---
title: "awesome-llm-human-preference-datasets vs stanford_alpaca"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/glgh-awesome-llm-human-preference-datasets-vs-tatsu-lab-stanford-alpaca"
tools: ["glgh-awesome-llm-human-preference-datasets", "tatsu-lab-stanford-alpaca"]
---

# awesome-llm-human-preference-datasets vs stanford_alpaca

*GraphCanon updated Aug 6, 2026*

## Verdict

Pick awesome-llm-human-preference-datasets if awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被; pick stanford_alpaca if resources for fine-tuning an instruction-following LLaMA model by Stanford University.

[awesome-llm-human-preference-datasets](https://github.com/glgh/awesome-llm-human-preference-datasets) reports 390 GitHub stars, 19 forks, and 0 open issues, last pushed Oct 4, 2023. [stanford_alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) has 30k stars, 4.0k forks, and 187 open issues, last pushed Jul 17, 2024. Figures are from public GitHub metadata via [awesome-llm-human-preference-datasets's repository](https://github.com/glgh/awesome-llm-human-preference-datasets) and [stanford_alpaca's repository](https://github.com/tatsu-lab/stanford_alpaca).

| | [awesome-llm-human-preference-datasets](/tools/glgh-awesome-llm-human-preference-datasets.md) | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) |
| --- | --- | --- |
| Tagline | Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval | Code and documentation to train Stanford's Alpaca models |
| Stars | 390 | 30,244 |
| Forks | 19 | 3,992 |
| Open issues | 0 | 187 |
| Language | - | Python |
| Adopt for | awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被 | Resources for fine-tuning an instruction-following LLaMA model by Stanford University. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Apache-2.0 |
| Categories | Evaluation & Observability, Model Training | Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [awesome-llm-human-preference-datasets](/tools/glgh-awesome-llm-human-preference-datasets.md) | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) |
| --- | --- | --- |
| Days since push | 1036d | 745d |
| Open issues (now) | 0 | 187 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/glgh-awesome-llm-human-preference-datasets/trust.md) | [trust report](/tools/tatsu-lab-stanford-alpaca/trust.md) |

## Decision facts: awesome-llm-human-preference-datasets

- **Adopt for:** awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被

## Decision facts: stanford_alpaca

- **Adopt for:** Resources for fine-tuning an instruction-following LLaMA model by Stanford University.

## Choose when

### Choose awesome-llm-human-preference-datasets if…

- License: awesome-llm-human-preference-datasets is MIT, stanford_alpaca is Apache-2.0.
- Tags unique to awesome-llm-human-preference-datasets: awesome-list, datasets, eval, human-preferences.
- Also covers Evaluation & Observability.
- 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。

### Choose stanford_alpaca if…

- License: stanford_alpaca is Apache-2.0, awesome-llm-human-preference-datasets is MIT.
- Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model.
- When you are conducting academic research on language models and need to experiment with an instruction-following model like Alpaca.

## When NOT to use awesome-llm-human-preference-datasets

- NLP，LLM、，。

## When NOT to use stanford_alpaca

- For commercial applications, as the license restricts usage to research purposes only and prohibits use for non-academic projects.
- If you need a model that has been fine-tuned specifically for safety and ethical considerations, since the current version of Alpaca is still in development without these specific refinements.

## Common questions

### What is the difference between awesome-llm-human-preference-datasets and stanford_alpaca?

awesome-llm-human-preference-datasets: Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval. stanford_alpaca: Code and documentation to train Stanford's Alpaca models. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-llm-human-preference-datasets over stanford_alpaca?

Choose awesome-llm-human-preference-datasets over stanford_alpaca when License: awesome-llm-human-preference-datasets is MIT, stanford_alpaca is Apache-2.0; Tags unique to awesome-llm-human-preference-datasets: awesome-list, datasets, eval, human-preferences; Also covers Evaluation & Observability; 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。.

### When should I choose stanford_alpaca over awesome-llm-human-preference-datasets?

Choose stanford_alpaca over awesome-llm-human-preference-datasets when License: stanford_alpaca is Apache-2.0, awesome-llm-human-preference-datasets is MIT; Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model; When you are conducting academic research on language models and need to experiment with an instruction-following model like Alpaca.

### When should I avoid awesome-llm-human-preference-datasets?

NLP，LLM、，。

### When should I avoid stanford_alpaca?

For commercial applications, as the license restricts usage to research purposes only and prohibits use for non-academic projects. If you need a model that has been fine-tuned specifically for safety and ethical considerations, since the current version of Alpaca is still in development without these specific refinements.

### Is awesome-llm-human-preference-datasets or stanford_alpaca more popular on GitHub?

stanford_alpaca has more GitHub stars (30,244 vs 390). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-llm-human-preference-datasets and stanford_alpaca open source?

Yes - both are open-source projects on GitHub (awesome-llm-human-preference-datasets: MIT, stanford_alpaca: Apache-2.0).

### Where can I find alternatives to awesome-llm-human-preference-datasets or stanford_alpaca?

GraphCanon lists graph-backed alternatives at [awesome-llm-human-preference-datasets alternatives](/tools/glgh-awesome-llm-human-preference-datasets/alternatives) and [stanford_alpaca alternatives](/tools/tatsu-lab-stanford-alpaca/alternatives) ([awesome-llm-human-preference-datasets markdown twin](/tools/glgh-awesome-llm-human-preference-datasets/alternatives.md), [stanford_alpaca markdown twin](/tools/tatsu-lab-stanford-alpaca/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/glgh-awesome-llm-human-preference-datasets-vs-tatsu-lab-stanford-alpaca.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, awesome-llm-human-preference-datasets or stanford_alpaca?

awesome-llm-human-preference-datasets: Dormant. stanford_alpaca: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for awesome-llm-human-preference-datasets and stanford_alpaca?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-llm-human-preference-datasets trust report](/tools/glgh-awesome-llm-human-preference-datasets/trust); [stanford_alpaca trust report](/tools/tatsu-lab-stanford-alpaca/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=glgh-awesome-llm-human-preference-datasets`](/api/graphcanon/graph?tool=glgh-awesome-llm-human-preference-datasets)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_