><span class="fo-k">Selection</span><span class="fo-v">Best blend from WS3 sweep</span></div> <div class="fo-row"><span class="fo-k">Output</span><span class="fo-v">Full merged weights (no adapter)</span></div> </div> <h3 class="fo-sub">Process</h3> <div class="fo-card fo-card--muted" style="margin-bottom:14px;"> <div class="fo-row"><span class="fo-v">Both parent LoRA adapters (Multimodal + text) were first merged into the base model (<code>KomdigiITS-8B-DFK-CPT</code>) to produce two full-weight checkpoints. These were then interpolated via SLERP at various weight ratios, with each blend evaluated on both Multimodal and text-only benchmarks to find the optimal trade-off.</span></div> </div> <h3 class="fo-sub">Label Classes</h3> <div class="fo-grid"> <div class="fo-card"> <div class="fo-chead">Multimodal Mode &middot; 4 Classes</div> <div class="fo-row"><span class="fo-k">Netral</span><span class="fo-v">Factual / non-DFK &mdash; no violation</span></div> <div class="fo-row"><span class="fo-k">Disinfo</span><span class="fo-v">Claims contradicting established facts</span></div> <div class="fo-row"><span class="fo-k">Fitnah</span><span class="fo-v">False claims targeting an individual</span></div> <div class="fo-row"><span class="fo-k">Ujrn Kbnci</span><span class="fo-v">Hate speech targeting SARA identity</span></div> </div> <div class="fo-card fo-card--sage"> <div class="fo-chead">Text Mode &middot; 5 Classes</div> <div class="fo-row"><span class="fo-k">Non-DFK</span><span class="fo-v">Content unrelated to DFK categories</span></div> <div class="fo-row"><span class="fo-k">Fakta</span><span class="fo-v">Factual content, verified true</span></div> <div class="fo-row"><span class="fo-k">Disinfo</span><span class="fo-v">Claims contradicting established facts</span></div> <div class="fo-row"><span class="fo-k">Fitnah</span><span class="fo-v">False claims targeting an individual</span></div> <div class="fo-row"><span class="fo-k">Ujrn Kbnci</span><span class="fo-v">Hate speech targeting SARA identity</span></div> </div> </div> </div> </div>

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<!-- ─── 06 · Input Formats ─── --> <div class="fo-section"> <div class="fo-shead"> <span class="fo-snum">06</span> <span class="fo-stitle">Input Formats</span> </div> <div class="fo-sbody"> <p>Both modes use the <code>ministral_3</code> chat template (<code>[INST]</code> / <code>[/INST]</code> delimiters). The default Ministral system prompt is included when no explicit system message is provided.</p>

<details> <summary>Multimodal Mode &middot; dfkvlmdatasetv3</summary> <div class="fo-drop"> <p>Image-based classification with contextual metadata. Note: the instruction and context fields are <strong>directly concatenated</strong> with no separator.</p> <pre><code>&lt;s&gt;[SYSTEMPROMPT]...default Ministral system prompt...[/SYSTEM_PROMPT][INST]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}[IMG][/INST]Label: {label}

Analisis: {analisis}&lt;/s&gt;</code></pre>

<h3 class="fo-sub">Input Fields</h3> <div class="fo-card" style="margin-bottom:14px;"> <div class="fo-row"><span class="fo-k">Ringkasan</span><span class="fo-v">Content summary. Concatenation of image caption and user-provided text.</span></div> <div class="fo-row"><span class="fo-k">Klaim</span><span class="fo-v">Core claim extracted from the content, used as a web search query for fact-checking.</span></div> <div class="fo-row"><span class="fo-k">Fakta</span><span class="fo-v">Verification context from web search. Defaults to <code>"Tidak ditemukan sumber yang valid."</code> if none found.</span></div> <div class="fo-row"><span class="fo-k">[IMG]</span><span class="fo-v">Screenshot of the social media post being analyzed.</span></div> </div> <h3 class="fo-sub">Output Fields</h3> <div class="fo-card"> <div class="fo-row"><span class="fo-k">Label</span><span class="fo-v">One of <code>netral</code>, <code>disinformasi</code>, <code>fitnah</code>, or <code>ujaran kebencian</code>.</span></div> <div class="fo-row"><span class="fo-k">Analisis</span><span class="fo-v">Free-form Indonesian-language reasoning for the classification.</span></div> </div> </div> </details>

<details class="fo-det--sage"> <summary>Text-Only Mode &middot; dfktextdataset</summary> <div class="fo-drop"> <p>Text-only classification with explicit system prompt and article references. Uses <code>mergelabels=false</code> (5 classes).</p> <pre><code>&lt;s&gt;[SYSTEMPROMPT]Anda adalah sistem deteksi konten DFK berbasis artikel rujukan. Tugas Anda adalah membandingkan klaim dengan artikel rujukan, lalu mengklasifikasikan teks ke dalam salah satu label: Fakta, Disinformasi, Fitnah, Ujaran Kebencian, atau Non-DFK. Jawab dengan format: Label: NamaLabel. penjelasan: ...[/SYSTEM_PROMPT][INST]{klaim}

Artikel Rujukan: {fakta}[/INST]Label: {label}. penjelasan: {analisis}&lt;/s&gt;</code></pre>

<h3 class="fo-sub">Input Fields</h3> <div class="fo-card" style="margin-bottom:14px;"> <div class="fo-row"><span class="fo-k">Klaim</span><span class="fo-v">The claim text to be verified, from the <code>input</code> column (before <code>Artikel Rujukan:</code>).</span></div> <div class="fo-row"><span class="fo-k">Fakta</span><span class="fo-v">Reference articles for fact-checking, from the <code>input</code> column (after <code>Artikel Rujukan:</code>).</span></div> </div> <h3 class="fo-sub">Output Fields</h3> <div class="fo-card"> <div class="fo-row"><span class="fo-k">Label</span><span class="fo-v">One of <code>Fakta</code>, <code>Disinformasi</code>, <code>Fitnah</code>, <code>Ujaran Kebencian</code>, or <code>Non-DFK</code>.</span></div> <div class="fo-row"><span class="fo-k">Penjelasan</span><span class="fo-v">Indonesian-language explanation for the classification decision.</span></div> </div> </div> </details> </div> </div>

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<!-- ─── 07 · Model Sources ─── --> <div class="fo-section"> <div class="fo-shead"> <span class="fo-snum">07</span> <span class="fo-stitle">Model Sources</span> </div> <div class="fo-sbody"> <div class="fo-card fo-card--muted"> <div class="fo-row"><span class="fo-k">Framework</span><span class="fo-v"><a href="https://github.com/aitf-its-tim3-dfk/SITA">SITA</a></span></div> <div class="fo-row"><span class="fo-k">Base model</span><span class="fo-v"><a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-CPT">aitf-komdigi/KomdigiITS-8B-DFK-CPT</a></span></div> <div class="fo-row"><span class="fo-k">Multimodal parent</span><span class="fo-v"><a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification">KomdigiITS-8B-DFK-MultimodalClassification</a></span></div> <div class="fo-row"><span class="fo-k">Text parent</span><span class="fo-v"><a href="https://huggingface.co/aitf-komdigi/KomdigiITS-8B-DFK-TextClassification">KomdigiITS-8B-DFK-TextClassification</a></span></div> </div> </div> </div>

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<!-- ─── 08 · Framework Versions ─── --> <div class="fo-section"> <div class="fo-shead"> <span class="fo-snum">08</span> <span class="fo-stitle">Framework Versions</span> </div> <div class="fo-sbody"> <div class="fo-card fo-card--muted"> <div class="fo-row"><span class="fo-k">Transformers</span><span class="fo-v">5.5.0</span></div> <div class="fo-row"><span class="fo-k">PyTorch</span><span class="fo-v">2.11.0+cu128</span></div> <div class="fo-row"><span class="fo-k">Unsloth</span><span class="fo-v">2026.5.5</span></div> </div> </div> </div>

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