--- title: "Model-Fingerprint vs instruct-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cnut1648-model-fingerprint-vs-declare-lab-instruct-eval" tools: ["cnut1648-model-fingerprint", "declare-lab-instruct-eval"] --- # Model-Fingerprint vs instruct-eval *GraphCanon updated Aug 7, 2026* ## 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 instruct-eval if key facts about instruct-eval. [Model-Fingerprint](https://github.com/cnut1648/Model-Fingerprint) reports 52 GitHub stars, 8 forks, and 5 open issues, last pushed Jul 11, 2024. [instruct-eval](https://declare-lab.github.io/instruct-eval/) has 552 stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. Figures are from public GitHub metadata via [Model-Fingerprint's repository](https://github.com/cnut1648/Model-Fingerprint) and [instruct-eval's repository](https://github.com/declare-lab/instruct-eval). | | [Model-Fingerprint](/tools/cnut1648-model-fingerprint.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Tagline | Fingerprint large language models | Quantitative evaluation for instruction-tuned language models | | Stars | 52 | 552 | | Forks | 8 | 45 | | Open issues | 5 | 24 | | Language | Python | Python | | Adopt for | Model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0. | Key facts about instruct-eval | | Persona | - | - | | Runtime | - | - | | License | MIT | The tool is distributed under Apache-2.0 license | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Model-Fingerprint](/tools/cnut1648-model-fingerprint.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Days since push | 754d | 879d | | Open issues (now) | 5 | 24 | | Owner type | User | Organization | | Full report | [trust report](/tools/cnut1648-model-fingerprint/trust.md) | [trust report](/tools/declare-lab-instruct-eval/trust.md) | ## Decision facts: Model-Fingerprint - **Adopt for:** Model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0. ## Decision facts: instruct-eval - **Requirements:** Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation. - **Adopt for:** Key facts about instruct-eval - **License detail:** The tool is distributed under Apache-2.0 license ## Choose when ### Choose Model-Fingerprint if… - License: Model-Fingerprint is MIT, instruct-eval is Apache-2.0. - Tags unique to Model-Fingerprint: fingerprinting, large language models, 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. ### Choose instruct-eval if… - License: instruct-eval is Apache-2.0, Model-Fingerprint is MIT. - Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.. - Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm. - When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ## 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. ## When NOT to use instruct-eval - When primarily interested in general model evaluation without a focus on instruction-tuned LMs. - If your primary interest lies in qualitative assessment rather than quantitative metrics. - If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ## Common questions ### What is the difference between Model-Fingerprint and instruct-eval? Model-Fingerprint: Fingerprint large language models. instruct-eval: Quantitative evaluation for instruction-tuned language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Model-Fingerprint over instruct-eval? Choose Model-Fingerprint over instruct-eval when License: Model-Fingerprint is MIT, instruct-eval is Apache-2.0; Tags unique to Model-Fingerprint: fingerprinting, large language models, 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 instruct-eval over Model-Fingerprint? Choose instruct-eval over Model-Fingerprint when License: instruct-eval is Apache-2.0, Model-Fingerprint is MIT; Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ### 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 instruct-eval? When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ### Is Model-Fingerprint or instruct-eval more popular on GitHub? instruct-eval has more GitHub stars (552 vs 52). Stars measure visibility, not whether either tool fits your constraints. ### Are Model-Fingerprint and instruct-eval open source? Yes - both are open-source projects on GitHub (Model-Fingerprint: MIT, instruct-eval: Apache-2.0). ### Where can I find alternatives to Model-Fingerprint or instruct-eval? GraphCanon lists graph-backed alternatives at [Model-Fingerprint alternatives](/tools/cnut1648-model-fingerprint/alternatives) and [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) ([Model-Fingerprint markdown twin](/tools/cnut1648-model-fingerprint/alternatives.md), [instruct-eval markdown twin](/tools/declare-lab-instruct-eval/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/cnut1648-model-fingerprint-vs-declare-lab-instruct-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Model-Fingerprint or instruct-eval? Model-Fingerprint: Dormant. instruct-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 Model-Fingerprint and instruct-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Model-Fingerprint trust report](/tools/cnut1648-model-fingerprint/trust); [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=cnut1648-model-fingerprint`](/api/graphcanon/graph?tool=cnut1648-model-fingerprint) - 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/_