Build a Reasoning Model (From Scratch), Video Edition

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Build a Reasoning Model (From Scratch), Video Edition

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Build a Reasoning Model (From Scratch), Video Edition
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001. Chapter 1 Understanding reasoning models.en.srt
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001. Chapter 1 Understanding reasoning models.mp4
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26.5 MB
002. Chapter 1 Understanding the standard LLM training pipeline.en.srt
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002. Chapter 1 Understanding the standard LLM training pipeline.mp4
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22.6 MB
003. Chapter 1 Improving LLM reasoning with training and inference techniques.en.srt
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003. Chapter 1 Improving LLM reasoning with training and inference techniques.mp4
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26.4 MB
004. Chapter 1 Pattern matching vs logical reasoning.en.srt
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004. Chapter 1 Pattern matching vs logical reasoning.mp4
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005. Chapter 1 Simulating reasoning without explicit rules.en.srt
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005. Chapter 1 Simulating reasoning without explicit rules.mp4
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006. Chapter 1 Why build reasoning models from scratch.en.srt
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006. Chapter 1 Why build reasoning models from scratch.mp4
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007. Chapter 1 A road map to building reasoning models from scratch.en.srt
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007. Chapter 1 A road map to building reasoning models from scratch.mp4
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008. Chapter 1 Summary.en.srt
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008. Chapter 1 Summary.mp4
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009. Chapter 2 Generating text with a pretrained LLM.en.srt
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009. Chapter 2 Generating text with a pretrained LLM.mp4
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010. Chapter 2 Setting up the coding environment.en.srt
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010. Chapter 2 Setting up the coding environment.mp4
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011. Chapter 2 Understanding hardware needs and recommendations.en.srt
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011. Chapter 2 Understanding hardware needs and recommendations.mp4
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012. Chapter 2 Preparing input texts for LLMs.en.srt
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012. Chapter 2 Preparing input texts for LLMs.mp4
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013. Chapter 2 Loading pretrained models.en.srt
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013. Chapter 2 Loading pretrained models.mp4
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014. Chapter 2 Understanding the sequential LLM text generation process.en.srt
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014. Chapter 2 Understanding the sequential LLM text generation process.mp4
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015. Chapter 2 Coding a minimal text generation function.en.srt
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015. Chapter 2 Coding a minimal text generation function.mp4
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016. Chapter 2 Faster inference via KV caching.en.srt
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016. Chapter 2 Faster inference via KV caching.mp4
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017. Chapter 2 Faster inference via PyTorch model compilation.en.srt
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017. Chapter 2 Faster inference via PyTorch model compilation.mp4
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018. Chapter 2 Summary.en.srt
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018. Chapter 2 Summary.mp4
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019. Chapter 3 Evaluating reasoning models.en.srt
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019. Chapter 3 Evaluating reasoning models.mp4
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020. Chapter 3 Loading a pretrained model to generate text.en.srt
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020. Chapter 3 Loading a pretrained model to generate text.mp4
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021. Chapter 3 Implementing a wrapper for easier text generation.en.srt
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021. Chapter 3 Implementing a wrapper for easier text generation.mp4
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022. Chapter 3 Extracting the final answer box.en.srt
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022. Chapter 3 Extracting the final answer box.mp4
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023. Chapter 3 Normalizing the extracted answer.en.srt
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023. Chapter 3 Normalizing the extracted answer.mp4
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024. Chapter 3 Verifying mathematical equivalence.en.srt
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024. Chapter 3 Verifying mathematical equivalence.mp4
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025. Chapter 3 Grading answers.en.srt
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025. Chapter 3 Grading answers.mp4
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026. Chapter 3 Loading the evaluation dataset.en.srt
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026. Chapter 3 Loading the evaluation dataset.mp4
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027. Chapter 3 Evaluating the model.en.srt
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027. Chapter 3 Evaluating the model.mp4
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028. Chapter 3 Summary.en.srt
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028. Chapter 3 Summary.mp4
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029. Chapter 4 Improving reasoning with inference-time scaling.en.srt
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029. Chapter 4 Improving reasoning with inference-time scaling.mp4
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030. Chapter 4 Loading a pretrained model.en.srt
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030. Chapter 4 Loading a pretrained model.mp4
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031. Chapter 4 Generating better responses with chain-of-thought prompting.en.srt
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031. Chapter 4 Generating better responses with chain-of-thought prompting.mp4
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032. Chapter 4 Controlling output diversity with temperature scaling.en.srt
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032. Chapter 4 Controlling output diversity with temperature scaling.mp4
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033. Chapter 4 Balancing diversity and coherence with top-p sampling.en.srt
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033. Chapter 4 Balancing diversity and coherence with top-p sampling.mp4
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034. Chapter 4 Improving response accuracy with self-consistency.en.srt
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034. Chapter 4 Improving response accuracy with self-consistency.mp4
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035. Chapter 4 Summary.en.srt
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035. Chapter 4 Summary.mp4
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036. Chapter 5 Inference-time scaling via self-refinement.en.srt
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036. Chapter 5 Inference-time scaling via self-refinement.mp4
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037. Chapter 5 Loading a pretrained model.en.srt
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037. Chapter 5 Loading a pretrained model.mp4
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038. Chapter 5 Scoring LLM responses with a rule-based score.en.srt
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038. Chapter 5 Scoring LLM responses with a rule-based score.mp4
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039. Chapter 5 Understanding token probability scores.en.srt
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039. Chapter 5 Understanding token probability scores.mp4
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040. Chapter 5 From token probability scores to log probabilities.en.srt
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040. Chapter 5 From token probability scores to log probabilities.mp4
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041. Chapter 5 Scoring model confidence with log probabilities.en.srt
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041. Chapter 5 Scoring model confidence with log probabilities.mp4
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042. Chapter 5 Self-refinement through iterative feedback.en.srt
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042. Chapter 5 Self-refinement through iterative feedback.mp4
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043. Chapter 5 Coding the self-refinement loop.en.srt
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043. Chapter 5 Coding the self-refinement loop.mp4
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044. Chapter 5 Summary.en.srt
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044. Chapter 5 Summary.mp4
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045. Chapter 6 Training reasoning models with reinforcement learning.en.srt
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045. Chapter 6 Training reasoning models with reinforcement learning.mp4
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046. Chapter 6 RLVR using GRPO.en.srt
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046. Chapter 6 RLVR using GRPO.mp4
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047. Chapter 6 Loading a pretrained model.en.srt
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047. Chapter 6 Loading a pretrained model.mp4
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048. Chapter 6 Loading a MATH training subset.en.srt
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048. Chapter 6 Loading a MATH training subset.mp4
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049. Chapter 6 Sampling rollouts.en.srt
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049. Chapter 6 Sampling rollouts.mp4
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050. Chapter 6 Calculating rewards.en.srt
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050. Chapter 6 Calculating rewards.mp4
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051. Chapter 6 Preparing learning signals from rollouts via advantages.en.srt
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051. Chapter 6 Preparing learning signals from rollouts via advantages.mp4
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052. Chapter 6 Scoring rollouts with sequence log probabilities.en.srt
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052. Chapter 6 Scoring rollouts with sequence log probabilities.mp4
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053. Chapter 6 From advantages to policy updates via the GRPO loss.en.srt
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053. Chapter 6 From advantages to policy updates via the GRPO loss.mp4
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054. Chapter 6 Putting everything together in a single GRPO function.en.srt
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054. Chapter 6 Putting everything together in a single GRPO function.mp4
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055. Chapter 6 Implementing the GRPO training loop.en.srt
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055. Chapter 6 Implementing the GRPO training loop.mp4
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056. Chapter 6 Loading and evaluating saved model checkpoints.en.srt
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056. Chapter 6 Loading and evaluating saved model checkpoints.mp4
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057. Chapter 6 Summary.en.srt
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057. Chapter 6 Summary.mp4
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058. Chapter 7 Improving GRPO for reinforcement learning.en.srt
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058. Chapter 7 Improving GRPO for reinforcement learning.mp4
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059. Chapter 7 Tracking GRPO performance metrics.en.srt
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059. Chapter 7 Tracking GRPO performance metrics.mp4
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060. Chapter 7 Tracking more advanced GRPO performance metrics.en.srt
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060. Chapter 7 Tracking more advanced GRPO performance metrics.mp4
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061. Chapter 7 Stabilizing sequence-level GRPO using clipped policy ratios.en.srt
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061. Chapter 7 Stabilizing sequence-level GRPO using clipped policy ratios.mp4
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062. Chapter 7 Controlling how much the model changes with a KL term.en.srt
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062. Chapter 7 Controlling how much the model changes with a KL term.mp4
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063. Chapter 7 Adding an explicit format reward.en.srt
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063. Chapter 7 Adding an explicit format reward.mp4
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064. Chapter 7 Summary.en.srt
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064. Chapter 7 Summary.mp4
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065. Chapter 8 Distilling reasoning models for efficient reasoning.en.srt
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065. Chapter 8 Distilling reasoning models for efficient reasoning.mp4
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066. Chapter 8 Generating a dataset for reasoning distillation.en.srt
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066. Chapter 8 Generating a dataset for reasoning distillation.mp4
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067. Chapter 8 Loading the MATH training dataset for distillation.en.srt
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067. Chapter 8 Loading the MATH training dataset for distillation.mp4
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068. Chapter 8 Building training examples.en.srt
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068. Chapter 8 Building training examples.mp4
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069. Chapter 8 Loading a pretrained model.en.srt
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069. Chapter 8 Loading a pretrained model.mp4
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070. Chapter 8 Computing the training and validation losses.en.srt
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070. Chapter 8 Computing the training and validation losses.mp4
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071. Chapter 8 Implementing the training loop for distillation.en.srt
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071. Chapter 8 Implementing the training loop for distillation.mp4
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072. Chapter 8 Evaluating the distilled model.en.srt
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072. Chapter 8 Evaluating the distilled model.mp4
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073. Chapter 8 Future directions for reasoning models.en.srt
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073. Chapter 8 Future directions for reasoning models.mp4
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074. Chapter 8 Conclusions.en.srt
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074. Chapter 8 Conclusions.mp4
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075. Chapter 8 Summary.en.srt
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075. Chapter 8 Summary.mp4
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076. appendix A References and further reading.en.srt
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076. appendix A References and further reading.mp4
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077. appendix A Chapter 2 Generating text with a pretrained LLM.en.srt
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077. appendix A Chapter 2 Generating text with a pretrained LLM.mp4
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078. appendix A Chapter 3 Evaluating reasoning models.en.srt
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078. appendix A Chapter 3 Evaluating reasoning models.mp4
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079. appendix A Chapter 4 Improving reasoning with inference-time scaling.en.srt
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079. appendix A Chapter 4 Improving reasoning with inference-time scaling.mp4
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080. appendix A Chapter 5 Inference-time scaling via self-refinement.en.srt
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080. appendix A Chapter 5 Inference-time scaling via self-refinement.mp4
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081. appendix A Chapter 6 Training reasoning models with reinforcement learning.en.srt
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081. appendix A Chapter 6 Training reasoning models with reinforcement learning.mp4
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082. appendix A Chapter 7 Improving GRPO for reinforcement learning.en.srt
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082. appendix A Chapter 7 Improving GRPO for reinforcement learning.mp4
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083. appendix A Chapter 8 Distilling reasoning models for efficient reasoning.en.srt
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083. appendix A Chapter 8 Distilling reasoning models for efficient reasoning.mp4
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084. appendix A Appendix F Common approaches to LLM evaluation.en.srt
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084. appendix A Appendix F Common approaches to LLM evaluation.mp4
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085. appendix B Exercise solutions.en.srt
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085. appendix B Exercise solutions.mp4
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086. appendix B Chapter 3.en.srt
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086. appendix B Chapter 3.mp4
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087. appendix B Chapter 4.en.srt
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087. appendix B Chapter 4.mp4
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088. appendix B Chapter 5.en.srt
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088. appendix B Chapter 5.mp4
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089. appendix B Chapter 6.en.srt
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089. appendix B Chapter 6.mp4
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090. appendix B Chapter 7.en.srt
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090. appendix B Chapter 7.mp4
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091. appendix B Chapter 8.en.srt
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091. appendix B Chapter 8.mp4
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092. appendix C Qwen3 LLM source code.en.srt
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092. appendix C Qwen3 LLM source code.mp4
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093. appendix C Feedforward module.en.srt
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093. appendix C Feedforward module.mp4
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094. appendix C Rotary position embeddings.en.srt
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094. appendix C Rotary position embeddings.mp4
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095. appendix C Grouped query attention.en.srt
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095. appendix C Grouped query attention.mp4
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096. appendix C Transformer block.en.srt
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096. appendix C Transformer block.mp4
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097. appendix C Main model code.en.srt
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097. appendix C Main model code.mp4
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098. appendix C KV cache.en.srt
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098. appendix C KV cache.mp4
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099. appendix C Tokenizer.en.srt
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099. appendix C Tokenizer.mp4
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100. appendix C Using the model.en.srt
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100. appendix C Using the model.mp4
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101. appendix D Using larger LLMs.en.srt
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101. appendix D Using larger LLMs.mp4
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102. appendix D Downloading larger checkpoints overview.en.srt
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102. appendix D Downloading larger checkpoints overview.mp4
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103. appendix D Loading a larger base model.en.srt
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103. appendix D Loading a larger base model.mp4
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104. appendix D Loading a larger reasoning variant.en.srt
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104. appendix D Loading a larger reasoning variant.mp4
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105. appendix D Practical recommendations.en.srt
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105. appendix D Practical recommendations.mp4
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106. appendix E Batching and throughput-oriented execution.en.srt
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106. appendix E Batching and throughput-oriented execution.mp4
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107. appendix E Running batched generation.en.srt
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107. appendix E Running batched generation.mp4
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108. appendix E Padding and attention masks.en.srt
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108. appendix E Padding and attention masks.mp4
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109. appendix E Chapter 3 Batched MATH-500 evaluation.en.srt
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109. appendix E Chapter 3 Batched MATH-500 evaluation.mp4
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110. appendix E Chapter 4 Batched self-consistency sampling.en.srt
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110. appendix E Chapter 4 Batched self-consistency sampling.mp4
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111. appendix E Chapter 6 Batched GRPO rollouts.en.srt
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111. appendix E Chapter 6 Batched GRPO rollouts.mp4
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112. appendix E Chapter 8 Batched distillation.en.srt
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112. appendix E Chapter 8 Batched distillation.mp4
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113. appendix E Single-sequence vs batch generation.en.srt
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113. appendix E Single-sequence vs batch generation.mp4
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114. appendix F Common approaches to model evaluation.en.srt
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114. appendix F Common approaches to model evaluation.mp4
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115. appendix F Evaluating answer-choice accuracy.en.srt
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115. appendix F Evaluating answer-choice accuracy.mp4
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116. appendix F Using verifiers to check answers.en.srt
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116. appendix F Using verifiers to check answers.mp4
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117. appendix F Comparing models using preferences and leaderboards.en.srt
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117. appendix F Comparing models using preferences and leaderboards.mp4
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118. appendix F Judging responses with other LLMs.en.srt
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118. appendix F Judging responses with other LLMs.mp4
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119. appendix G Building a chat interface.en.srt
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119. appendix G Building a chat interface.mp4
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120. appendix G Running the code as a script.en.srt
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120. appendix G Running the code as a script.mp4
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121. appendix G Downloading the scripts.en.srt
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121. appendix G Downloading the scripts.mp4
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122. appendix G The regular single-turn script.en.srt
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122. appendix G The regular single-turn script.mp4
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123. appendix G Running the single-turn script.en.srt
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123. appendix G Running the single-turn script.mp4
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124. appendix G The multi-turn interface.en.srt
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124. appendix G The multi-turn interface.mp4
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Bonus Resources.txt
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102.4 B

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