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[[{“value”:”Learn how to build high-speed decision-making pipelines for language models using Jev in PyTorch. This comprehensive course covers both encoder and decoder architectures, custom causal attention masking, and KV-cache reuse to bypass slow autoregressive generation. You will also explore fine-tuning techniques, teacher-student distillation, and reinforcement learning methods like GRPO to optimize decision latency and accuracy.
Code: https://github.com/vivekkalyanarangan30/how-jev-works
✏️ Course developed by Vivek Kalyanarangan.
📘 Author, Quantization and Fast Inference (Manning)
https://hubs.la/Q04ff8F40
⭐️ Chapters ⭐️
– 0:00:00 Introduction & Course Overview
– 0:01:12 What is Jev & LLM Prefill vs. Decode
– 0:03:26 Jev Interfaces: Choice, Score, and Boolean
– 0:05:30 Prerequisites & Hardware Requirements
– 0:07:10 The 3-Phase Roadmap
– 0:08:00 Baseline: Vanilla LLM Token-by-Token Decoding
– 0:13:00 Stepping Through KV Cache & Autoregressive Loops
– 0:21:24 Encoder-Only Architecture for Fast Decisions
– 0:25:26 Implementing Encoder-Based Jev in PyTorch
– 0:32:00 Question Spans, Mask Tokens & Mean Pooling
– 0:36:30 Dot Product Interactions, Linear Heads & Fine-Tuning Approaches
– 0:40:42 Decoder-Only Architecture & Context Caching
– 0:46:22 Custom Causal Attention Masking for Multi-Question Packing
– 0:52:16 Implementing Decoder-Based Jev with KV Cache Re-use
– 1:03:52 Extracting Decision Point Logits & Token Probabilities
– 1:09:12 Handling Multi-Token Collision Issues
– 1:11:15 Training Workflows & Teacher-Student Distillation
– 1:12:35 Reinforcement Learning with GRPO & Perturbed Trajectories
– 1:16:54 Log Loss, Spherical Scores & Ranked Probability Score (RPS)
– 1:21:38 Constructing the Final RLCD Loss Function
– 1:23:17 Encoder vs. Decoder: Trade-offs & Architecture Comparison
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