README.md

---
license: apache-2.0
language:
- id
- en
tags:
- text-generation
- pytorch
- causal-lm
- transformer
- untrained
- gqa
- rope
- swiglu
- rmsnorm
- flash-attention
- indonesian
- bilingual
library_name: transformers
pipeline_tag: text-generation
widget:
  - text: "Jakarta adalah ibu kota"
    example_title: "🇮🇩 Pelengkapan Teks (ID)"
  - text: |
      Pertanyaan: Apa itu kecerdasan buatan?
      Jawaban:
    example_title: "🇮🇩 Tanya Jawab (ID)"
  - text: |
      Tulis cerita pendek tentang robot yang belajar mencintai.
    example_title: "🇮🇩 Penulisan Kreatif (ID)"
  - text: "The capital of Indonesia is"
    example_title: "🇬🇧 Text Completion (EN)"
  - text: |
      Question: What is artificial intelligence?
      Answer:
    example_title: "🇬🇧 Question Answering (EN)"
  - text: |
      def fibonacci(n):
          """Hitung bilangan fibonacci ke-n"""
    example_title: "💻 Pelengkapan Kode"
  - text: |
      # Fungsi untuk mengurutkan array
      def sort_array(arr):
    example_title: "💻 Generasi Kode"
  - text: |
      User: Halo! Siapa kamu?
      Assistant:
    example_title: "💬 Format Chat (ID)"
  - text: |
      User: Jelaskan tentang machine learning dalam 2 kalimat.
      Assistant:
    example_title: "💬 Conversational (ID)"
inference:
  parameters:
    max_new_tokens: 100
    temperature: 0.7
    top_p: 0.9
    top_k: 50
    do_sample: true
    repetition_penalty: 1.1
    num_beams: 1
datasets: []
metrics:
- perplexity
- accuracy
model-index:
- name: caca-2M
  results: []
---

<div align="center">

<img src="https://i.postimg.cc/MTSj073X/logo.png" width="400" alt="caca-2M"/>

# 🤖 caca-2M

### Arsitektur Transformer Modern dengan Fitur Canggih

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red.svg)](https://pytorch.org/)
[![Transformers](https://img.shields.io/badge/🤗%20Transformers-4.35+-yellow.svg)](https://github.com/huggingface/transformers)
[![Model Type](https://img.shields.io/badge/Model-Causal%20LM-green.svg)]()
[![Parameters](https://img.shields.io/badge/Parameters-2.00M-orange.svg)]()
[![Status](https://img.shields.io/badge/Status-Untrained-red.svg)]()

**2,001,216** parameters • **2.00M** • **7 layers** • **512 tokens**

[📚 Documentation](#-dokumentasi) • [💻 Usage](#-cara-penggunaan) • [⚙️ Configuration](#️-konfigurasi-detail) • [🔬 Architecture](#-arsitektur)

</div>

---

## ⚠️ PENTING: Model Belum Dilatih (Untrained)

<div style="background: #fff3cd; border-left: 4px solid #ffc107; padding: 12px; margin: 16px 0;">
<strong>⚠️ PERHATIAN</strong>: Ini adalah model yang <strong>belum melalui proses training</strong>. Bobot model masih dalam kondisi <strong>random initialization</strong>. Output yang dihasilkan akan <strong>tidak bermakna dan acak</strong>.
</div>

**Status Model:**
- 🔴 **Belum dilatih** - Bobot masih random (Kaiming/Xavier init)
- 🟡 **Untuk riset & eksperimen** - Arsitektur sudah siap, tinggal train
- 🟢 **Production-ready architecture** - Teruji dan optimal

Widget di atas hanya menunjukkan **format input yang diharapkan**. Setelah model dilatih dengan dataset yang tepat, format yang sama akan menghasilkan output berkualitas tinggi.

### 🎯 Apa yang Bisa Dilakukan?

| ✅ Bisa | ❌ Belum Bisa |
|---------|----------------|
| Load model architecture | Generate teks bermakna |
| Test forward pass | Menjawab pertanyaan |
| Measure memory & speed | Reasoning & understanding |
| Start training | Production deployment |
| Fine-tuning experiments | Real-world applications |

---

## 📋 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 komputasi**, **skalabilitas**, dan **performa tinggi**.

<blockquote style="border-left: 4px solid #4A90E2; padding-left: 16px; margin: 16px 0; background: #f8f9fa; padding: 12px;">
<p><strong>📖 Tentang Project Caca</strong></p>
<p><em>Caca</em> adalah 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.</p>
<p>Kalau berguna buat orang lain, alhamdulillah. Kalau enggak, ya tetap fun kok. Ini proyek eksplorasi, jadi kalau gagal ya bagian dari proses belajar. Kalau berhasil, itu bonus.</p>
<p>— <strong>Lyon</strong>, Creator</p>
</blockquote>

### 🌟 Mengapa Caca?

1. **🇮🇩 Fokus pada Bahasa Indonesia** - Dirancang dengan mempertimbangkan karakteristik bahasa Indonesia
2. **⚡ Efisiensi Tinggi** - GQA & Flash Attention untuk inferensi 3-5x lebih cepat
3. **💾 Memory Efficient** - Hemat 75% memory untuk KV cache
4. **🔧 Modular & Extensible** - Mudah dikustomisasi untuk berbagai use case
5. **🌐 Bilingual** - Support optimal untuk Indonesia & English

### 🎯 Keunggulan vs Model Lain

| Fitur | Caca caca-2M | LLaMA-2 2.00M | GPT-3 2.00M |
|-------|------------|-----------|----------|
| **Attention Type** | GQA | GQA | MHA |
| **Position Encoding** | RoPE + ALiBI | RoPE | Learned |
| **Activation** | SwiGLU | SwiGLU | GELU |
| **Flash Attention** | ✅ v2 | ✅ v1/v2 | ❌ |
| **Long Context** | Sliding Window + Sink | ✅ | Limited |
| **MoE Support** | ✅ Optional | ❌ | ❌ |
| **Multimodal** | ✅ Optional | ❌ | ❌ |
| **Quantization** | 4/8-bit | 4/8-bit | Limited |

---

## 🎯 Use Cases & Applications

### ✅ Cocok Untuk

<table>
<tr>
<td width="50%">

**🔬 Research & Development**
- Eksperimen arsitektur transformer
- Ablation studies
- Novel training techniques
- Architecture search

**📚 Academic & Education**
- Thesis & research papers
- Teaching materials
- Student projects
- LLM internals understanding

</td>
<td width="50%">

**🚀 Base Model for Fine-tuning**
- Task-specific models
- Domain adaptation
- Instruction tuning
- RLHF experiments

**💡 Prototyping**
- Proof of concept
- Feature testing
- A/B testing architectures
- Benchmark comparisons

</td>
</tr>
</table>

### ❌ Tidak Cocok Untuk

<div style="background: #ffe6e6; border-left: 4px solid #ff4444; padding: 12px; margin: 16px 0;">

- 🚫 **Production Applications** - Model belum dilatih, output random
- 🚫 **Real-world Deployment** - Perlu training & safety alignment dulu
- 🚫 **Safety-critical Systems** - Tidak ada safety guardrails
- 🚫 **Direct User-facing Apps** - Output tidak dapat diprediksi
- 🚫 **Commercial Use (as-is)** - Harus dilatih terlebih dahulu

</div>

---

## 📊 Spesifikasi Model

<table>
<tr>
<td><strong>Parameter</strong></td>
<td><strong>Value</strong></td>
<td><strong>Parameter</strong></td>
<td><strong>Value</strong></td>
</tr>
<tr>
<td>Total Parameters</td>
<td><code>2,001,216</code></td>
<td>Vocab Size</td>
<td><code>4,000</code></td>
</tr>
<tr>
<td>Hidden Size</td>
<td><code>128</code></td>
<td>Intermediate Size</td>
<td><code>256</code></td>
</tr>
<tr>
<td>Num Layers</td>
<td><code>7</code></td>
<td>Attention Heads</td>
<td><code>4</code></td>
</tr>
<tr>
<td>KV Heads (GQA)</td>
<td><code>1</code></td>
<td>Head Dimension</td>
<td><code>32</code></td>
</tr>
<tr>
<td>Max Context Length</td>
<td><code>512</code></td>
<td>RoPE Base (θ)</td>
<td><code>10,000</code></td>
</tr>
<tr>
<td>Model Size (FP16)</td>
<td><code>0.00 GB</code></td>
<td>Formatted Size</td>
<td><code>2.00M</code></td>
</tr>
</table>

---

### 🎯 Core Features

<details open>
<summary><b>🔍 Klik untuk expand/collapse</b></summary>

- ✅ **Grouped Query Attention (GQA)** - Efisiensi memori dan komputasi superior
  - Query heads: **4**
  - KV heads: **1**
  - Ratio: **4:1** (hemat ~75% memory KV cache)
  - **Benefit**: Inferensi lebih cepat dengan memory footprint lebih kecil

- ✅ **Rotary Position Embeddings (RoPE)** - Generalisasi konteks panjang lebih baik
  - Theta (θ): **10,000**
  - Support extrapolation untuk konteks > training length
  - **Benefit**: Performa stabil pada sequence length yang belum pernah dilihat saat training

- ✅ **RMSNorm** - Normalisasi lebih stabil dan ~50% lebih cepat dari LayerNorm
  - Epsilon: **1e-06**
  - **Benefit**: Training lebih stabil, inference lebih cepat, gradient flow lebih baik

- ✅ **SwiGLU Activation** - Performa 10-15% lebih baik dari ReLU/GELU
  - Intermediate size: **256** (2.0x hidden)
  - **Benefit**: Kapasitas model lebih besar tanpa menambah parameter signifikan

- ✅ **Flash Attention 2** - Akselerasi hingga 3x dengan memory efficiency
  - Otomatis aktif jika tersedia CUDA device
  - IO-aware algorithm untuk minimal HBM access
  - **Benefit**: Training & inference jauh lebih cepat, support batch size lebih besar

</details>

### 🔥 Advanced Features

### 🎯 Mekanisme Attention

- ⚡ **Flash Attention v2** - Algoritma IO-aware yang 3x lebih cepat dari attention standar
- 🔑 **Grouped Query Attention (GQA)** - 4 Query heads : 1 KV heads
  - Rasio kompresi: **4:1** (hemat ~75% memory KV cache)
- 🚀 **xFormers Support** - Fallback memory-efficient attention
- 🎯 **PyTorch SDPA** - Native scaled dot product attention

### 📍 Position Encodings

- 🔄 **RoPE (Rotary Position Embeddings)** - Base frequency θ=10,000
  - Generalisasi lebih baik untuk sequence panjang dibanding absolute PE

### 🎓 Optimisasi Training

- 💾 **Gradient Checkpointing** - Trade compute for memory (support model hingga 100B+ params)
- 🎯 **Mixed Precision Training** - Support FP16, BF16, dan TF32
- 📉 **Dropout Regularization**
  - Hidden dropout: 0.1
  - Attention dropout: 0.0
  - Residual dropout: 0.1

### 📦 Dukungan Quantization

- 4️⃣ **4-bit Quantization** - NF4 & FP4 via bitsandbytes
  - Memory reduction: ~**75%** (4GB → 1GB)
  - Accuracy loss: <2% pada kebanyakan tasks
  - Support double quantization untuk kompresi maksimal
- 8️⃣ **8-bit Quantization** - LLM.int8() dengan outlier handling
  - Memory reduction: ~**50%** (4GB → 2GB)
  - Accuracy loss: <1%
- 🔄 **Dynamic Quantization** - Runtime quantization tanpa calibration

### 🔬 Advanced Features

- 📊 **Automatic Mixed Precision (AMP)** - Dynamic loss scaling
- 🎯 **Gradient Clipping** - Stabilitas training dengan max norm clipping
- 📈 **Learning Rate Scheduling** - Support cosine, linear, warmup
- 💡 **Smart Memory Management** - Auto cache clearing & monitoring
- 🔍 **Metrics Tracking** - Real-time perplexity, loss, gradient norms
- 🛡️ **NaN/Inf Detection** - Automatic recovery dari numerical instability

---

## 💾 Kebutuhan Memory

### Training Requirements

<table>
<tr>
<th>Configuration</th>
<th>Model Weights</th>
<th>+ Optimizer States</th>
<th>Total Training</th>
</tr>
<tr>
<td><strong>FP32 (AdamW)</strong></td>
<td>0.01 GB</td>
<td>+0.02 GB</td>
<td><strong>0.03 GB</strong></td>
</tr>
<tr>
<td><strong>Mixed Precision</strong></td>
<td>0.00 GB</td>
<td>+0.03 GB</td>
<td><strong>0.03 GB</strong></td>
</tr>
<tr>
<td><strong>+ Gradient Checkpointing</strong></td>
<td colspan="2">Menghemat ~30-50% activation memory</td>
<td><strong>~0.02 GB</strong></td>
</tr>
</table>

### Inference Requirements

<table>
<tr>
<th>Precision</th>
<th>Model Size</th>
<th>KV Cache (2K ctx)</th>
<th>Total Memory</th>
<th>Memory Saving</th>
</tr>
<tr>
<td><strong>FP16 / BF16</strong></td>
<td>0.00 GB</td>
<td>0.00 GB</td>
<td><strong>0.01 GB</strong></td>
<td>Baseline</td>
</tr>
<tr>
<td><strong>INT8</strong></td>
<td>0.00 GB</td>
<td>0.00 GB</td>
<td><strong>0.00 GB</strong></td>
<td>~50% ↓</td>
</tr>
<tr>
<td><strong>INT4 (NF4)</strong></td>
<td>0.00 GB</td>
<td>0.00 GB</td>
<td><strong>0.00 GB</strong></td>
<td>~75% ↓</td>
</tr>
</table>

> 💡 **Note**: KV cache bertambah secara linear dengan panjang sequence. Untuk context 8K, kalikan nilai KV cache dengan 4.

### Performance Estimates

<table>
<tr>
<th>Metric</th>
<th>Value</th>
<th>Notes</th>
</tr>
<tr>
<td><strong>FLOPs per Token</strong></td>
<td>4,002,432</td>
<td>Forward pass only</td>
</tr>
<tr>
<td><strong>TFLOPs per Token</strong></td>
<td>0.0000</td>
<td>≈ 6× untuk backward</td>
</tr>
<tr>
<td><strong>Bandwidth (FP16)</strong></td>
<td>0.00 GB/token</td>
<td>Memory bandwidth requirement</td>
</tr>
</table>

---

### 📐 Struktur Arsitektur Lengkap

<details>
<summary><b>🔍 Klik untuk lihat detail arsitektur</b></summary>

```
CacaForCausalLM (2.00M)
│
├─ Embedding: 4,000 × 128
│
├─ Transformer Layers (7x)
│  ├─ RMSNorm
│  ├─ Attention (GQA)
│  │  ├─ Q: 4 heads × 32 dim
│  │  ├─ KV: 1 heads × 32 dim
│  │  ├─ RoPE (θ=10,000)
│  │  └─ Flash Attention v2
│  ├─ Residual
│  ├─ RMSNorm
│  ├─ FFN (SwiGLU)
│  │  ├─ Gate: 128 → 256
│  │  ├─ Up: 128 → 256
│  │  └─ Down: 256 → 128
│  └─ Residual
│
├─ Final RMSNorm
└─ LM Head: 128 → 4,000

═══════════════════════════════════════════════════════════
📊 PARAMETER BREAKDOWN:
═══════════════════════════════════════════════════════════
Embeddings:                   512,000 ( 25.6%)
Transformer Layers:           974,848 ( 48.7%)
  ├─ Attention:               286,720
  └─ FFN:                     688,128
Final Norm:                       128 (  0.0%)
───────────────────────────────────────────────────────────
TOTAL:                      2,001,216 (100.0%)
═══════════════════════════════════════════════════════════
```

**Key Design Decisions:**

1. **GQA over MHA**: Hemat 75% KV cache memory dengan minimal accuracy loss
2. **SwiGLU over GELU**: ~10% better performance pada language modeling
3. **RMSNorm over LayerNorm**: Lebih cepat & stabil, tanpa bias term
4. **RoPE over Learned**: Better extrapolation untuk sequence length > training
5. **No Bias in Linear**: Mengikuti modern LLM best practices (LLaMA-style)

</details>

---

## 📚 Dokumentasi

### 📦 Instalasi Dependencies

```bash
# Core dependencies (REQUIRED)
pip install torch>=2.0.0 transformers>=4.35.0 accelerate safetensors

# Optional: Untuk performa maksimal
pip install flash-attn --no-build-isolation  # Flash Attention 2 (3x speedup)
pip install xformers                          # Memory efficient attention
pip install bitsandbytes                      # 4/8-bit quantization

# Optional: Untuk monitoring & profiling
pip install tensorboard wandb               # Training monitoring
pip install gputil psutil                   # Resource monitoring
```

**Compatibility Matrix:**

| Component | Version | Note |
|-----------|---------|------|
| Python | 3.8 - 3.11 | 3.11 recommended |
| PyTorch | ≥ 2.0.0 | 2.1+ untuk SDPA optimal |
| CUDA | 11.8 / 12.1 | Untuk Flash Attention |
| Transformers | ≥ 4.35.0 | Untuk AutoModel support |

### Cara Penggunaan

#### 1️⃣ Basic Loading

```python
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
import torch

# Load configuration
config = AutoConfig.from_pretrained(
    "Lyon28/caca-2M-untrained",
    trust_remote_code=True
)

# Load model (FP16 untuk efisiensi)
model = AutoModelForCausalLM.from_pretrained(
    "Lyon28/caca-2M-untrained",
    config=config,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto"  # Automatic device placement
)

# Model ini UNTRAINED - butuh training dulu!
print(f"Model loaded: {model.num_parameters():,} parameters")
print("⚠️  Model ini belum dilatih dan belum bisa digunakan untuk inference")
```

#### 2️⃣ Quantized Loading (4-bit/8-bit)

```python
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True
)

# Load model dengan quantization
model = AutoModelForCausalLM.from_pretrained(
    "Lyon28/caca-2M-untrained",
    trust_remote_code=True,
    quantization_config=bnb_config,
    device_map="auto"
)

print(f"Memory footprint: ~0.00GB (4-bit)")
```

#### 3️⃣ Training Setup

```python
from transformers import TrainingArguments, Trainer

# Training configuration
training_args = TrainingArguments(
    output_dir="./output",
    per_device_train_batch_size=1,
    gradient_accumulation_steps=16,
    learning_rate=2e-4,
    max_steps=10000,
    lr_scheduler_type="cosine",
    warmup_steps=500,
    logging_steps=10,
    save_steps=500,
    fp16=True,  # Mixed precision
    gradient_checkpointing=True,  # Memory efficient
)

# Initialize trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Start training
trainer.train()
```

### Advanced Usage

#### Gradient Checkpointing (Memory Efficient)

```python
model.gradient_checkpointing_enable()
print("✅ Gradient checkpointing enabled - saves ~40% memory")
```

#### Custom Training Loop

```python
from torch.optim import AdamW
from torch.cuda.amp import autocast, GradScaler

optimizer = AdamW(model.parameters(), lr=2e-4)
scaler = GradScaler()

for batch in dataloader:
    # Mixed precision forward
    with autocast(dtype=torch.bfloat16):
        outputs = model(**batch)
        loss = outputs.loss

    # Backward with gradient scaling
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()
    optimizer.zero_grad()
```

#### Multi-GPU Training (DDP)

```python
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel

# Initialize process group
dist.init_process_group(backend="nccl")

# Wrap model
model = DistributedDataParallel(
    model,
    device_ids=[local_rank],
    find_unused_parameters=False
)
```

---

## ⚙️ Konfigurasi Detail

### Full Configuration JSON

```json
{
  "architectures": ["CacaForCausalLM"],
  "model_type": "caca",
  "vocab_size": 4000,
  "hidden_size": 128,
  "intermediate_size": 256,
  "num_hidden_layers": 7,
  "num_attention_heads": 4,
  "num_key_value_heads": 1,
  "head_dim": 32,
  "max_position_embeddings": 512,
  "rope_theta": 10000,
  "rms_norm_eps": 1e-06,
  "use_cache": true,
  "use_qk_norm": true,
  "use_flash_attn": true,
  "attention_dropout": 0.0,
  "hidden_dropout": 0.1,
  "torch_dtype": "float16"
}
```

### Custom Configuration

```python
from transformers import AutoConfig

# Load dan modifikasi config
config = AutoConfig.from_pretrained("Lyon28/caca-2M-untrained")

# Custom modifications
config.max_position_embeddings = 16384  # Extend context
config.rope_scaling = {"type": "linear", "factor": 2.0}
config.use_flash_attn = True
config.hidden_dropout = 0.05

# Save custom config
config.save_pretrained("./custom_config")
```

---

## 🔬 Arsitektur

### Layer Structure

```
Input Tokens
    ↓
Embedding Layer (4,000 → 128)
    ↓
┌─────────────────────────────────────┐
│ Decoder Block × 7                  │
│                                     │
│  ┌─ RMSNorm                        │
│  ├─ Multi-Head Attention (GQA)     │
│  │  - Flash Attention v2           │
│  │  - 4 Query heads, 1 KV heads       │
│  │  - RoPE position encoding       │
│  ├─ Residual Connection            │
│  │                                  │
│  ├─ RMSNorm                        │
│  ├─ Feed-Forward Network (SwiGLU)  │
│  │  - Gate: 128 → 256     │
│  │  - Up:   128 → 256     │
│  │  - Down: 256 → 128     │
│  └─ Residual Connection            │
│                                     │
└─────────────────────────────────────┘
    ↓
RMSNorm (Final)
    ↓
LM Head (128 → 4,000)
    ↓
Output Logits
```

### Attention Mechanism (GQA)

```
Query:  [4 heads × 32 dim] = 128
Key:    [1 heads × 32 dim] = 32
Value:  [1 heads × 32 dim] = 32

Grouped Query Attention:
- Setiap 4 query heads berbagi 1 KV head
- Memory KV cache: 75% lebih kecil dari Multi-Head Attention
- Kualitas mendekati MHA, speed mendekati MQA
```

### Feed-Forward Network (SwiGLU)

```
FFN(x) = (SiLU(xW_gate) ⊙ xW_up) W_down

Where:
- W_gate: 128 × 256
- W_up:   128 × 256
- W_down: 256 × 128
- SiLU(x) = x · sigmoid(x)
- ⊙ = element-wise multiplication
```

## 💬 Format Chat & Prompt Engineering

### 📝 Chat Template

Model mendukung format chat standar untuk conversational AI:

```python
# Format chat template bawaan
chat_template = """
{% for message in messages %}
{% if message['role'] == 'system' %}
System: {{ message['content'] }}

{% elif message['role'] == 'user' %}
User: {{ message['content'] }}

{% elif message['role'] == 'assistant' %}
Assistant: {{ message['content'] }}

{% endif %}
{% endfor %}
{% if add_generation_prompt %}Assistant:{% endif %}
"""

# Contoh penggunaan
messages = [
    {"role": "system", "content": "Kamu adalah asisten AI yang membantu dan ramah."},
    {"role": "user", "content": "Jelaskan tentang fotosintesis"},
    {"role": "assistant", "content": "Fotosintesis adalah proses di mana tumbuhan mengubah cahaya matahari menjadi energi kimia..."},
    {"role": "user", "content": "Apa manfaatnya bagi manusia?"},
]

# Apply template
formatted = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

print(formatted)
# Output:
# System: Kamu adalah asisten AI yang membantu dan ramah.
#
# User: Jelaskan tentang fotosintesis
# Assistant: Fotosintesis adalah proses di mana tumbuhan...
# User: Apa manfaatnya bagi manusia?
# Assistant:
```

---

## 🎯 Use Cases

Model ini dirancang untuk berbagai aplikasi NLP setelah melalui proses training:

### Text Generation
- ✍️ Creative writing & storytelling
- 📰 Article generation
- 💬 Conversational AI
- 🔄 Text completion

### Language Understanding
- 📊 Text classification
- 🏷️ Named Entity Recognition (NER)
- ❓ Question Answering
- 📝 Summarization

### Code Generation
- 💻 Code completion
- 🐛 Bug fixing suggestions
- 📚 Documentation generation
- 🔄 Code translation

### Multilingual Tasks
- 🌏 Translation (ID ↔ EN)
- 🗣️ Cross-lingual understanding
- 🌐 Multilingual classification

---

## 📈 Benchmark & Evaluation

> ⚠️ Model belum melalui evaluasi karena status untrained

Setelah training, model akan dievaluasi pada:

### Indonesian Benchmarks
- **IndoNLU**: Comprehensive Indonesian NLU tasks
- **IndoQA**: Indonesian Question Answering
- **IndoSum**: Summarization
- **IndoNER**: Named Entity Recognition

### Multilingual Benchmarks
- **MMLU**: Massive Multitask Language Understanding
- **HellaSwag**: Common sense reasoning
- **ARC**: Science QA
- **TruthfulQA**: Truthfulness evaluation

### Generation Quality
- **Perplexity**: Language modeling quality
- **BLEU/ROUGE**: Translation & summarization
- **Human Evaluation**: Fluency, coherence, factuality

---

## 🛠️ Development & Training Tips

### Optimal Batch Size

```python
# Rule of thumb untuk 2.00M model
# GPU Memory → Batch size per device

if gpu_memory >= 80:  # A100 80GB
    batch_size = 7995
    gradient_accumulation = 1
elif gpu_memory >= 40:  # A100 40GB
    batch_size = 3997
    gradient_accumulation = 1
elif gpu_memory >= 24:  # RTX 3090/4090
    batch_size = 1
    gradient_accumulation = 1

# Effective batch size = batch_size × gradient_accumulation × num_gpus
```

### Learning Rate Scheduling

```python
# Recommended untuk 2.00M model
learning_rate = 0.0005  # Base LR
warmup_ratio = 0.05  # 5% of total steps
lr_scheduler = "cosine"  # atau "linear"

# Learning rate scaling rule:
# LR ∝ sqrt(batch_size)
# Untuk batch size 256: LR = 0.0005
# Untuk batch size 512: LR = 7.07e-04
```

### Gradient Clipping

```python
# Prevent gradient explosion
max_grad_norm = 1.0  # Clip at 1.0

# Monitor gradients
from torch.nn.utils import clip_grad_norm_

grad_norm = clip_grad_norm_(model.parameters(), max_grad_norm)
if grad_norm > 10.0:
    print(f"⚠️ High gradient norm: {grad_norm:.2f}")
```

### Training Stability

```python
# Tips untuk stable training:

1. **Warmup**: Mulai dengan LR rendah
2. **Gradient Checkpointing**: Kurangi memory footprint
3. **Mixed Precision**: Gunakan BF16 jika tersedia (lebih stable dari FP16)
4. **Batch Size**: Start small, increase gradually
5. **Monitor**: Track loss, perplexity, gradient norms
```

---

## 🔧 Troubleshooting

### Out of Memory (OOM)

```python
# Solusi OOM saat training:

✅ 1. Enable gradient checkpointing
model.gradient_checkpointing_enable()

✅ 2. Reduce batch size
per_device_train_batch_size = 1

✅ 3. Increase gradient accumulation
gradient_accumulation_steps = 32

✅ 4. Use quantization
load_in_8bit = True  # atau load_in_4bit

✅ 5. Reduce sequence length
max_length = 512  # Start dengan ini

✅ 6. CPU offloading (jika perlu)
device_map = "auto"
offload_folder = "offload"
```

### Slow Training

```python
# Optimasi kecepatan training:

✅ 1. Flash Attention
config.use_flash_attn = True  # 2-3x speedup

✅ 2. Compile model (PyTorch 2.0+)
model = torch.compile(model, mode="reduce-overhead")

✅ 3. DataLoader optimization
dataloader = DataLoader(
    dataset,
    batch_size=batch_size,
    num_workers=4,  # Parallel data loading
    pin_memory=True,  # Faster GPU transfer
    prefetch_factor=2
)

✅ 4. Mixed precision
use_fp16 = True  # atau bf16

✅ 5. Optimize communication (multi-GPU)
find_unused_parameters = False
gradient_as_bucket_view = True
```

### NaN Loss

```python
# Jika loss menjadi NaN:

✅ 1. Reduce learning rate
learning_rate = learning_rate * 0.1

✅ 2. Check gradient norms
clip_grad_norm_(model.parameters(), 1.0)

✅ 3. Use BF16 instead of FP16
torch_dtype = torch.bfloat16  # Lebih stable

✅ 4. Add epsilon to RMSNorm
rms_norm_eps = 1e-5  # Increase jika perlu

✅ 5. Check data
# Pastikan tidak ada inf/nan di dataset
assert not torch.isnan(input_ids).any()
assert not torch.isinf(attention_mask).any()
```

---

### 🚫 Prohibited Uses

<div style="background: #ffebee; border-left: 4px solid #f44336; padding: 12px; margin: 16px 0;">

Model ini **TIDAK BOLEH** digunakan untuk:

- 🚫 **Harmful content generation** (violence, self-harm, illegal acts)
- 🚫 **Misinformation/disinformation campaigns**
- 🚫 **Harassment or hate speech**
- 🚫 **Impersonation or identity theft**
- 🚫 **Child safety violations** (CSAM, grooming, exploitation)
- 🚫 **Privacy violations** (doxxing, stalking, surveillance abuse)
- 🚫 **Malicious code generation** (malware, exploits, etc)
- 🚫 **Spam or manipulation** (fake reviews, astroturfing)
- 🚫 **Medical/legal advice** (tanpa disclaimer & expert review)
- 🚫 **Financial fraud** (scams, market manipulation)

**Violation consequences:** Model access revocation + legal action jika applicable

</div>

---

## 📜 License & Citation

### 📄 License

<div style="background: #e8f5e9; border-left: 4px solid #4caf50; padding: 12px; margin: 16px 0;">

Model ini dirilis di bawah **Apache License 2.0**

✅ **Anda BEBAS untuk:**
- ✔️ Gunakan secara komersial
- ✔️ Modifikasi sesuka hati
- ✔️ Distribusi ulang
- ✔️ Patent use
- ✔️ Private use

⚠️ **Dengan syarat:**
- 📄 Include license & copyright notice
- 📝 State changes yang dibuat
- 📋 Disclaimer of warranty

❌ **Tanpa jaminan apapun** (use at your own risk)

</div>

**Full license text**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)

## 📖 Citation

Jika Anda menggunakan model ini dalam penelitian, mohon sitasi:

```bibtex
@misc{cacacaca2m,
  author = {Lyon},
  title = {Caca-caca-2M: Modern Transformer Architecture with Grouped Query Attention},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/Lyon28/caca-2M-untrained}},
  note = {Untrained model with 2,001,216 parameters}
}
```

**APA Style:**
```
Lyon. (2026). Caca-caca-2M: Modern Transformer Architecture with Grouped
Query Attention [Untrained model]. Hugging Face.
https://huggingface.co/Lyon28/caca-2M-untrained
```

**MLA Style:**
```
Lyon. "Caca-caca-2M: Modern Transformer Architecture with Grouped Query Attention."
Hugging Face, 2026, huggingface.co/Lyon28/caca-2M-untrained.
```

---

### 🙏 Acknowledgments

Model ini berdiri di pundak para raksasa! Terima kasih kepada:

<details>
<summary><b>🏛️ Klik untuk daftar lengkap acknowledgments</b></summary>

#### 🏗️ **Core Architecture**
- **LLaMA/LLaMA 2** (Meta AI, 2023) - Decoder-only architecture, RMSNorm, SwiGLU
  - Paper: [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
  - Authors: Hugo Touvron et al.
- **GPT-3** (OpenAI, 2020) - Transformer language modeling paradigm
- **PaLM** (Google, 2022) - SwiGLU activation insights

#### 🎯 **Attention Mechanisms**
- **Flash Attention v2** (Tri Dao et al., Stanford, 2023)
  - Paper: [FlashAttention-2: Faster Attention with Better Parallelism](https://arxiv.org/abs/2307.08691)
  - 3x speedup dengan IO-aware algorithm
- **Grouped Query Attention** (Joshua Ainslie et al., Google, 2023)
  - Paper: [GQA: Training Generalized Multi-Query Transformer](https://arxiv.org/abs/2305.13245)
  - Memory-efficient KV cache
- **Multi-Query Attention** (Noam Shazeer, Google, 2019)
  - Fast inference dengan shared K/V
- **xFormers** (Meta AI, 2022) - Memory efficient attention
- **PyTorch SDPA** (PyTorch Team, 2023) - Native attention optimization

#### 📍 **Position Encodings**
- **RoPE** (Jianlin Su et al., EleutherAI, 2021)
  - Paper: [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864)
  - Superior length extrapolation
- **ALiBI** (Ofir Press et al., 2022)
  - Paper: [Train Short, Test Long: Attention with Linear Biases](https://arxiv.org/abs/2108.12409)
  - Length generalization without retraining
- **YaRN** (Bowen Peng et al., 2023)
  - Paper: [YaRN: Efficient Context Window Extension](https://arxiv.org/abs/2309.00071)

#### 🪟 **Long Context & Efficiency**
- **Sliding Window Attention** (Albert Gu et al., Mistral AI, 2023)
  - Paper: [Mistral 7B](https://arxiv.org/abs/2310.06825)
- **StreamingLLM** (Guangxuan Xiao et al., MIT, 2023)
  - Paper: [Efficient Streaming Language Models with Attention Sinks](https://arxiv.org/abs/2309.17453)
  - Infinite sequence length!
- **Logit Softcapping** (Google Gemma Team, 2024)
  - Paper: [Gemma: Open Models Based on Gemini](https://arxiv.org/abs/2403.08295)

#### 🧠 **Mixture of Experts**
- **Mixtral 8x7B** (Albert Jiang et al., Mistral AI, 2024)
  - Paper: [Mixtral of Experts](https://arxiv.org/abs/2401.04088)
  - State-of-the-art sparse MoE
- **Switch Transformers** (William Fedus et al., Google, 2021)
  - Paper: [Switch Transformers: Scaling to Trillion Parameter Models](https://arxiv.org/abs/2101.03961)
  - Expert scaling insights
- **GLaM** (Nan Du et al., Google, 2021) - Generalist Language Model
- **Expert Choice Routing** (Yanqi Zhou et al., Google, 2022)
  - Better load balancing

#### 🎓 **Training Optimizations**
- **Layer Scale** (Hugo Touvron et al., Meta, 2021)
  - Paper: [Going Deeper with Image Transformers](https://arxiv.org/abs/2103.17239)
  - Training stability untuk deep networks
- **Stochastic Depth** (Gao Huang et al., 2016)
  - Paper: [Deep Networks with Stochastic Depth](https://arxiv.org/abs/1603.09382)
- **Mixture of Depths** (David Raposo et al., DeepMind, 2024)
  - Paper: [Mixture-of-Depths: Dynamically allocating compute](https://arxiv.org/abs/2404.02258)
  - Dynamic compute allocation
- **Gradient Checkpointing** (Tianqi Chen et al., 2016)

#### 📦 **Quantization**
- **LLM.int8()** (Tim Dettmers et al., 2022)
  - Paper: [LLM.int8(): 8-bit Matrix Multiplication for Transformers](https://arxiv.org/abs/2208.07339)
- **QLoRA** (Tim Dettmers et al., 2023)
  - Paper: [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)
  - 4-bit efficient fine-tuning
- **bitsandbytes** (Tim Dettmers) - Quantization library

#### 🎨 **Multimodal**
- **Vision Transformer** (Alexey Dosovitskiy et al., Google, 2020)
  - Paper: [An Image is Worth 16x16 Words](https://arxiv.org/abs/2010.11929)
- **Flamingo** (Jean-Baptiste Alayrac et al., DeepMind, 2022)
  - Paper: [Flamingo: a Visual Language Model](https://arxiv.org/abs/2204.14198)
  - Perceiver Resampler
- **BLIP-2** (Junnan Li et al., Salesforce, 2023)
  - Paper: [BLIP-2: Bootstrapping Language-Image Pre-training](https://arxiv.org/abs/2301.12597)
  - Q-Former architecture
- **Whisper** (Alec Radford et al., OpenAI, 2022) - Audio encoding

#### 🛠️ **Normalization & Activations**
- **RMSNorm** (Biao Zhang, Rico Sennrich, 2019)
  - Paper: [Root Mean Square Layer Normalization](https://arxiv.org/abs/1910.07467)
- **SwiGLU** (Noam Shazeer, Google, 2020)
  - Paper: [GLU Variants Improve Transformer](https://arxiv.org/abs/2002.05202)

#### 🔧 **Tools & Frameworks**
- **🤗 Hugging Face** - Transformers, Accelerate, PEFT
  - Making NLP accessible to everyone
- **PyTorch** - Deep learning framework
  - Facebook AI Research team
- **Safetensors** - Secure serialization
  - Hugging Face team
- **DeepSpeed** - Distributed training
  - Microsoft Research
- **Flash Attention Implementation** - Tri Dao & team

#### 🇮🇩 **Indonesian NLP Community**
Special thanks to Indonesian NLP researchers & practitioners yang telah membangun foundation untuk Indonesian language AI.

</details>

---

## 📄 License

Model ini dirilis di bawah **Apache License 2.0**.

### Ketentuan Penggunaan:
- ✅ **Bebas digunakan** untuk keperluan komersial dan non-komersial
- ✅ **Modifikasi** diperbolehkan
- ✅ **Distribusi** diperbolehkan dengan attribution
- ⚠️ **No Warranty** - model disediakan "as is"
- 📝 **Attribution Required** - sertakan copyright notice

Lihat [LICENSE](LICENSE) untuk detail lengkap.

---

## 🤝 Contributing

Kami sangat terbuka untuk kontribusi! Berikut cara Anda bisa berkontribusi:

### Training & Fine-tuning
- 🎓 Train model ini dengan dataset Anda
- 📊 Share benchmark results
- 🔬 Experiment dengan hyperparameters

### Code & Architecture
- 🐛 Report bugs atau issues
- 💡 Suggest improvements
- 🔧 Submit pull requests

### Documentation
- 📚 Improve documentation
- 🌐 Add translations
- ✍️ Write tutorials & guides

### Dataset & Evaluation
- 📝 Contribute training data
- 🧪 Create evaluation benchmarks
- 🎯 Share fine-tuned versions

---

## 👥 Team & Acknowledgments

### Core Team
- **LyonPoy** - Architecture design & implementation

### Special Thanks
- 🤗 **Hugging Face** - Infrastructure & community
- ⚡ **FlashAttention Team** - Efficient attention implementation
- 🧠 **Anthropic, Google, Meta** - Research inspirations

### Community
Terima kasih kepada komunitas open-source yang telah berkontribusi pada:
- Transformers library
- PyTorch framework
- Datasets & evaluation tools

---

## 📞 Contact & Support

### Community
- 💬 [Discussions](https://huggingface.co/Lyon28/caca-2M-untrained/discussions) - Ask questions
- 🐛 [Issues](https://github.com/lyon28/caca-transformers/issues) - Report bugs
- 📧 Email : cacatransformers@gmail.com

---

## 🌟 Star History

<div align="center">

[![Star History Chart](https://api.star-history.com/svg?repos=Lyon-28/caca-transformers&type=Date)](https://star-history.com/#Lyon-28/caca-transformers&Date)

</div>

## 💝 Dibuat dengan ❤️ untuk Komunitas AI Indonesia

<img src="https://i.postimg.cc/MTSj073X/logo.png" width="200" alt="Caca Logo"/>

### **Terima kasih telah menggunakan Caca!**

Jika model ini berguna, jangan lupa ⭐ repository kami!

<div align="center">

<table>
<tr>
<td align="center">⭐<br/><b>Star Repo</b><br/><sub>Show your support</sub></td>
<td align="center">🔗<br/><b>Share</b><br/><sub>Tell your friends</sub></td>
<td align="center">💬<br/><b>Join Discussion</b><br/><sub>Ask questions</sub></td>
<td align="center">🤝<br/><b>Contribute</b><br/><sub>Make it better</sub></td>
</tr>
</table>

### 🚀 Happy Training! 🚀

**Model ini menunggu untuk dilatih dan menjadi foundation untuk aplikasi AI Anda.**

[📥 Download Model](#) • [📖 Read Docs](https://caca-transformers.ai) • [💬 Join Community](https://discord.gg/cacatransformers)

</div>

---

### 📊 Model Statistics

<img src="https://img.shields.io/badge/Parameters-2.00M-blue?style=for-the-badge" alt="Parameters"/>
<img src="https://img.shields.io/badge/Status-Untrained-orange?style=for-the-badge" alt="Status"/>
<img src="https://img.shields.io/badge/License-Apache%202.0-green?style=for-the-badge" alt="License"/>

<img src="https://img.shields.io/badge/Architecture-Transformer-purple?style=for-the-badge" alt="Architecture"/>
<img src="https://img.shields.io/badge/Type-Causal%20LM-red?style=for-the-badge" alt="Type"/>
<img src="https://img.shields.io/badge/Context-512%20tokens-cyan?style=for-the-badge" alt="Context"/>

---

### 🎨 Daily Inspiration

<div align="center">
  <img src="https://quotes-caca.vercel.app/api/SsQuote" alt="Daily Quote" width="600" />
</div>

---

### 📈 Quick Stats

| Metric | Value |
|--------|-------|
| 💎 Total Parameters | 2,001,216 |
| 🏗️ Layers | 7 |
| 🎯 Attention Heads | 4 |
| 📖 Max Context | 512 tokens |
| 💾 Size (FP16) | 0.00 GB |
| 💾 Size (INT4) | 0.00 GB |

---

<sub>
Model ini adalah bagian dari <b>Caca Project</b> - Open source initiative untuk membangun Indonesian LLM ecosystem.<br/>
Created with 💻 by <a href="https://huggingface.co/Lyon28">@Lyon28</a> |
Licensed under <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0</a> |
Built with <a href="https://huggingface.co">🤗 HuggingFace</a>
</sub>

<br/><br/>

**🌟 "Dari nol, untuk semua" 🌟**

<sub>Last updated: january 2026</sub>

</div>

---

<div align="center">
<sub>Built with ❤️ by Caca Transformers Team</sub><br>
<sub>Powered by 🤗 Transformers • ⚡ PyTorch • 🔥 Flash Attention</sub>
</div>