--- title: "PiSSA vs superpipe" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mulabpku-pissa-vs-villagecomputing-superpipe" tools: ["mulabpku-pissa", "villagecomputing-superpipe"] --- # PiSSA vs superpipe *GraphCanon updated Aug 24, 2026* ## Verdict Pick PiSSA if piSSA targets efficient fine-tuning of large language models via principal singular values and vectors; pick superpipe if superpipe specializes in optimizing large language model pipelines for tasks involving structured data such as classification and extraction. [PiSSA](https://proceedings.neurips.cc/paper_files/paper/2024/file/db36f4d603cc9e3a2a5e10b93e6428f2-Paper-Conference.pdf) reports 430 GitHub stars, 23 forks, and 16 open issues, last pushed Jun 30, 2025. [superpipe](https://superpipe.ai) has 109 stars, 2 forks, and 3 open issues, last pushed Jun 18, 2024. Figures are from public GitHub metadata via [PiSSA's repository](https://github.com/MuLabPKU/PiSSA) and [superpipe's repository](https://github.com/villagecomputing/superpipe). | | [PiSSA](/tools/mulabpku-pissa.md) | [superpipe](/tools/villagecomputing-superpipe.md) | | --- | --- | --- | | Tagline | Principal Singular Values and Singular Vectors Adaptation of Large Language Models | Optimized LLM pipelines for structured data | | Stars | 430 | 109 | | Forks | 23 | 2 | | Open issues | 16 | 3 | | Language | Jupyter Notebook | Python | | Adopt for | PiSSA targets efficient fine-tuning of large language models via principal singular values and vectors. | Superpipe specializes in optimizing large language model pipelines for tasks involving structured data such as classification and extraction. | | Persona | - | - | | Runtime | - | - | | License | - | The license terms are under MIT, allowing for broad use and modification with attribution requirements maintained as per typical open-source licensing standards. | | Categories | LLM Frameworks, Model Training | Data & Retrieval, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [PiSSA](/tools/mulabpku-pissa.md) | [superpipe](/tools/villagecomputing-superpipe.md) | | --- | --- | --- | | Days since push | 420d | 770d | | Open issues (now) | 16 | 3 | | Stars delta | +1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/mulabpku-pissa/trust.md) | [trust report](/tools/villagecomputing-superpipe/trust.md) | ## Decision facts: PiSSA - **Adopt for:** PiSSA targets efficient fine-tuning of large language models via principal singular values and vectors. ## Decision facts: superpipe - **Pricing:** freemium - Superpipe is free to use under its MIT License for both commercial and non-commercial purposes, supporting a community-driven model with potential premium services or support options. - **Requirements:** The minimum Python version required is 3.10+, as specified in the installation section. - **Adopt for:** Superpipe specializes in optimizing large language model pipelines for tasks involving structured data such as classification and extraction. - **License detail:** The license terms are under MIT, allowing for broad use and modification with attribution requirements maintained as per typical open-source licensing standards. ## Choose when ### Choose PiSSA if… - PiSSA is primarily Jupyter Notebook; superpipe is Python. - Tags unique to PiSSA: fine-tuning, peft, quantization. - You need to fine-tune a large language model efficiently with limited resources. ### Choose superpipe if… - superpipe is primarily Python; PiSSA is Jupyter Notebook. - Pricing: Superpipe is free to use under its MIT License for both commercial and non-commercial purposes, supporting a community-driven model with potential premium services or support options.. - Requirements: The minimum Python version required is 3.10+, as specified in the installation section.. - Tags unique to superpipe: classification, data-extraction, data-labeling, llm-optimization. - Also covers Data & Retrieval. - When you have specific tasks requiring the processing of structured datasets, such as detailed classification or precise data extraction. ## When NOT to use PiSSA - Insufficient flexibility in model adaptation is acceptable, prefer broader customization options. - Full fine-tuning of the entire model rather than just key components via peft. ## When NOT to use superpipe - If your project focuses on unstructured data mainly like free-form text analysis without a need for specialized structured-data algorithms. - When the Python version requirement of at least 3.10 is not feasible in your development environment or dependencies. ## Common questions ### What is the difference between PiSSA and superpipe? PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models. superpipe: Optimized LLM pipelines for structured data. See the comparison table for live GitHub stats and shared categories. ### When should I choose PiSSA over superpipe? Choose PiSSA over superpipe when PiSSA is primarily Jupyter Notebook; superpipe is Python; Tags unique to PiSSA: fine-tuning, peft, quantization; You need to fine-tune a large language model efficiently with limited resources. ### When should I choose superpipe over PiSSA? Choose superpipe over PiSSA when superpipe is primarily Python; PiSSA is Jupyter Notebook; Pricing: Superpipe is free to use under its MIT License for both commercial and non-commercial purposes, supporting a community-driven model with potential premium services or support options.; Requirements: The minimum Python version required is 3.10+, as specified in the installation section.; Tags unique to superpipe: classification, data-extraction, data-labeling, llm-optimization; Also covers Data & Retrieval; When you have specific tasks requiring the processing of structured datasets, such as detailed classification or precise data extraction. ### When should I avoid PiSSA? Insufficient flexibility in model adaptation is acceptable, prefer broader customization options. Full fine-tuning of the entire model rather than just key components via peft. ### When should I avoid superpipe? If your project focuses on unstructured data mainly like free-form text analysis without a need for specialized structured-data algorithms. When the Python version requirement of at least 3.10 is not feasible in your development environment or dependencies. ### Is PiSSA or superpipe more popular on GitHub? PiSSA has more GitHub stars (430 vs 109). Stars measure visibility, not whether either tool fits your constraints. ### Are PiSSA and superpipe open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to PiSSA or superpipe? GraphCanon lists graph-backed alternatives at [PiSSA alternatives](/tools/mulabpku-pissa/alternatives) and [superpipe alternatives](/tools/villagecomputing-superpipe/alternatives) ([PiSSA markdown twin](/tools/mulabpku-pissa/alternatives.md), [superpipe markdown twin](/tools/villagecomputing-superpipe/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/mulabpku-pissa-vs-villagecomputing-superpipe.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, PiSSA or superpipe? PiSSA: Dormant. superpipe: 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 PiSSA and superpipe? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [PiSSA trust report](/tools/mulabpku-pissa/trust); [superpipe trust report](/tools/villagecomputing-superpipe/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mulabpku-pissa`](/api/graphcanon/graph?tool=mulabpku-pissa) - 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/_