AI Моделдеринин Архитектурасы
Заманбап жасалма интеллекттин негизин түзгөн моделдерди жана концепцияларды терең изилдеңиз. Бул жерде биздин интерактивдүү сабактардын жыйнагы.
1. Негизги Transformer туундулары
- BERT / RoBERTa / DeBERTa Context-aware language models
- ALBERT A Lite BERT with parameter-sharing
- ELECTRA Replaced Token Detection model
- DistilBERT / TinyBERT / MiniLM Distilled (compressed) BERT variants
- T5 / mT5 / Flan-T5 Universal "Text-to-Text" models
- GPT / GPT-NeoX / LLaMA Autoregressive text generation models
2. Сейрек жана эффективдүү эсептөөлөр
- Mixture of Experts (MoE) Models with specialized sub-networks per layer
- Switch Transformer / GLaM / Mixtral Expert routing and selection models
- Sparse Transformer / Reformer / Linformer Sparse Attention models for efficiency
- Longformer / BigBird / ETC Efficient architectures for long documents
- FlashAttention / xFormers Fast attention methods for GPU efficiency
3. Эс тутум жана узак контекст
- Transformer-XL / Compressive Transformer Recurrent transformers with extended memory
- RETRO Retrieval-enhanced transformer using external DB
- RMT (Recurrent Memory Transformer) Transformer with recurrent memory segments
- Hyena / Hyena Hierarchy Implicit attention models (convolution+attention)
- RWKV / Mamba Hybrid RNN and Transformer architectures
4. Тил жана структураны түшүнүү
- Syntax-aware Transformer Transformers that understand grammar
- Graph Neural Transformers Transformers for graph-structured data
- AdapterFusion / LoRA / Prefix-Tuning Modular and efficient fine-tuning methods
5. Көпмодалдуу моделдер
- CLIP / BLIP / ALIGN Text-image contrastive learning models
- Whisper / SpeechT5 / SeamlessM4T Speech-to-text and speech-to-speech models
- Flamingo / Kosmos / Gemini Multi-modal intelligence systems (text+image+video)
- Perceiver IO / Perceiver AR General-purpose architectures for all modalities
6. Логика, издөө жана ой жүгүртүү
7. Токенизация жана өкүлчүлүк
8. Жаңы багыттар (2024–2025)
- Mixture-of-Depths (Depth-MoE) Models with dynamic layer depth
- State Space Models (S4, S5, Mamba) Hybrids of RNN and mathematical state models
- Diffusion Transformer (DiT) Generators based on diffusion methods
- QLoRA / BitNet / 4-bit quantization Compressed and quantized models
- Lattice-Transformer / Dynamic Positional Encoding Models with novel positional encoding methods