--- title: "self-llm vs DeepSeek-R1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-self-llm-vs-deepseek-ai-deepseek-r1" tools: ["datawhalechina-self-llm", "deepseek-ai-deepseek-r1"] --- # self-llm vs DeepSeek-R1 *GraphCanon updated Oct 10, 2026* ## Verdict Pick self-llm if self-llm provides tutorials and guides for deploying and fine-tuning open-source large language models in a Linux environment, covering setup, deployment, and fine-tuning methods for various models; pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. [self-llm](https://github.com/datawhalechina/self-llm) reports 32k GitHub stars, 3.1k forks, and 159 open issues, last pushed Sep 12, 2026. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) has 92k stars, 12k forks, and 42 open issues, last pushed Jun 27, 2025. Figures are from public GitHub metadata via [self-llm's repository](https://github.com/datawhalechina/self-llm) and [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1). | | [self-llm](/tools/datawhalechina-self-llm.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | | --- | --- | --- | | Tagline | 教程和指南，帮助用户在Linux环境下快速部署和微调开源大模型 | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | | Stars | 32,251 | 91,902 | | Forks | 3,124 | 11,656 | | Open issues | 159 | 42 | | Language | Jupyter Notebook | - | | Adopt for | self-llm provides tutorials and guides for deploying and fine-tuning open-source large language models in a Linux environment, covering setup, deployment, and fine-tuning methods for various models. | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | | Persona | - | - | | Runtime | - | - | | License | self-llm is licensed under Apache-2.0, allowing for broad use, modification, and distribution with proper attribution. | MIT | | Categories | Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [self-llm](/tools/datawhalechina-self-llm.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 6d | 469d | | Open issues (now) | 159 | 42 | | Stars delta | +529 (30d) | -118 (30d) | | Open issues delta | -5 (30d) | +9 (30d) | | Full report | [trust report](/tools/datawhalechina-self-llm/trust.md) | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | ## Decision facts: self-llm - **Requirements:** Min 8 GB RAM; A Linux environment is required for following the tutorials.; Basic knowledge of Linux and command-line operations is recommended. - **Adopt for:** self-llm provides tutorials and guides for deploying and fine-tuning open-source large language models in a Linux environment, covering setup, deployment, and fine-tuning methods for various models. - **License detail:** self-llm is licensed under Apache-2.0, allowing for broad use, modification, and distribution with proper attribution. ## Decision facts: DeepSeek-R1 - **Pricing:** freemium - The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository. - **Requirements:** Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs. - **Adopt for:** DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. ## Choose when ### Choose self-llm if… - License: self-llm is Apache-2.0, DeepSeek-R1 is MIT. - Requirements: Min 8 GB RAM; A Linux environment is required for following the tutorials.; Basic knowledge of Linux and command-line operations is recommended.. - Tags unique to self-llm: chatglm, chatglm3, gemma-2b-it, glm-4. - Also covers Inference & Serving. - Use self-llm if you are a student or researcher in China looking for detailed tutorials on deploying and fine-tuning open-source large language models in a Linux environment. ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, self-llm is Apache-2.0. - Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.. - Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.. - Tags unique to DeepSeek-R1: commercial-use, derived models, distilled models, mit license. - When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ## When NOT to use self-llm - Avoid self-llm if you are not comfortable with Linux and prefer a different operating system for your model deployment and fine-tuning. - Do not use self-llm if you are looking for a tool that focuses on proprietary models or if you require support for non-Linux environments. ## When NOT to use DeepSeek-R1 - Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. - If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ## Common questions ### What is the difference between self-llm and DeepSeek-R1? self-llm: 教程和指南，帮助用户在Linux环境下快速部署和微调开源大模型. DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. See the comparison table for live GitHub stats and shared categories. ### When should I choose self-llm over DeepSeek-R1? Choose self-llm over DeepSeek-R1 when License: self-llm is Apache-2.0, DeepSeek-R1 is MIT; Requirements: Min 8 GB RAM; A Linux environment is required for following the tutorials.; Basic knowledge of Linux and command-line operations is recommended.; Tags unique to self-llm: chatglm, chatglm3, gemma-2b-it, glm-4; Also covers Inference & Serving; Use self-llm if you are a student or researcher in China looking for detailed tutorials on deploying and fine-tuning open-source large language models in a Linux environment. ### When should I choose DeepSeek-R1 over self-llm? Choose DeepSeek-R1 over self-llm when License: DeepSeek-R1 is MIT, self-llm is Apache-2.0; Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.; Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.; Tags unique to DeepSeek-R1: commercial-use, derived models, distilled models, mit license; When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### When should I avoid self-llm? Avoid self-llm if you are not comfortable with Linux and prefer a different operating system for your model deployment and fine-tuning. Do not use self-llm if you are looking for a tool that focuses on proprietary models or if you require support for non-Linux environments. ### When should I avoid DeepSeek-R1? Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ### Is self-llm or DeepSeek-R1 more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,902 vs 32,251). Stars measure visibility, not whether either tool fits your constraints. ### Are self-llm and DeepSeek-R1 open source? Yes - both are open-source projects on GitHub (self-llm: Apache-2.0, DeepSeek-R1: MIT). ### Where can I find alternatives to self-llm or DeepSeek-R1? GraphCanon lists graph-backed alternatives at [self-llm alternatives](/tools/datawhalechina-self-llm/alternatives) and [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) ([self-llm markdown twin](/tools/datawhalechina-self-llm/alternatives.md), [DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/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/datawhalechina-self-llm-vs-deepseek-ai-deepseek-r1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, self-llm or DeepSeek-R1? self-llm: Very active. DeepSeek-R1: 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 self-llm and DeepSeek-R1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [self-llm trust report](/tools/datawhalechina-self-llm/trust); [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-self-llm`](/api/graphcanon/graph?tool=datawhalechina-self-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/_