--- title: "llm-course vs P-tuning-v2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-thudm-p-tuning-v2" tools: ["mlabonne-llm-course", "thudm-p-tuning-v2"] --- # llm-course vs P-tuning-v2 *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-course when requirements: Course materials are available in Colab notebooks; access requires a Google account; pick P-tuning-v2 when tags unique to P-tuning-v2: p-tuning, python, prompt-tuning, parameter-efficient-learning. [llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. [P-tuning-v2](https://github.com/THUDM/P-tuning-v2) has 2.1k stars, 212 forks, and 35 open issues, last pushed Nov 16, 2023. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [P-tuning-v2's repository](https://github.com/THUDM/P-tuning-v2). | | [llm-course](/tools/mlabonne-llm-course.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks | | Stars | 80,839 | 2,075 | | Forks | 9,421 | 212 | | Open issues | 84 | 35 | | Language | - | Python | | Adopt for | 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 | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | LLM Frameworks, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 155d | 968d | | Open issues (now) | 84 | 35 | | Owner type | User | Organization | | Security scan | No lockfile | 50 low (50 low) | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/thudm-p-tuning-v2/trust.md) | ## Shared compatibility - **Python**: [llm-course](/tools/mlabonne-llm-course.md) - Python runtime; [P-tuning-v2](/tools/thudm-p-tuning-v2.md) - Python runtime ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** 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 - **License detail:** Apache-2.0 ## Choose when ### Choose llm-course if… - 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, Evaluation & Observability. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ### Choose P-tuning-v2 if… - Tags unique to P-tuning-v2: p-tuning, python, prompt-tuning, parameter-efficient-learning. - Also covers Vector Databases. - Leaner open-issue backlog (35). ## 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 ## When NOT to use P-tuning-v2 - Last GitHub push was 969 days ago (dormant maintenance, Nov 16, 2023). Validate activity before betting a new project on P-tuning-v2. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between llm-course and P-tuning-v2? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. P-tuning-v2: An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over P-tuning-v2? Choose llm-course over P-tuning-v2 when 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, Evaluation & Observability; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I choose P-tuning-v2 over llm-course? Choose P-tuning-v2 over llm-course when Tags unique to P-tuning-v2: p-tuning, python, prompt-tuning, parameter-efficient-learning; Also covers Vector Databases; Leaner open-issue backlog (35). ### 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 P-tuning-v2? Last GitHub push was 969 days ago (dormant maintenance, Nov 16, 2023). Validate activity before betting a new project on P-tuning-v2. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is llm-course or P-tuning-v2 more popular on GitHub? llm-course has more GitHub stars (80,839 vs 2,075). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and P-tuning-v2 open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, P-tuning-v2: Apache-2.0). ### Where can I find alternatives to llm-course or P-tuning-v2? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [P-tuning-v2 alternatives](/tools/thudm-p-tuning-v2/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [P-tuning-v2 markdown twin](/tools/thudm-p-tuning-v2/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/mlabonne-llm-course-vs-thudm-p-tuning-v2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or P-tuning-v2? llm-course: Slowing. P-tuning-v2: 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 P-tuning-v2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [P-tuning-v2 trust report](/tools/thudm-p-tuning-v2/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course) - 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/_