--- title: "Hands-On-Large-Language-Models vs OpenCoder-llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-opencoder-llm-opencoder-llm" tools: ["handsonllm-hands-on-large-language-models", "opencoder-llm-opencoder-llm"] --- # Hands-On-Large-Language-Models vs OpenCoder-llm *GraphCanon updated Aug 16, 2026* ## Verdict Pick Hands-On-Large-Language-Models if consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples; pick OpenCoder-llm if openCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines. [Hands-On-Large-Language-Models](https://www.llm-book.com/) reports 28k GitHub stars, 6.5k forks, and 38 open issues, last pushed Apr 24, 2026. [OpenCoder-llm](https://opencoder-llm.github.io/) has 2.1k stars, 125 forks, and 11 open issues, last pushed Dec 8, 2024. Figures are from public GitHub metadata via [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) and [OpenCoder-llm's repository](https://github.com/OpenCoder-llm/OpenCoder-llm). | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [OpenCoder-llm](/tools/opencoder-llm-opencoder-llm.md) | | --- | --- | --- | | Tagline | Official code repo for the O'Reilly Book - 'Hands-On Large Language Models' | The Open Cookbook for Top-Tier Code Large Language Models | | Stars | 28,252 | 2,103 | | Forks | 6,531 | 125 | | Open issues | 38 | 11 | | Language | Jupyter Notebook | Python | | Adopt for | Consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples. | OpenCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | MIT | | Categories | LLM Frameworks, Model Training | Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [OpenCoder-llm](/tools/opencoder-llm-opencoder-llm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 114d | 604d | | Open issues (now) | 38 | 11 | | Stars delta | +642 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/opencoder-llm-opencoder-llm/trust.md) | ## Decision facts: Hands-On-Large-Language-Models - **Pricing:** freemium - The repository is free and open under the Apache-2.0 license. - **Requirements:** - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial. - **Adopt for:** Consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples. - **License detail:** Apache-2.0 License ## Decision facts: OpenCoder-llm - **Adopt for:** OpenCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines. ## Choose when ### Choose Hands-On-Large-Language-Models if… - Hands-On-Large-Language-Models is primarily Jupyter Notebook; OpenCoder-llm is Python. - License: Hands-On-Large-Language-Models is Apache-2.0, OpenCoder-llm is MIT. - Pricing: The repository is free and open under the Apache-2.0 license.. - Requirements: - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial.. - Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llm, llms. - - You are focusing on practical implementation aspects detailed in a structured format as outlined by O'Reilly's authoritative book. ### Choose OpenCoder-llm if… - OpenCoder-llm is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook. - License: OpenCoder-llm is MIT, Hands-On-Large-Language-Models is Apache-2.0. - Tags unique to OpenCoder-llm: code generation, data filtering, dataset, evaluation-framework. - Also covers Data & Retrieval, Evaluation & Observability. - When you need access to both English and Chinese language support in your code generation tasks. ## When NOT to use Hands-On-Large-Language-Models - - If you need real-time model evaluation tools rather than educational materials, as this repository primarily provides code for understanding and implementing concepts covered in a book. - - You are seeking proprietary or more specialized frameworks that go beyond the examples provided in an educational context to meet specific, advanced use-case needs. ## When NOT to use OpenCoder-llm - If your primary focus is on natural language processing tasks that do not involve code generation or require languages other than English or Chinese. - For scenarios where the availability of intermediate checkpoints during pretraining stages does not add value to your development process. - If you are working with datasets that already provide synthetic annealing data, and additional resources for this type of data are unnecessary. - When a tool without an open-source data cleaning pipeline is sufficient for your code generation tasks. ## Common questions ### What is the difference between Hands-On-Large-Language-Models and OpenCoder-llm? Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'. OpenCoder-llm: The Open Cookbook for Top-Tier Code Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Hands-On-Large-Language-Models over OpenCoder-llm? Choose Hands-On-Large-Language-Models over OpenCoder-llm when Hands-On-Large-Language-Models is primarily Jupyter Notebook; OpenCoder-llm is Python; License: Hands-On-Large-Language-Models is Apache-2.0, OpenCoder-llm is MIT; Pricing: The repository is free and open under the Apache-2.0 license.; Requirements: - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial.; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llm, llms; - You are focusing on practical implementation aspects detailed in a structured format as outlined by O'Reilly's authoritative book. ### When should I choose OpenCoder-llm over Hands-On-Large-Language-Models? Choose OpenCoder-llm over Hands-On-Large-Language-Models when OpenCoder-llm is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook; License: OpenCoder-llm is MIT, Hands-On-Large-Language-Models is Apache-2.0; Tags unique to OpenCoder-llm: code generation, data filtering, dataset, evaluation-framework; Also covers Data & Retrieval, Evaluation & Observability; When you need access to both English and Chinese language support in your code generation tasks. ### When should I avoid Hands-On-Large-Language-Models? - If you need real-time model evaluation tools rather than educational materials, as this repository primarily provides code for understanding and implementing concepts covered in a book. - You are seeking proprietary or more specialized frameworks that go beyond the examples provided in an educational context to meet specific, advanced use-case needs. ### When should I avoid OpenCoder-llm? If your primary focus is on natural language processing tasks that do not involve code generation or require languages other than English or Chinese. For scenarios where the availability of intermediate checkpoints during pretraining stages does not add value to your development process. If you are working with datasets that already provide synthetic annealing data, and additional resources for this type of data are unnecessary. When a tool without an open-source data cleaning pipeline is sufficient for your code generation tasks. ### Is Hands-On-Large-Language-Models or OpenCoder-llm more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (28,252 vs 2,103). Stars measure visibility, not whether either tool fits your constraints. ### Are Hands-On-Large-Language-Models and OpenCoder-llm open source? Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, OpenCoder-llm: MIT). ### Where can I find alternatives to Hands-On-Large-Language-Models or OpenCoder-llm? GraphCanon lists graph-backed alternatives at [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) and [OpenCoder-llm alternatives](/tools/opencoder-llm-opencoder-llm/alternatives) ([Hands-On-Large-Language-Models markdown twin](/tools/handsonllm-hands-on-large-language-models/alternatives.md), [OpenCoder-llm markdown twin](/tools/opencoder-llm-opencoder-llm/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/handsonllm-hands-on-large-language-models-vs-opencoder-llm-opencoder-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Hands-On-Large-Language-Models or OpenCoder-llm? Hands-On-Large-Language-Models: Slowing. OpenCoder-llm: 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 Hands-On-Large-Language-Models and OpenCoder-llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/trust); [OpenCoder-llm trust report](/tools/opencoder-llm-opencoder-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models`](/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models) - 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/_