Comparison
Model-Fingerprint vs hallucination-index
Verdict
Pick Model-Fingerprint if model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0; pick hallucination-index if hallucination-Index helps users identify LLMs with the lowest propensity for factual errors across varying context lengths and source types.
Markdown twin · Model-Fingerprint alternatives · hallucination-index alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Model-Fingerprint | hallucination-index |
|---|---|---|
| Maintenance | Dormant (754d since push) As of 2w · github_public_v1 | Dormant (365d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Model-Fingerprint
- Fingerprint large language models
- hallucination-index
- Initiative to evaluate and rank popular LLMs based on hallucination propensity
Stars
- Model-Fingerprint
- 52
- hallucination-index
- 116
Forks
- Model-Fingerprint
- 8
- hallucination-index
- 8
Open issues
- Model-Fingerprint
- 5
- hallucination-index
- 1
Language
- Model-Fingerprint
- Python
- hallucination-index
- -
Adopt for
- Model-Fingerprint
- Model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0.
- hallucination-index
- Hallucination-Index helps users identify LLMs with the lowest propensity for factual errors across varying context lengths and source types.
Persona
- Model-Fingerprint
- -
- hallucination-index
- -
Runtime
- Model-Fingerprint
- -
- hallucination-index
- -
License
- Model-Fingerprint
- MIT
- hallucination-index
- -
Last pushed
- Model-Fingerprint
- Jul 11, 2024
- hallucination-index
- Jul 28, 2025
Categories
- Model-Fingerprint
- Evaluation & Observability
- hallucination-index
- Evaluation & Observability
Trust and health
Days since push
- Model-Fingerprint
- 754d
- hallucination-index
- 365d
Open issues (now)
- Model-Fingerprint
- 5
- hallucination-index
- 1
Owner type
- Model-Fingerprint
- User
- hallucination-index
- Organization
OSV dependency advisories
- Model-Fingerprint
- No published findings from this source as of 2026-07-11
- hallucination-index
- No lockfile (source not queried)
Full report
- Model-Fingerprint
- Trust report
- hallucination-index
- Trust report
Choose Model-Fingerprint if…
- Tags unique to Model-Fingerprint: fingerprinting, pytorch.
- Use Model-Fingerprint when you need to fingerprint large language models for evaluation or observability purposes, especially in research contexts involving CUDA 11.3 and PyTorch 2.0 environments.
When NOT to use Model-Fingerprint
- Do not use Model-Fingerprint if your development environment does not support CUDA 11.3 and PyTorch 2.0, as it may lead to incompatibility issues.
- Avoid this toolset if you need a solution that supports multiple versions of CUDA or Pytorch for flexibility across different hardware configurations without modification.
Choose hallucination-index if…
- Tags unique to hallucination-index: hallucinations, llm-evaluation, openai, rag.
- Use when you need to ensure accuracy in short-context tasks, as it tests models like Chain-of-Note prompting techniques specifically for such scenarios.
- More GitHub stars (116 vs 52) - visibility, not fit.
When NOT to use hallucination-index
- Avoid using Hallucination-Index when your application requires real-time evaluation of hallucinations, as it focuses on predefined tests rather than live model performance.
- Do not rely solely on this index if your primary concern is the latest updates to LLM models; its data might not reflect recent improvements in models or the introduction of new ones.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (cnut1648/Model-Fingerprint) · observed Aug 5, 2026
- GitHub forks (cnut1648/Model-Fingerprint) · observed Aug 5, 2026
- Last push (cnut1648/Model-Fingerprint) · observed Jul 11, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (rungalileo/hallucination-index) · observed Jul 29, 2026
- GitHub forks (rungalileo/hallucination-index) · observed Jul 29, 2026
- Last push (rungalileo/hallucination-index) · observed Jul 28, 2025
- License file (unknown) · observed Jul 29, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Model-Fingerprint 52 · hallucination-index 116 (synced Aug 5, 2026).
Common questions
- What is the difference between Model-Fingerprint and hallucination-index?
- Model-Fingerprint: Fingerprint large language models. hallucination-index: Initiative to evaluate and rank popular LLMs based on hallucination propensity. See the comparison table for live GitHub stats and shared categories.
- When should I choose Model-Fingerprint over hallucination-index?
- Choose Model-Fingerprint over hallucination-index when Tags unique to Model-Fingerprint: fingerprinting, pytorch; Use Model-Fingerprint when you need to fingerprint large language models for evaluation or observability purposes, especially in research contexts involving CUDA 11.3 and PyTorch 2.0 environments.
- When should I choose hallucination-index over Model-Fingerprint?
- Choose hallucination-index over Model-Fingerprint when Tags unique to hallucination-index: hallucinations, llm-evaluation, openai, rag; Use when you need to ensure accuracy in short-context tasks, as it tests models like Chain-of-Note prompting techniques specifically for such scenarios; More GitHub stars (116 vs 52) - visibility, not fit.
- When should I avoid Model-Fingerprint?
- Do not use Model-Fingerprint if your development environment does not support CUDA 11.3 and PyTorch 2.0, as it may lead to incompatibility issues. Avoid this toolset if you need a solution that supports multiple versions of CUDA or Pytorch for flexibility across different hardware configurations without modification.
- When should I avoid hallucination-index?
- Avoid using Hallucination-Index when your application requires real-time evaluation of hallucinations, as it focuses on predefined tests rather than live model performance. Do not rely solely on this index if your primary concern is the latest updates to LLM models; its data might not reflect recent improvements in models or the introduction of new ones.
- Is Model-Fingerprint or hallucination-index more popular on GitHub?
- hallucination-index has more GitHub stars (116 vs 52). Stars measure visibility, not whether either tool fits your constraints.
- Are Model-Fingerprint and hallucination-index open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Model-Fingerprint or hallucination-index?
- GraphCanon lists graph-backed alternatives at Model-Fingerprint alternatives and hallucination-index alternatives (Model-Fingerprint markdown twin, hallucination-index 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, Model-Fingerprint or hallucination-index?
- Model-Fingerprint: Dormant. hallucination-index: 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 Model-Fingerprint and hallucination-index?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Model-Fingerprint trust report; hallucination-index trust report.