Stanford Alpaca

Generates diverse instruction-following examples by prompting GPT-3.5 Turbo (text-davinci-003) with batch decoding to produce 20 instructions simultaneously, then filtering for diversity and quality. Implements the Self-Instruct methodology with simplified pipeline (removes classification vs non-classification distinction) to create 52K unique instruction-input-output triplets at scale. Uses in-context learning with seed examples to bootstrap diverse task coverage across domains.

Defines and enforces a standardized JSON schema for instruction-following examples with three fields: instruction (task description), input (optional context), and output (expected response). Provides structured format that became the de facto template for all subsequent instruction datasets. Includes validation logic to ensure consistency and completeness across 52K examples, enabling downstream tools to parse and process uniformly.

Fine-tunes Meta's LLaMA-7B base model on 52K instruction examples using Hugging Face Transformers with hyperparameters optimized for consumer hardware: batch size 128, learning rate 2e-5, 3 epochs, max sequence length 512. Implements three memory optimization strategies—Fully Sharded Data Parallel (FSDP), DeepSpeed with CPU offloading, and Low-Rank Adaptation (LoRA)—to enable training on limited VRAM. Produces weight differentials (only delta from base model) for efficient distribution.

Enables users to reconstruct the full Alpaca model by combining Meta's original LLaMA-7B weights with released weight differentials (delta parameters). Implements a conversion and merging process that applies the fine-tuning delta to the base model, avoiding need to redistribute full model weights and circumventing licensing restrictions. Users provide their own LLaMA weights, then apply the delta to recover the complete Alpaca model for inference.

Provides two standardized prompt templates for inference: one for instructions with optional input context (includes ### Input section) and one for instructions alone. Templates use consistent formatting with clear delimiters (### Instruction, ### Input, ### Response) to guide model generation. Designed to match training data format, ensuring model sees consistent prompt structure during both fine-tuning and inference. Enables reproducible evaluation and comparison across instruction-following models.

Analyzes the 52K instruction dataset to ensure coverage across diverse task categories and domains. Uses seed examples and in-context prompting to guide GPT-3.5 generation toward underrepresented task types. Implements heuristic-based diversity filtering to avoid duplicate or near-duplicate instructions within batches. Provides visibility into task distribution across categories (writing, math, coding, reasoning, etc.) to validate dataset quality and identify gaps.

Provides a complete, configurable fine-tuning pipeline built on Hugging Face Transformers that accepts hyperparameter configurations (batch size, learning rate, epochs, sequence length, weight decay). Includes training script that handles data loading, model initialization, loss computation, and checkpoint saving. Supports multiple optimization backends (FSDP, DeepSpeed, LoRA) via configuration flags. Enables researchers to reproduce Alpaca training or adapt hyperparameters for different model sizes and hardware constraints.