Open Documentation
Post-Training Cookbooks
& Engineering Guides
A growing repository of utility scripts, bug fixes, tutorials, and architectural frameworks to support the community navigating model fine-tuning and agentic deployments.
RAG vs SFT: When to Use Which | SR Cookbooks
A technical breakdown of when to use Retrieval-Augmented Generation (knowledge) versus Supervised Fine-Tuning (behavior) in enterprise AI pipelines.
What Are RL Environments? Rubrics, Verifiable Rewards, and Scaling | SR Cookbooks
An introduction to RL environments: how agents observe, act, and receive rewards, the shift from human grading to verifiable rewards (RLVR) and rubrics, and scaling agent training.
What Is Grokking? An Introduction and some Open Questions | SR Cookbooks
An in-depth look at the grokking phenomenon in neural networks. Understand delayed generalization, review leading mechanistic theories, and run a minimal PyTorch MLP example.
Self-Hosting an Open-Weight Coding Agent on AWS | SR Cookbooks
A complete guide to self-hosting open coding models on AWS EC2 with vLLM, connecting VS Code Continue, running VPC-isolated MCP tools, and analyzing team infrastructure costs.
The Math of DPO, Explained | SR Cookbooks
A step-by-step mathematical derivation of Direct Preference Optimization (DPO), showing how the partition function cancels and verifying the implicit reward gradient in Python.
Building Local MCP Servers for VS Code & Cursor with FastMCP | SR Cookbooks
A step-by-step technical guide to building Python MCP servers with FastMCP, configuring VS Code and Cursor mcp.json, and debugging stdio JSON-RPC streams.
Speculative Decoding From Scratch | SR Cookbooks
Implement speculative decoding from scratch using NumPy to verify losslessness via rejection sampling and calculate wall-clock speedup bounds.
Power Analysis for Benchmark Design | SR Cookbooks
Calculate statistical power and minimum detectable accuracy gaps for LLM benchmarks using Python to avoid reporting sampling noise as signal.
Deduplication at Scale: MinHash & LSH in Python | SR Cookbooks
Implement MinHash and Locality-Sensitive Hashing (LSH) in pure Python to eliminate the quadratic bottleneck of near-duplicate detection for ML dataset curation.
Fixing EOS Errors: Why Fine-Tuned Models Talk to Themselves | SR Cookbooks
A technical guide to fixing the infinite generation bug in SFT by properly mapping EOS tokens and chat templates in Hugging Face.
What is the Model Context Protocol (MCP)? | SR Cookbooks
A technical architecture guide to the Model Context Protocol (MCP). Learn how it solves the M x N integration problem for AI agents and tool calling.
PEFT Explained: LoRA vs. QLoRA | SR Cookbooks
Understand the architectural differences between LoRA and QLoRA, and learn when to use each Parameter-Efficient Fine-Tuning technique based on your VRAM limits.
How to Train Custom Tokens with LoRA | SR Cookbooks
Learn how to fix untrained embedding errors when adding custom tokens to an LLM vocabulary during PEFT and LoRA fine-tuning.
Catastrophic Forgetting in Fine-Tuning | SR Cookbooks
What catastrophic forgetting is, why it happens when you fine-tune an LLM, what it costs you, and how to spot it, plus when a specialized small model can safely ignore it.
Inspecting What a Tiny Transformer Actually Learned | SR Cookbooks
A technical guide to probing a character-level PyTorch Transformer. Learn how to measure rule acquisition, test generalization, and ablate attention heads.
How to Check a Fine-Tuning Dataset Before You Train | SR Cookbooks
Avoid common fine-tuning failures. Learn how to validate chat templates, prevent silent truncation, verify loss masking, and check for data leakage before spending GPU hours.
Implementing LoRA from Scratch in PyTorch | SR Cookbooks
Learn how to write a custom LoRA wrapper in pure PyTorch. Freeze a base model, train low-rank A and B matrices, and merge the weights without using PEFT.
LoRA & Gradient Checkpointing: The Phantom Bug | SR Cookbooks
Wondering why combining LoRA with gradient checkpointing no longer crashes your script? A deep dive into PyTorch detached tensors, Hugging Face PEFT fixes, and manual graph attachment.
Knowledge Distillation from Scratch: Pure PyTorch | SR Cookbooks
Learn the underlying math of Knowledge Distillation. Move beyond standard SFT by implementing temperature scaling and Kullback-Leibler divergence from scratch in PyTorch.
Implementing a KV Cache From Scratch: Pure PyTorch | SR Cookbooks
Learn the mechanics of autoregressive LLM optimization. Implement a transformer Key-Value (KV) cache from scratch in PyTorch to massively speed up decoding.
Tokenizers From Scratch: BPE vs MaxMatch | SR Cookbooks
Stop treating tokenization as a black box. Learn how Byte Pair Encoding (BPE) and MaxMatch split text differently, and what mathematically happens when you add new tokens for fine-tuning.