{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"stanford-alpaca","slug":"stanford-alpaca","name":"Stanford Alpaca","type":"dataset","url":"https://github.com/tatsu-lab/stanford_alpaca","page_url":"https://unfragile.ai/stanford-alpaca","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"stanford-alpaca__cap_0","uri":"capability://data.processing.analysis.self.instruct.dataset.generation.via.gpt.3.5.bootstrapping","name":"self-instruct dataset generation via gpt-3.5 bootstrapping","description":"Generates diverse instruction-following examples by prompting GPT-3.5 Turbo (text-davinci-003) with seed instructions and iteratively expanding the dataset through batch decoding of 20 instructions at once. Uses a simplified Self-Instruct pipeline that removes classification/non-classification distinctions, producing 52K unique instruction-input-output triplets with minimal human annotation. The approach demonstrates that a single API call budget (~$500) can create training data sufficient for 7B model instruction-tuning.","intents":["Generate large-scale instruction-following training datasets without manual annotation","Bootstrap instruction diversity from minimal seed examples using LLM-in-the-loop generation","Reduce dataset creation cost from thousands of human hours to single-digit API spend"],"best_for":["researchers prototyping instruction-tuned models on limited budgets","teams building domain-specific instruction datasets from scratch","organizations wanting to replicate instruction-tuning without proprietary data"],"limitations":["Requires OpenAI API access and associated costs (~$500 for 52K examples)","Generated data inherits biases and limitations of GPT-3.5 Turbo","No built-in deduplication or quality filtering beyond diversity sampling","Batch decoding of 20 instructions increases latency per generation cycle"],"requires":["OpenAI API key with text-davinci-003 access","Python 3.7+","Sufficient API quota for ~52K instruction generations"],"input_types":["seed instructions (text)","generation parameters (JSON config)"],"output_types":["instruction-input-output triplets (JSON/JSONL)","52K examples in standardized format"],"categories":["data-processing-analysis","dataset-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stanford-alpaca__cap_1","uri":"capability://data.processing.analysis.instruction.following.dataset.format.standardization","name":"instruction-following dataset format standardization","description":"Defines a canonical JSON schema for instruction-following examples with three fields: instruction (task description), input (optional context), and output (expected response). This simple, language-agnostic format became the de facto standard for all subsequent instruction-tuning datasets. The schema is minimal enough to support diverse task types (classification, generation, reasoning) while structured enough for reproducible fine-tuning pipeline integration.","intents":["Establish a reusable data format for instruction-tuning across different model architectures","Enable easy composition and mixing of multiple instruction datasets","Provide a template that downstream projects can adopt without modification"],"best_for":["dataset creators building instruction-tuning corpora","framework developers integrating multiple instruction datasets","researchers comparing models trained on heterogeneous instruction sources"],"limitations":["No built-in support for multi-turn conversations or dialogue history","No metadata fields for task category, difficulty, or source attribution","Flat structure makes it difficult to represent hierarchical task dependencies","No versioning or provenance tracking within the format itself"],"requires":["JSON or JSONL file support","Text encoding (UTF-8)"],"input_types":["instruction (string)","input (string, optional)","output (string)"],"output_types":["JSON objects","JSONL files (one example per line)"],"categories":["data-processing-analysis","schema-definition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stanford-alpaca__cap_2","uri":"capability://automation.workflow.llama.7b.fine.tuning.with.memory.optimized.training","name":"llama 7b fine-tuning with memory-optimized training","description":"Fine-tunes Meta's LLaMA-7B base model on the 52K instruction dataset using Hugging Face Transformers with configurable memory optimization techniques. Supports three optimization strategies: Fully Sharded Data Parallel (FSDP) for distributed training, DeepSpeed with CPU offloading for single-GPU training, and Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. Uses fixed hyperparameters (batch size 128, learning rate 2e-5, 3 epochs, max sequence length 512) optimized for 7B models to fit within typical GPU memory constraints.","intents":["Fine-tune instruction-following capability onto LLaMA-7B within single-GPU or multi-GPU constraints","Reduce memory footprint of fine-tuning through LoRA or DeepSpeed CPU offloading","Reproduce Alpaca model training with standardized hyperparameters across different hardware"],"best_for":["researchers with limited GPU memory (single 40GB A100 or equivalent)","teams fine-tuning LLaMA variants for instruction-following tasks","practitioners wanting reproducible training with published hyperparameters"],"limitations":["Fixed hyperparameters (batch size 128, lr 2e-5) may not be optimal for all domains or model sizes","Max sequence length of 512 tokens truncates longer instructions and outputs","FSDP requires multi-GPU setup; LoRA reduces model capacity compared to full fine-tuning","DeepSpeed CPU offloading adds ~30-50% training time overhead vs GPU-only training","No built-in learning rate scheduling or warmup strategies beyond linear decay"],"requires":["LLaMA-7B base model weights (requires Meta license)","PyTorch 1.13+","Hugging Face Transformers 4.28+","CUDA 11.8+ for GPU training","Minimum 40GB GPU memory (A100) or multi-GPU setup with FSDP","Python 3.9+"],"input_types":["instruction dataset (JSONL format)","LLaMA-7B model weights","training configuration (JSON)"],"output_types":["fine-tuned model weights","training logs (TensorBoard format)","checkpoint files (PyTorch .pt format)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stanford-alpaca__cap_3","uri":"capability://data.processing.analysis.weight.differential.recovery.for.model.reconstruction","name":"weight differential recovery for model reconstruction","description":"Enables reconstruction of the full Alpaca model by combining the original LLaMA-7B weights with a published weight differential (delta). The recovery process converts Meta's LLaMA weights to Hugging Face format, then applies the delta to reconstruct the fine-tuned Alpaca weights. This approach circumvents direct distribution of fine-tuned weights by leveraging the mathematical property that fine_tuned_weights = base_weights + delta, allowing users to recover the model while respecting Meta's LLaMA licensing constraints.","intents":["Reconstruct Alpaca model weights from base LLaMA and published delta without direct distribution","Enable model sharing while respecting Meta's LLaMA license restrictions","Allow users with LLaMA access to obtain fine-tuned weights through a lightweight delta"],"best_for":["researchers with legitimate Meta LLaMA-7B access","practitioners wanting to use Alpaca without downloading full fine-tuned weights","organizations distributing instruction-tuned models under licensing constraints"],"limitations":["Requires users to independently obtain LLaMA-7B weights (not provided by Stanford)","Delta recovery is irreversible — cannot extract delta from fine-tuned weights","Requires format conversion from Meta's LLaMA format to Hugging Face format","No verification mechanism to ensure weight integrity after recovery","Recovery process is one-time; cannot update delta for new fine-tuning runs without full weight redistribution"],"requires":["Meta LLaMA-7B model weights (requires separate Meta license agreement)","Published Alpaca weight delta (provided by Stanford)","PyTorch 1.13+","Python 3.9+","~30GB disk space for base + recovered weights"],"input_types":["LLaMA-7B weights (Meta format)","Alpaca weight delta (PyTorch .pt file)"],"output_types":["Reconstructed Alpaca-7B weights (Hugging Face format)","Model configuration files"],"categories":["data-processing-analysis","model-distribution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stanford-alpaca__cap_4","uri":"capability://text.generation.language.prompt.template.formatting.for.instruction.following.inference","name":"prompt template formatting for instruction-following inference","description":"Defines two prompt templates for model inference depending on whether optional input context is provided. For instructions with input, wraps the instruction and input in a structured format with explicit section headers (### Instruction, ### Input, ### Response). For instructions without input, uses a simplified template with only instruction and response sections. These templates were used during training and must be replicated during inference to maintain consistency with the fine-tuned model's learned formatting expectations.","intents":["Format user instructions consistently with training-time prompt structure for optimal model performance","Support both simple instructions and complex tasks with optional context input","Enable reproducible inference across different deployment environments"],"best_for":["practitioners deploying Alpaca for inference","teams building instruction-following applications on top of Alpaca","researchers studying prompt engineering effects on instruction-tuned models"],"limitations":["Templates are rigid and don't support multi-turn conversations or dialogue history","No built-in support for system prompts or role-playing instructions","Template format is specific to Alpaca; not generalizable to other instruction-tuned models","No guidance on optimal input/output length or token budgets","Model was not fine-tuned for safety; templates don't include safety guardrails"],"requires":["Alpaca-7B model weights (recovered or fine-tuned)","Hugging Face Transformers 4.28+","Python 3.9+"],"input_types":["instruction (string)","input (string, optional)"],"output_types":["formatted prompt (string)","model response (string)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stanford-alpaca__cap_5","uri":"capability://data.processing.analysis.instruction.diversity.sampling.and.deduplication","name":"instruction diversity sampling and deduplication","description":"During dataset generation, the Self-Instruct pipeline samples diverse instructions from the growing pool to avoid redundancy and ensure coverage across task types. The simplified Alpaca pipeline removes the original Self-Instruct distinction between classification and non-classification tasks, treating all instructions uniformly. Diversity is maintained through batch decoding (generating 20 instructions per API call) and iterative sampling from the existing pool to seed new instruction generation, creating a balanced distribution across task types without explicit task categorization.","intents":["Ensure generated instruction dataset covers diverse task types without manual categorization","Avoid redundant or near-duplicate instructions in the final 52K dataset","Maintain instruction diversity while minimizing API calls through batch generation"],"best_for":["dataset creators wanting diverse instruction coverage without manual task labeling","teams generating large instruction datasets with limited annotation budgets","researchers studying the relationship between instruction diversity and model generalization"],"limitations":["Diversity sampling is implicit through batch decoding; no explicit diversity metrics or guarantees","No deduplication mechanism beyond iterative sampling — near-duplicate instructions may exist","Batch size of 20 is fixed; no adaptive batching based on diversity metrics","Simplified pipeline (no task distinction) may reduce task coverage compared to original Self-Instruct","No built-in filtering for low-quality or nonsensical instructions"],"requires":["OpenAI API access","Python 3.7+","Seed instruction pool (typically 175 examples)"],"input_types":["seed instructions (text list)","generation parameters (batch size, num iterations)"],"output_types":["diverse instruction pool (JSONL)","generation logs (JSON)"],"categories":["data-processing-analysis","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stanford-alpaca__cap_6","uri":"capability://planning.reasoning.instruction.tuning.evaluation.on.downstream.tasks","name":"instruction-tuning evaluation on downstream tasks","description":"Evaluates the fine-tuned Alpaca-7B model on instruction-following tasks using human evaluation and comparison to GPT-3.5 Turbo (text-davinci-003). The evaluation framework assesses model responses on dimensions like instruction adherence, factuality, and helpfulness. Preliminary results show Alpaca-7B achieves comparable performance to text-davinci-003 on instruction-following tasks despite being 50x smaller, demonstrating the effectiveness of instruction-tuning for capability transfer.","intents":["Measure instruction-following capability of fine-tuned models against proprietary baselines","Validate that instruction-tuning transfers capabilities from large models to smaller models","Establish benchmarks for evaluating instruction-tuned model quality"],"best_for":["researchers validating instruction-tuning effectiveness on new models","teams comparing instruction-tuned models to proprietary baselines","practitioners assessing whether fine-tuned models meet production quality standards"],"limitations":["Evaluation is primarily human-based; no automated metrics provided","Comparison is to text-davinci-003 only; no comparison to other instruction-tuned models","Evaluation set size and composition not fully documented","No breakdown of performance by task type or difficulty","Human evaluation is subjective and not reproducible without detailed rubrics","Model was not fine-tuned for safety; evaluation does not assess safety or harmlessness"],"requires":["Alpaca-7B model (recovered or fine-tuned)","Evaluation dataset (instruction-following tasks)","Human evaluators or automated evaluation framework","Baseline model access (text-davinci-003 for comparison)"],"input_types":["instruction-following test set (JSONL)","model outputs (text)"],"output_types":["evaluation scores (numeric)","human judgments (text)","comparison results (JSON)"],"categories":["planning-reasoning","evaluation-benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stanford-alpaca__headline","uri":"capability://model.training.instruction.following.dataset.for.fine.tuning.language.models","name":"instruction-following dataset for fine-tuning language models","description":"Stanford Alpaca is a pioneering dataset of 52,000 instruction-following examples designed for fine-tuning language models, enabling researchers to create aligned AI systems with minimal cost and effort.","intents":["best instruction-following dataset","instruction-following dataset for model training","top datasets for fine-tuning language models","datasets for creating instruction-following AI","best datasets for LLaMA fine-tuning"],"best_for":["research purposes","cost-effective model training"],"limitations":["not fine-tuned for safety"],"requires":["access to LLaMA model"],"input_types":["instruction","optional input"],"output_types":["expected output"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with text-davinci-003 access","Python 3.7+","Sufficient API quota for ~52K instruction generations","JSON or JSONL file support","Text encoding (UTF-8)","LLaMA-7B base model weights (requires Meta license)","PyTorch 1.13+","Hugging Face Transformers 4.28+","CUDA 11.8+ for GPU training","Minimum 40GB GPU memory (A100) or multi-GPU setup with FSDP"],"failure_modes":["Requires OpenAI API access and associated costs (~$500 for 52K examples)","Generated data inherits biases and limitations of GPT-3.5 Turbo","No built-in deduplication or quality filtering beyond diversity sampling","Batch decoding of 20 instructions increases latency per generation cycle","No built-in support for multi-turn conversations or dialogue history","No metadata fields for task category, difficulty, or source attribution","Flat structure makes it difficult to represent hierarchical task dependencies","No versioning or provenance tracking within the format itself","Fixed hyperparameters (batch size 128, lr 2e-5) may not be optimal for all domains or model sizes","Max sequence length of 512 tokens truncates longer instructions and outputs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stanford-alpaca","compare_url":"https://unfragile.ai/compare?artifact=stanford-alpaca"}},"signature":"P5owwqB7i2+kwYpa5QqEKCkga383y0ZZFkElHcMJ8l3fcUGTPkJ4mjUW/FZzDwYmvT+K5V4tNunjDgeDnjJYAQ==","signedAt":"2026-06-20T04:18:49.839Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stanford-alpaca","artifact":"https://unfragile.ai/stanford-alpaca","verify":"https://unfragile.ai/api/v1/verify?slug=stanford-alpaca","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}