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Comparison

llm-course vs human-eval

Verdict

Pick llm-course when license: llm-course is Apache-2.0, human-eval is MIT; pick human-eval when license: human-eval is MIT, llm-course is Apache-2.0.

Markdown twin · llm-course alternatives · human-eval alternatives

GraphCanon updated today

llm-course logo

llm-course

mlabonne/llm-course

★ 81kpushed Feb 5, 2026
vs
human-eval logo

human-eval

openai/human-eval

★ 3.3kpushed Jan 17, 2025

Trust & integrity

Signalllm-coursehuman-eval
Maintenance
Slowing (155d since push)
As of today · github_public_v1
Dormant (540d 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 criticals
As of today · osv@v1
llm-course trust reporthuman-eval trust reportTrust methodology

Tagline

llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
human-eval
Code for the paper "Evaluating Large Language Models Trained on Code"

Stars

llm-course
81k
human-eval
3.3k

Forks

llm-course
9.4k
human-eval
449

Open issues

llm-course
84
human-eval
42

Language

llm-course
-
human-eval
Python

Adopt for

llm-course
The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to
human-eval
-

Persona

llm-course
-
human-eval
-

Runtime

llm-course
-
human-eval
-

License

llm-course
Apache-2.0
human-eval
MIT

Last pushed

llm-course
Feb 5, 2026
human-eval
Jan 17, 2025

Categories

llm-course
LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability
human-eval
Model Training, LLM Frameworks, Evaluation & Observability

Trust and health

Maintenance

llm-course
Slowing (36%)
human-eval
Dormant (18%)

Days since push

llm-course
155d
human-eval
540d

Open issues (now)

llm-course
84
human-eval
42

Owner type

llm-course
User
human-eval
Organization

Security scan

llm-course
No lockfile
human-eval
No criticals

Full report

llm-course
Trust report
human-eval
Trust report

Shared compatibility

  • Python · llm-course: Python runtime · human-eval: Python runtime

Choose llm-course if…

  • License: llm-course is Apache-2.0, human-eval is MIT.
  • Requirements: Course materials are available in Colab notebooks; access requires a Google account.
  • Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models.
  • Also covers Inference & Serving.
  • - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge

When NOT to use llm-course

  • - If you only require a quick introduction to LLMs without deep dive into core components
  • - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI
GitHubDetails

Choose human-eval if…

  • License: human-eval is MIT, llm-course is Apache-2.0.
  • Tags unique to human-eval: python.
  • Leaner open-issue backlog (42).

When NOT to use human-eval

  • Last GitHub push was 540 days ago (dormant maintenance, Jan 17, 2025). Validate activity before betting a new project on human-eval.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
GitHubDetails

Explore

llm-course trust report →human-eval trust report →LLM Frameworks category →Model Training category →Inference & Serving category →Evaluation & Observability category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: llm-course 81k · human-eval 3.3k (synced Jul 11, 2026).

Common questions

What is the difference between llm-course and human-eval?
llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. human-eval: Code for the paper "Evaluating Large Language Models Trained on Code". See the comparison table for live GitHub stats and shared categories.
When should I choose llm-course over human-eval?
Choose llm-course over human-eval when License: llm-course is Apache-2.0, human-eval is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models; Also covers Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge.
When should I choose human-eval over llm-course?
Choose human-eval over llm-course when License: human-eval is MIT, llm-course is Apache-2.0; Tags unique to human-eval: python; Leaner open-issue backlog (42).
When should I avoid llm-course?
- If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI
When should I avoid human-eval?
Last GitHub push was 540 days ago (dormant maintenance, Jan 17, 2025). Validate activity before betting a new project on human-eval. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
Is llm-course or human-eval more popular on GitHub?
llm-course has more GitHub stars (80,839 vs 3,294). Stars measure visibility, not whether either tool fits your constraints.
Are llm-course and human-eval open source?
Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, human-eval: MIT).
Where can I find alternatives to llm-course or human-eval?
GraphCanon lists graph-backed alternatives at llm-course alternatives and human-eval alternatives (llm-course markdown twin, human-eval 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, llm-course or human-eval?
llm-course: Slowing. human-eval: 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 llm-course and human-eval?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-course trust report; human-eval trust report.