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All digests tagged LLMs from Scratch book
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Model distillation offers an efficient alternative to RLVR for reasoning LLMs. By training a smaller student model on the outputs of a large teacher model, developers can achieve high performance with reduced computational cost.
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This session details implementing Reinforcement Learning with Verifiable Rewards (RLVR) using the GRPO algorithm to train a reasoning model. The process involves calculating rewards, advantages, and policy gradient loss to enhance reasoning capability.
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This deep dive explores inference-time scaling techniques for LLMs, covering Chain-of-Thought prompting, Temperature Scaling, Top-p filtering, and Self-Consistency. Learn how to boost model accuracy by managing compute trade-offs.
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The video details building a verifier for LLM evaluation on math benchmarks. This robust pipeline involves extracting the final answer, normalizing the format, and using symbolic math libraries like sympy to verify mathematical equivalence for RL training.
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This deep dive covers loading pre-trained LLMs (Qwen3), implementing autoregressive text generation in PyTorch, and optimizing performance using KV caching and torch.compile for maximum inference speed.
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This guide details loading pre-trained LLMs (like Qwen3) and optimizing text generation through tokenization, KV caching, and torch.compile. Learn how to achieve high tokens/second throughput for reasoning models.
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Learn how to build reasoning models from scratch, tracing the evolution from conventional LLMs to agent harnesses. The video covers setup using 'uv' and PyTorch best practices.
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