Comparison
AlignLLMHumanSurvey vs Awesome-LLMOps
Verdict
Pick AlignLLMHumanSurvey if alignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · AlignLLMHumanSurvey alternatives · Awesome-LLMOps alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | AlignLLMHumanSurvey | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (1034d since push) As of today · github_public_v1 | Steady (51d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- AlignLLMHumanSurvey
- A survey on aligning large language models with human expectations
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- AlignLLMHumanSurvey
- 742
- Awesome-LLMOps
- 5.9k
Forks
- AlignLLMHumanSurvey
- 30
- Awesome-LLMOps
- 901
Open issues
- AlignLLMHumanSurvey
- 0
- Awesome-LLMOps
- 157
Language
- AlignLLMHumanSurvey
- -
- Awesome-LLMOps
- Shell
Adopt for
- AlignLLMHumanSurvey
- AlignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- AlignLLMHumanSurvey
- -
- Awesome-LLMOps
- -
Runtime
- AlignLLMHumanSurvey
- -
- Awesome-LLMOps
- -
License
- AlignLLMHumanSurvey
- -
- Awesome-LLMOps
- CC0-1.0
Last pushed
- AlignLLMHumanSurvey
- Sep 11, 2023
- Awesome-LLMOps
- May 21, 2026
Categories
- AlignLLMHumanSurvey
- Model Training, Evaluation & Observability
- Awesome-LLMOps
- Vector Databases, LLM Frameworks, Model Training
Trust and health
Maintenance
- AlignLLMHumanSurvey
- Dormant (18%)
- Awesome-LLMOps
- Steady (60%)
Days since push
- AlignLLMHumanSurvey
- 1034d
- Awesome-LLMOps
- 51d
Open issues (now)
- AlignLLMHumanSurvey
- 0
- Awesome-LLMOps
- 157
Owner type
- AlignLLMHumanSurvey
- User
- Awesome-LLMOps
- Organization
Full report
- AlignLLMHumanSurvey
- Trust report
- Awesome-LLMOps
- Trust report
Choose AlignLLMHumanSurvey if…
- Tags unique to AlignLLMHumanSurvey: chinese-llama, awesome, llms, llama.
- Also covers Evaluation & Observability.
- 当您需要全面了解将大型语言模型与人类期望对齐的技术和方法时，可以使用AlignLLMHumanSurvey，它涵盖了数据收集、训练方法和模型评估等多个方面。
When NOT to use AlignLLMHumanSurvey
- 当您需要具体的技术实现代码或工具箱时，请勿使用AlignLLMHumanSurvey，因为它仅仅是一个汇总知识的调研文档，不提供具体的编程实现细节。
- 如果您正在寻找特定模型的性能评估数据或者准备在实际项目中直接应用某种对齐技术，则这个资源可能不会满足您的需求，因为它更侧重于理论和调查研究。
Choose Awesome-LLMOps if…
- Tags unique to Awesome-LLMOps: llmops, shell, awesome-list, mlops.
- Also covers Vector Databases, LLM Frameworks.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (GaryYufei/AlignLLMHumanSurvey) · observed Jul 11, 2026
- GitHub forks (GaryYufei/AlignLLMHumanSurvey) · observed Jul 11, 2026
- Last push (GaryYufei/AlignLLMHumanSurvey) · observed Sep 11, 2023
- License file (unknown) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Jul 11, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Jul 11, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: AlignLLMHumanSurvey 742 · Awesome-LLMOps 5.9k (synced Jul 11, 2026).
Common questions
- What is the difference between AlignLLMHumanSurvey and Awesome-LLMOps?
- AlignLLMHumanSurvey: A survey on aligning large language models with human expectations. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose AlignLLMHumanSurvey over Awesome-LLMOps?
- Choose AlignLLMHumanSurvey over Awesome-LLMOps when Tags unique to AlignLLMHumanSurvey: chinese-llama, awesome, llms, llama; Also covers Evaluation & Observability; 当您需要全面了解将大型语言模型与人类期望对齐的技术和方法时，可以使用AlignLLMHumanSurvey，它涵盖了数据收集、训练方法和模型评估等多个方面。.
- When should I choose Awesome-LLMOps over AlignLLMHumanSurvey?
- Choose Awesome-LLMOps over AlignLLMHumanSurvey when Tags unique to Awesome-LLMOps: llmops, shell, awesome-list, mlops; Also covers Vector Databases, LLM Frameworks; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid AlignLLMHumanSurvey?
- 当您需要具体的技术实现代码或工具箱时，请勿使用AlignLLMHumanSurvey，因为它仅仅是一个汇总知识的调研文档，不提供具体的编程实现细节。 如果您正在寻找特定模型的性能评估数据或者准备在实际项目中直接应用某种对齐技术，则这个资源可能不会满足您的需求，因为它更侧重于理论和调查研究。
- When should I avoid Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is AlignLLMHumanSurvey or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,877 vs 742). Stars measure visibility, not whether either tool fits your constraints.
- Are AlignLLMHumanSurvey and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to AlignLLMHumanSurvey or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at AlignLLMHumanSurvey alternatives and Awesome-LLMOps alternatives (AlignLLMHumanSurvey markdown twin, Awesome-LLMOps markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, AlignLLMHumanSurvey or Awesome-LLMOps?
- AlignLLMHumanSurvey: Dormant. Awesome-LLMOps: Steady. 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 AlignLLMHumanSurvey and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: AlignLLMHumanSurvey trust report; Awesome-LLMOps trust report.