TINY TRANSFORMERS MASTERING ON- DEVICE LANGUAGE MODELS: Optimization, Quantization, and Deployment Strategies for Edge Computing

Disponibilidad:
En stock

Peso con empaque:
0.72 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Sobre este producto

The SLM Revolution: Understand why the industry is pivoting from trillion-parameter giants to efficient 3B-7B parameter models like Phi-3, Gemma, Llama 3, and Mistral.
Architectural Efficiency: Master modern techniques like Grouped-Query Attention (GQA), Sliding Window Attention, and Mixture of Experts (MoE) to fit long contexts into limited RAM.
Advanced Quantization: Go beyond basic INT8. Dive deep into 4-bit quantization (GPTQ, AWQ), K-Quants, and the GGUF format ecosystem to run models on consumer hardware without losing accuracy.
Pruning & Sparsity: Learn to implement 2:4 Structured Sparsity (Wanda) to leverage the hardware acceleration of modern mobile NPUs like Qualcomm Snapdragon and MediaTek.
Efficient Fine-Tuning: Personalize models directly on the edge using LoRA, QLoRA, and DoRA, minimizing memory usage while maximizing task-specific performance.
Hardware Acceleration: Unlock the full potential of Neural Processing Units (NPUs), DSPs, and the Apple Neural Engine using heterogeneous computing strategies.
Production Deployment: Profiling with Perfetto, managing thermal throttling, and securing your IP with encryption.
Machine Learning Engineers seeking to optimize Transformers for inference speed and memory efficiency.
Mobile Developers (iOS/Android) wanting to integrate Generative AI directly into apps using CoreML, TFLite, or ExecuTorch.
Embedded Systems Architects designing for the constraints of battery life, thermal limits, and memory bandwidth.
Frameworks: PyTorch, TensorFlow, ONNX Runtime, llama.cpp.
Algorithms: LoRA, QLoRA, Speculative Decoding, PagedAttention.
Hardware Focus: Apple Silicon (M-Series/A-Series), NVIDIA Jetson, Qualcomm Hexagon, Google Edge TPU.