--- title: "Awesome-Code-LLM vs codealpaca" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-sahil280114-codealpaca" tools: ["huybery-awesome-code-llm", "sahil280114-codealpaca"] --- # Awesome-Code-LLM vs codealpaca *GraphCanon updated Aug 6, 2026* ## Verdict Pick Awesome-Code-LLM if awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers; pick codealpaca if a specialized LLaMA model for generating code from instructions, leveraging 20K fine-tuning data inspired by the Self-Instruct paper. [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) reports 1.3k GitHub stars, 74 forks, and 4 open issues, last pushed Dec 10, 2024. [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 [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [codealpaca's repository](https://github.com/sahil280114/codealpaca). | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [codealpaca](/tools/sahil280114-codealpaca.md) | | --- | --- | --- | | Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | An instruction-following LLaMA model for code generation. | | Stars | 1,291 | 1,514 | | Forks | 74 | 113 | | Open issues | 4 | 17 | | Language | - | Python | | Adopt for | Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. | A specialized LLaMA model for generating code from instructions, leveraging 20K fine-tuning data inspired by the Self-Instruct paper. | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | 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._ | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [codealpaca](/tools/sahil280114-codealpaca.md) | | --- | --- | --- | | Days since push | 604d | 1180d | | Open issues (now) | 4 | 17 | | Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/sahil280114-codealpaca/trust.md) | ## Decision facts: Awesome-Code-LLM - **Requirements:** No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs. - **Adopt for:** Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. - **License detail:** MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. ## 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 Awesome-Code-LLM if… - License: Awesome-Code-LLM is MIT, codealpaca is Apache-2.0. - Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.. - Tags unique to Awesome-Code-LLM: awesome, large language models. - Also covers Evaluation & Observability. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### Choose codealpaca if… - License: codealpaca is Apache-2.0, Awesome-Code-LLM is MIT. - Requirements: Model weights are not available in this repository due to licensing restrictions.. - Tags unique to codealpaca: 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 Awesome-Code-LLM - When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. - If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. - In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ## 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 Awesome-Code-LLM and codealpaca? Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. codealpaca: An instruction-following LLaMA model for code generation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Code-LLM over codealpaca? Choose Awesome-Code-LLM over codealpaca when License: Awesome-Code-LLM is MIT, codealpaca is Apache-2.0; Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.; Tags unique to Awesome-Code-LLM: awesome, large language models; Also covers Evaluation & Observability; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### When should I choose codealpaca over Awesome-Code-LLM? Choose codealpaca over Awesome-Code-LLM when License: codealpaca is Apache-2.0, Awesome-Code-LLM is MIT; Requirements: Model weights are not available in this repository due to licensing restrictions.; Tags unique to codealpaca: 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 Awesome-Code-LLM? When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ### 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 Awesome-Code-LLM or codealpaca more popular on GitHub? codealpaca has more GitHub stars (1,514 vs 1,291). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Code-LLM and codealpaca open source? Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, codealpaca: Apache-2.0). ### Where can I find alternatives to Awesome-Code-LLM or codealpaca? GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [codealpaca alternatives](/tools/sahil280114-codealpaca/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/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/huybery-awesome-code-llm-vs-sahil280114-codealpaca.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Code-LLM or codealpaca? Awesome-Code-LLM: Dormant. 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 Awesome-Code-LLM and codealpaca? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [codealpaca trust report](/tools/sahil280114-codealpaca/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huybery-awesome-code-llm`](/api/graphcanon/graph?tool=huybery-awesome-code-llm) - 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/_