--- title: "HLCE vs codealpaca" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/humanity-s-last-code-exam-hlce-vs-sahil280114-codealpaca" tools: ["humanity-s-last-code-exam-hlce", "sahil280114-codealpaca"] --- # HLCE vs codealpaca *GraphCanon updated Aug 8, 2026* ## Verdict Pick HLCE if hLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes; pick codealpaca if a specialized LLaMA model for generating code from instructions, leveraging 20K fine-tuning data inspired by the Self-Instruct paper. [HLCE](https://humanity-s-last-code-exam.github.io/website/) reports 96 GitHub stars, 8 forks, and 1 open issues, last pushed Aug 21, 2025. [codealpaca](https://github.com/sahil280114/codealpaca) has 1.5k stars, 113 forks, and 17 open issues, last pushed May 12, 2023. Figures are from public GitHub metadata via [HLCE's repository](https://github.com/Humanity-s-Last-Code-Exam/HLCE) and [codealpaca's repository](https://github.com/sahil280114/codealpaca). | | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | [codealpaca](/tools/sahil280114-codealpaca.md) | | --- | --- | --- | | Tagline | Source Evaluation scripts for Humanity's Last Code Exam | An instruction-following LLaMA model for code generation. | | Stars | 96 | 1,514 | | Forks | 8 | 113 | | Open issues | 1 | 17 | | Language | Python | Python | | Adopt for | HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. | A specialized LLaMA model for generating code from instructions, leveraging 20K fine-tuning data inspired by the Self-Instruct paper. | | Persona | - | - | | Runtime | - | - | | License | - | The project uses Apache-2.0 license, allowing users to utilize the source code and documentation freely. | | Categories | Evaluation & Observability, LLM Frameworks | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | [codealpaca](/tools/sahil280114-codealpaca.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 352d | 1180d | | Open issues (now) | 1 | 17 | | Owner type | Organization | User | | Full report | [trust report](/tools/humanity-s-last-code-exam-hlce/trust.md) | [trust report](/tools/sahil280114-codealpaca/trust.md) | ## Decision facts: HLCE - **Adopt for:** HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. ## Decision facts: codealpaca - **Requirements:** Model weights are not available in this repository due to licensing restrictions. - **Adopt for:** A specialized LLaMA model for generating code from instructions, leveraging 20K fine-tuning data inspired by the Self-Instruct paper. - **License detail:** The project uses Apache-2.0 license, allowing users to utilize the source code and documentation freely. ## Choose when ### Choose HLCE if… - Tags unique to HLCE: benchmark, codegen, codellm, llm-evaluation. - Also covers Evaluation & Observability. - When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively. ### Choose codealpaca if… - Requirements: Model weights are not available in this repository due to licensing restrictions.. - Tags unique to codealpaca: code generation, fine-tuning, instruction-following, python. - Also covers Model Training. - When you need instruction-following capabilities tailored specifically for code generation tasks. ## When NOT to use HLCE - If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. - When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only. ## When NOT to use codealpaca - Avoid if you require models fine-tuned on datasets that cover a broader spectrum of non-code-related instructions beyond code editing and generation. - Do not use this tool when you must adhere to strict compliance or safety standards for model output, as the Code Alpaca model is noted to be unsafe and not fine-tuned for harmlessness. ## Common questions ### What is the difference between HLCE and codealpaca? HLCE: Source Evaluation scripts for Humanity's Last Code Exam. codealpaca: An instruction-following LLaMA model for code generation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose HLCE over codealpaca? Choose HLCE over codealpaca when Tags unique to HLCE: benchmark, codegen, codellm, llm-evaluation; Also covers Evaluation & Observability; When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively. ### When should I choose codealpaca over HLCE? Choose codealpaca over HLCE when Requirements: Model weights are not available in this repository due to licensing restrictions.; Tags unique to codealpaca: code generation, fine-tuning, instruction-following, python; Also covers Model Training; When you need instruction-following capabilities tailored specifically for code generation tasks. ### When should I avoid HLCE? If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only. ### When should I avoid codealpaca? Avoid if you require models fine-tuned on datasets that cover a broader spectrum of non-code-related instructions beyond code editing and generation. Do not use this tool when you must adhere to strict compliance or safety standards for model output, as the Code Alpaca model is noted to be unsafe and not fine-tuned for harmlessness. ### Is HLCE or codealpaca more popular on GitHub? codealpaca has more GitHub stars (1,514 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are HLCE and codealpaca open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to HLCE or codealpaca? GraphCanon lists graph-backed alternatives at [HLCE alternatives](/tools/humanity-s-last-code-exam-hlce/alternatives) and [codealpaca alternatives](/tools/sahil280114-codealpaca/alternatives) ([HLCE markdown twin](/tools/humanity-s-last-code-exam-hlce/alternatives.md), [codealpaca markdown twin](/tools/sahil280114-codealpaca/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/humanity-s-last-code-exam-hlce-vs-sahil280114-codealpaca.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, HLCE or codealpaca? HLCE: Slowing. codealpaca: 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 HLCE and codealpaca? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HLCE trust report](/tools/humanity-s-last-code-exam-hlce/trust); [codealpaca trust report](/tools/sahil280114-codealpaca/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=humanity-s-last-code-exam-hlce`](/api/graphcanon/graph?tool=humanity-s-last-code-exam-hlce) - 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/_