File size: 16,688 Bytes
578c09c ab80cf5 578c09c ab80cf5 578c09c 2d57285 863d205 2d57285 adae325 3ccf587 eb961bf 3ccf587 2a9ce3d 6f56e7a 00c3d19 2c14ad1 c0e0992 ab80cf5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 |
---
title: README
emoji: 🐨
colorFrom: purple
colorTo: indigo
sdk: static
pinned: true
license: mit
---
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<div align="center">
<img src="https://huggingface.co/spaces/Caca-AI/README/resolve/main/logo.png" width="50%" alt="caca-AI" />
</div>
<hr>
<div align="center">
<a href="https://huggingface.co/Lyon28"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Lyon28-ffc107?color=ffc107&logoColor=white"/></a>
<br>
<a href="LICENSE">
<img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue.svg"/>
</a>
</div>
## 📋 Deskripsi
**Caca** adalah arsitektur Large Language Model (LLM) generasi terbaru yang menggabungkan berbagai teknik state-of-the-art dalam deep learning. Model ini dirancang dengan fokus pada **efisiensi**, **skalabilitas**, dan **performa tinggi**.
<blockquote style="border-left: 4px solid #4A90E2; padding-left: 16px; margin: 16px 0; color: #555;">
<p><strong>Caca</strong> itu eksperimen open-source Indonesian LLM yang dibuat dari nol secara individual dan bertahap. Bukan kompetitor siapa-siapa, cuma pengen eksplorasi apa yang bisa dilakukan dengan budget terbatas, passion unlimited, dan mindset collaborative. Kalau berguna buat orang lain, alhamdulillah. Kalau enggak, ya tetap fun kok.</p>
<p>Ini proyek eksplorasi, jadi kalau gagal ya bagian dari proses belajar. Kalau berhasil, itu bonus.</p>
</blockquote>
<!-- COMPARISON -->
<h2>📊 Perbandingan dengan Arsitektur Lain</h2>
<table width="100%">
<thead>
<tr style="background-color: #f0f0f0;">
<th width="25%">Fitur</th>
<th width="15%">Caca</th>
<th width="15%">LLaMA 2</th>
<th width="15%">Mistral</th>
<th width="15%">IndoGPT</th>
<th width="15%">GPT-2</th>
</tr>
</thead>
<tbody>
<!-- Basic Architecture -->
<tr>
<td colspan="6" style="background-color: #e3f2fd; padding: 10px;">
<b>🏗️ Arsitektur Dasar</b>
</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Status</b></td>
<td bgcolor="#fff3cd">⚠️ Untrained</td>
<td bgcolor="#d4edda">✅ Trained</td>
<td bgcolor="#d4edda">✅ Trained</td>
<td bgcolor="#d4edda">✅ Trained</td>
<td bgcolor="#d4edda">✅ Trained</td>
</tr>
<tr>
<td><b>Ukuran Model</b></td>
<td bgcolor="#d4edda">60+ variant<br><small>1M - 1T (semoga)</small></td>
<td bgcolor="#d4edda">7B / 13B / 70B</td>
<td bgcolor="#d4edda">7B</td>
<td bgcolor="#fff3cd">117M</td>
<td bgcolor="#fff3cd">117M - 1.5B</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Tipe Arsitektur</b></td>
<td>Decoder-only</td>
<td>Decoder-only</td>
<td>Decoder-only</td>
<td>Decoder-only</td>
<td>Decoder-only</td>
</tr>
<tr>
<td><b>Fungsi Aktivasi</b></td>
<td bgcolor="#d4edda">SwiGLU</td>
<td bgcolor="#d4edda">SwiGLU</td>
<td bgcolor="#d4edda">SwiGLU</td>
<td bgcolor="#f8d7da">GELU</td>
<td bgcolor="#f8d7da">GELU</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Normalisasi</b></td>
<td bgcolor="#d4edda">RMSNorm</td>
<td bgcolor="#d4edda">RMSNorm</td>
<td bgcolor="#d4edda">RMSNorm</td>
<td bgcolor="#f8d7da">LayerNorm</td>
<td bgcolor="#f8d7da">LayerNorm</td>
</tr>
<tr>
<td><b>Tahun Release</b></td>
<td>2025</td>
<td>2023</td>
<td>2023</td>
<td>2020</td>
<td>2019</td>
</tr>
<!-- Attention Mechanisms -->
<tr>
<td colspan="6" style="background-color: #e3f2fd; padding: 10px;">
<b>👁️ Mekanisme Attention</b>
</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Tipe Attention</b></td>
<td bgcolor="#d4edda">GQA (configurable)</td>
<td bgcolor="#d4edda">GQA</td>
<td bgcolor="#d4edda">GQA</td>
<td bgcolor="#f8d7da">MHA</td>
<td bgcolor="#f8d7da">MHA</td>
</tr>
<tr>
<td><b>Position Encoding</b></td>
<td bgcolor="#d4edda">RoPE + variants</td>
<td bgcolor="#d4edda">RoPE</td>
<td bgcolor="#d4edda">RoPE</td>
<td bgcolor="#f8d7da">Learned</td>
<td bgcolor="#f8d7da">Learned</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Max Context</b></td>
<td bgcolor="#d4edda">8K - 16K</td>
<td bgcolor="#fff3cd">4K</td>
<td bgcolor="#d4edda">32K</td>
<td bgcolor="#f8d7da">1K</td>
<td bgcolor="#f8d7da">1K</td>
</tr>
<tr>
<td><b>Sliding Window</b></td>
<td bgcolor="#d4edda">✅ Optional</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#d4edda">✅ 4K window</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Flash Attention</b></td>
<td bgcolor="#d4edda">✅ Flash Attn 2</td>
<td bgcolor="#d4edda">✅ Supported</td>
<td bgcolor="#d4edda">✅ Supported</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
</tr>
<tr>
<td><b>KV Cache Efficiency</b></td>
<td bgcolor="#d4edda">75% reduction<br><small>(GQA 4:1)</small></td>
<td bgcolor="#d4edda">~60% reduction</td>
<td bgcolor="#d4edda">75% reduction</td>
<td bgcolor="#f8d7da">No optimization</td>
<td bgcolor="#f8d7da">No optimization</td>
</tr>
<!-- Advanced Features -->
<tr>
<td colspan="6" style="background-color: #e3f2fd; padding: 10px;">
<b>🚀 Fitur Lanjutan</b>
</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Mixture of Experts</b></td>
<td bgcolor="#d4edda">✅ Optional<br><small>TopK + ExpertChoice</small></td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌<br><small>(Mixtral variant)</small></td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
</tr>
<tr>
<td><b>Multimodal</b></td>
<td bgcolor="#d4edda">✅ Native<br><small>Vision + Audio</small></td>
<td bgcolor="#f8d7da">❌<br><small>(LLaVA separate)</small></td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Config Flexibility</b></td>
<td bgcolor="#d4edda">✅ 50+ parameters<br><small>Toggle semua fitur</small></td>
<td bgcolor="#fff3cd">⚠️ Limited</td>
<td bgcolor="#fff3cd">⚠️ Limited</td>
<td bgcolor="#f8d7da">❌ Fixed</td>
<td bgcolor="#f8d7da">❌ Fixed</td>
</tr>
<tr>
<td><b>Layer Scale</b></td>
<td bgcolor="#d4edda">✅ Optional</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Stochastic Depth</b></td>
<td bgcolor="#d4edda">✅ Optional</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
<td bgcolor="#f8d7da">❌</td>
</tr>
<!-- Performance & Optimization -->
<tr>
<td colspan="6" style="background-color: #e3f2fd; padding: 10px;">
<b>⚡ Performa & Optimisasi</b>
</td>
</tr>
<tr>
<td><b>Inference Speed</b><br><small>(7B model, A100)</small></td>
<td bgcolor="#fff3cd">⚠️ TBD<br><small>(belum trained)</small></td>
<td bgcolor="#d4edda">~75 tok/s</td>
<td bgcolor="#d4edda">~78 tok/s</td>
<td bgcolor="#d4edda">~150 tok/s<br><small>(jauh lebih kecil)</small></td>
<td bgcolor="#d4edda">~120 tok/s<br><small>(jauh lebih kecil)</small></td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Memory Footprint</b><br><small>(7B, BF16)</small></td>
<td bgcolor="#d4edda">~14GB<br><small>(dengan GQA)</small></td>
<td bgcolor="#fff3cd">~14GB</td>
<td bgcolor="#d4edda">~14GB</td>
<td bgcolor="#d4edda">~500MB</td>
<td bgcolor="#d4edda">~500MB</td>
</tr>
<tr>
<td><b>Gradient Checkpointing</b></td>
<td bgcolor="#d4edda">✅ Full support</td>
<td bgcolor="#d4edda">✅ Supported</td>
<td bgcolor="#d4edda">✅ Supported</td>
<td bgcolor="#fff3cd">⚠️ Manual</td>
<td bgcolor="#fff3cd">⚠️ Manual</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Quantization</b></td>
<td bgcolor="#d4edda">✅ 8-bit/4-bit built-in</td>
<td bgcolor="#fff3cd">⚠️ Via external tools</td>
<td bgcolor="#fff3cd">⚠️ Via external tools</td>
<td bgcolor="#f8d7da">❌ Limited support</td>
<td bgcolor="#f8d7da">❌ Limited support</td>
</tr>
<tr>
<td><b>Multi-Backend Support</b></td>
<td bgcolor="#d4edda">✅ 4 backends<br><small>Flash/xFormers/SDPA/Standard</small></td>
<td bgcolor="#fff3cd">⚠️ 2 backends</td>
<td bgcolor="#fff3cd">⚠️ 2 backends</td>
<td bgcolor="#f8d7da">❌ Standard only</td>
<td bgcolor="#f8d7da">❌ Standard only</td>
</tr>
<!-- Language Support -->
<tr>
<td colspan="6" style="background-color: #e3f2fd; padding: 10px;">
<b>🌏 Dukungan Bahasa</b>
</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Bahasa Indonesia</b></td>
<td bgcolor="#fff3cd">⚠️ Belum trained<br><small>Designed for ID</small></td>
<td bgcolor="#f8d7da">❌ Poor<br><small>English-heavy</small></td>
<td bgcolor="#f8d7da">❌ Poor<br><small>English-heavy</small></td>
<td bgcolor="#d4edda">✅ Native</td>
<td bgcolor="#f8d7da">❌ Minimal</td>
</tr>
<tr>
<td><b>English</b></td>
<td bgcolor="#fff3cd">⚠️ TBD<br><small>Bilingual design</small></td>
<td bgcolor="#d4edda">✅ Excellent</td>
<td bgcolor="#d4edda">✅ Excellent</td>
<td bgcolor="#fff3cd">⚠️ Limited</td>
<td bgcolor="#d4edda">✅ Good</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Training Data</b></td>
<td bgcolor="#fff3cd">⚠️ To be trained<br><small>User's choice</small></td>
<td bgcolor="#d4edda">2T tokens<br><small>English-heavy</small></td>
<td bgcolor="#d4edda">Unknown<br><small>English-heavy</small></td>
<td bgcolor="#d4edda">23GB<br><small>Indonesian</small></td>
<td bgcolor="#d4edda">40GB<br><small>WebText</small></td>
</tr>
<tr>
<td><b>Vocab Size</b></td>
<td bgcolor="#d4edda">32K<br><small>(configurable)</small></td>
<td bgcolor="#d4edda">32K</td>
<td bgcolor="#d4edda">32K</td>
<td bgcolor="#fff3cd">50K</td>
<td bgcolor="#fff3cd">50K</td>
</tr>
<!-- Developer Experience -->
<tr>
<td colspan="6" style="background-color: #e3f2fd; padding: 10px;">
<b>👨💻 Developer Experience</b>
</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Error Messages</b></td>
<td bgcolor="#d4edda">✅ Helpful + solutions<br><small>Detailed debugging</small></td>
<td bgcolor="#fff3cd">⚠️ Standard PyTorch</td>
<td bgcolor="#fff3cd">⚠️ Standard PyTorch</td>
<td bgcolor="#f8d7da">❌ Basic errors</td>
<td bgcolor="#f8d7da">❌ Basic errors</td>
</tr>
<tr>
<td><b>Config Validation</b></td>
<td bgcolor="#d4edda">✅ Comprehensive<br><small>Auto-check conflicts</small></td>
<td bgcolor="#fff3cd">⚠️ Basic</td>
<td bgcolor="#fff3cd">⚠️ Basic</td>
<td bgcolor="#f8d7da">❌ Minimal</td>
<td bgcolor="#f8d7da">❌ Minimal</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Documentation</b></td>
<td bgcolor="#d4edda">✅ Extensive<br><small>ID + EN, with examples</small></td>
<td bgcolor="#d4edda">✅ Good<br><small>Official docs</small></td>
<td bgcolor="#fff3cd">⚠️ Medium<br><small>Community-driven</small></td>
<td bgcolor="#f8d7da">❌ Limited<br><small>Minimal docs</small></td>
<td bgcolor="#d4edda">✅ Extensive<br><small>OpenAI docs</small></td>
</tr>
<tr>
<td><b>Code Examples</b></td>
<td bgcolor="#d4edda">✅ 50+ examples<br><small>Training to deployment</small></td>
<td bgcolor="#d4edda">✅ Many examples</td>
<td bgcolor="#fff3cd">⚠️ Some examples</td>
<td bgcolor="#f8d7da">❌ Few examples</td>
<td bgcolor="#d4edda">✅ Many examples</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>HuggingFace Integration</b></td>
<td bgcolor="#d4edda">✅ Full native<br><small>Auto-registered</small></td>
<td bgcolor="#d4edda">✅ Official</td>
<td bgcolor="#d4edda">✅ Official</td>
<td bgcolor="#d4edda">✅ Available</td>
<td bgcolor="#d4edda">✅ Standard</td>
</tr>
<!-- Availability & License -->
<tr>
<td colspan="6" style="background-color: #e3f2fd; padding: 10px;">
<b>🌍 Ketersediaan & Lisensi</b>
</td>
</tr>
<tr>
<td><b>License</b></td>
<td bgcolor="#d4edda">✅ Apache 2.0<br><small>Fully permissive</small></td>
<td bgcolor="#fff3cd">⚠️ LLaMA 2 License<br><small>Commercial OK</small></td>
<td bgcolor="#d4edda">✅ Apache 2.0</td>
<td bgcolor="#d4edda">✅ MIT</td>
<td bgcolor="#d4edda">✅ MIT</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Commercial Use</b></td>
<td bgcolor="#d4edda">✅ Allowed<br><small>No restrictions</small></td>
<td bgcolor="#d4edda">✅ Allowed</td>
<td bgcolor="#d4edda">✅ Allowed</td>
<td bgcolor="#d4edda">✅ Allowed</td>
<td bgcolor="#d4edda">✅ Allowed</td>
</tr>
<tr>
<td><b>Weights Available</b></td>
<td bgcolor="#f8d7da">❌ Not trained<br><small>Architecture only</small></td>
<td bgcolor="#d4edda">✅ All sizes<br><small>7B/13B/70B</small></td>
<td bgcolor="#d4edda">✅ 7B</td>
<td bgcolor="#d4edda">✅ 117M</td>
<td bgcolor="#d4edda">✅ All sizes</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Self-Hosting</b></td>
<td bgcolor="#d4edda">✅ Designed for it<br><small>Full control</small></td>
<td bgcolor="#d4edda">✅ Yes</td>
<td bgcolor="#d4edda">✅ Yes</td>
<td bgcolor="#d4edda">✅ Yes</td>
<td bgcolor="#d4edda">✅ Yes</td>
</tr>
<tr>
<td><b>Training Required</b></td>
<td bgcolor="#f8d7da">❌ Yes<br><small>From scratch</small></td>
<td bgcolor="#d4edda">✅ No<br><small>Ready to use</small></td>
<td bgcolor="#d4edda">✅ No<br><small>Ready to use</small></td>
<td bgcolor="#d4edda">✅ No<br><small>Ready to use</small></td>
<td bgcolor="#d4edda">✅ No<br><small>Ready to use</small></td>
</tr>
<!-- Use Cases -->
<tr>
<td colspan="6" style="background-color: #e3f2fd; padding: 10px;">
<b>🎯 Use Cases</b>
</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Production Ready</b></td>
<td bgcolor="#f8d7da">❌ Not yet<br><small>After training</small></td>
<td bgcolor="#d4edda">✅ Yes</td>
<td bgcolor="#d4edda">✅ Yes</td>
<td bgcolor="#fff3cd">⚠️ Limited<br><small>Too small</small></td>
<td bgcolor="#fff3cd">⚠️ Limited<br><small>Outdated</small></td>
</tr>
<tr>
<td><b>Research</b></td>
<td bgcolor="#d4edda">✅ Excellent<br><small>Modular design</small></td>
<td bgcolor="#d4edda">✅ Good</td>
<td bgcolor="#d4edda">✅ Good</td>
<td bgcolor="#fff3cd">⚠️ Limited</td>
<td bgcolor="#d4edda">✅ Classic baseline</td>
</tr>
<tr bgcolor="#f8f9fa">
<td><b>Indonesian NLP</b></td>
<td bgcolor="#fff3cd">⚠️ After training<br><small>High potential</small></td>
<td bgcolor="#f8d7da">❌ Poor<br><small>Needs fine-tuning</small></td>
<td bgcolor="#f8d7da">❌ Poor<br><small>Needs fine-tuning</small></td>
<td bgcolor="#d4edda">✅ Native<br><small>But limited</small></td>
<td bgcolor="#f8d7da">❌ Poor</td>
</tr>
<tr>
<td><b>Education</b></td>
<td bgcolor="#d4edda">✅ Excellent<br><small>Learn modern LLMs</small></td>
<td bgcolor="#d4edda">✅ Good</td>
<td bgcolor="#fff3cd">⚠️ Medium</td>
<td bgcolor="#d4edda">✅ Good<br><small>Simple architecture</small></td>
<td bgcolor="#d4edda">✅ Classic<br><small>Well-documented</small></td>
</tr>
</tbody>
</table>
<div style="margin-top: 20px; padding: 15px; background-color: #fff3cd; border-left: 4px solid #ffc107;">
<p><b>📝 Catatan Penting:</b></p>
<ul style="margin: 10px 0;">
<li><b>Caca</b> adalah arsitektur modern yang <b>belum dilatih</b> - perlu training dari nol dengan dataset Indonesian</li>
<li><b>LLaMA 2 & Mistral</b> sangat bagus untuk English, tapi <b>poor untuk Indonesian</b> tanpa fine-tuning</li>
<li><b>IndoGPT</b> adalah satu-satunya dedicated Indonesian LLM, tapi <b>arsitektur sudah outdated</b> (GPT-2 era)</li>
<li><b>GPT-2</b> dimasukkan sebagai baseline klasik - arsitektur yang sudah proven tapi tidak modern</li>
</ul>
</div>
<div style="margin-top: 15px; padding: 15px; background-color: #d4edda; border-left: 4px solid #28a745;">
<p><b>✨ Keunggulan Unik Caca:</b></p>
<ul style="margin: 10px 0;">
<li>🎯 <b>Modular Design</b>: Toggle 50+ fitur tanpa rewrite code</li>
<li>🔧 <b>Developer-Friendly</b>: Error messages helpful + config validation</li>
<li>🚀 <b>Modern Architecture</b>: GQA + Flash Attention + SwiGLU + RMSNorm</li>
<li>🎨 <b>Multimodal Native</b>: Vision & Audio built-in (bukan add-on)</li>
<li>📚 <b>Extensive Docs</b>: Bahasa Indonesia + English dengan banyak contoh</li>
<li>⚡ <b>Optimization Focus</b>: 4 attention backends, auto-fallback, quantization ready</li>
<li>🔬 <b>Research-Oriented</b>: MoE, Mixture of Depths, Layer Scale, dll.</li>
</ul>
</div>
<div style="margin-top: 15px; padding: 15px; background-color: #f8d7da; border-left: 4px solid #dc3545;">
<p><b>⚠️ Keterbatasan Realistis:</b></p>
<ul style="margin: 10px 0;">
<li>❌ <b>Belum trained</b> - output akan random sampai di-training</li>
<li>❌ <b>Belum ada tokenizer</b> - perlu training tokenizer sendiri untuk Indonesian</li>
<li>❌ <b>Butuh resources besar</b> - training 7B model perlu GPU kelas A100</li>
<li>❌ <b>Belum teruji</b> - perlu extensive evaluation setelah training</li>
<li>❌ <b>Community masih kecil</b> - tidak sebesar LLaMA/Mistral ecosystem</li>
</ul>
</div>
<!-- Quotes -->
<div align="center">
<img src="https://quotes-caca.vercel.app/api/SsQuote" alt="Daily Quote" width="100%" />
</div>
<!-- Akhir Quotes -->
## 🔗 Links
- PyPI: https://pypi.org/project/caca-transformers/
- GitHub: https://github.com/Lyon-28/caca-transformers
- Email: cacatransformers@gmail.com |