--- title: "awesome-llms-fine-tuning vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/curated-awesome-lists-awesome-llms-fine-tuning-vs-rucaibox-llmsurvey" tools: ["curated-awesome-lists-awesome-llms-fine-tuning", "rucaibox-llmsurvey"] --- # awesome-llms-fine-tuning vs LLMSurvey *GraphCanon updated Sep 20, 2026* ## Verdict Pick awesome-llms-fine-tuning if a curated list for LLM fine-tuning resources including tutorials, papers, and tools; pick LLMSurvey if lLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. [awesome-llms-fine-tuning](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) reports 527 GitHub stars, 80 forks, and 10 open issues, last pushed Sep 4, 2026. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 930 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [awesome-llms-fine-tuning's repository](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | A comprehensive collection of resources for fine-tuning Large Language Models. | A collection of papers and resources related to Large Language Models | | Stars | 527 | 12,216 | | Forks | 80 | 930 | | Open issues | 10 | 30 | | Language | - | Python | | Adopt for | A curated list for LLM fine-tuning resources including tutorials, papers, and tools. | LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. | | Persona | - | - | | Runtime | - | - | | License | (unknown) - (unknown) | The license information for LLMSurvey is unknown. | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 14d | 555d | | Open issues (now) | 10 | 30 | | Stars delta | +2 (30d) | +11 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/curated-awesome-lists-awesome-llms-fine-tuning/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## Decision facts: awesome-llms-fine-tuning - **Adopt for:** A curated list for LLM fine-tuning resources including tutorials, papers, and tools. - **License detail:** (unknown) - (unknown) ## Decision facts: LLMSurvey - **Pricing:** freemium - The repository is free to access, but the Chinese book is available for purchase. - **Requirements:** Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book. - **Adopt for:** LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. - **License detail:** The license information for LLMSurvey is unknown. ## Choose when ### Choose awesome-llms-fine-tuning if… - Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning. - Need extensive guidance on LLM-specific fine-tuning strategies - More recently updated (last pushed Sep 4, 2026). ### Choose LLMSurvey if… - Pricing: The repository is free to access, but the Chinese book is available for purchase.. - Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.. - Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning. - Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ## When NOT to use awesome-llms-fine-tuning - Looking for real-time interactive support or direct code implementation help - Favor more specialized tools for immediate performance optimization over broad learning ## When NOT to use LLMSurvey - Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. - Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ## Common questions ### What is the difference between awesome-llms-fine-tuning and LLMSurvey? awesome-llms-fine-tuning: A comprehensive collection of resources for fine-tuning Large Language Models.. LLMSurvey: A collection of papers and resources related to Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-llms-fine-tuning over LLMSurvey? Choose awesome-llms-fine-tuning over LLMSurvey when Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning; Need extensive guidance on LLM-specific fine-tuning strategies; More recently updated (last pushed Sep 4, 2026). ### When should I choose LLMSurvey over awesome-llms-fine-tuning? Choose LLMSurvey over awesome-llms-fine-tuning when Pricing: The repository is free to access, but the Chinese book is available for purchase.; Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.; Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning; Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ### When should I avoid awesome-llms-fine-tuning? Looking for real-time interactive support or direct code implementation help Favor more specialized tools for immediate performance optimization over broad learning ### When should I avoid LLMSurvey? Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ### Is awesome-llms-fine-tuning or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,216 vs 527). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-llms-fine-tuning and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-llms-fine-tuning or LLMSurvey? GraphCanon lists graph-backed alternatives at [awesome-llms-fine-tuning alternatives](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([awesome-llms-fine-tuning markdown twin](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/curated-awesome-lists-awesome-llms-fine-tuning-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-llms-fine-tuning or LLMSurvey? awesome-llms-fine-tuning: Active. LLMSurvey: 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-llms-fine-tuning and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-llms-fine-tuning trust report](/tools/curated-awesome-lists-awesome-llms-fine-tuning/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=curated-awesome-lists-awesome-llms-fine-tuning`](/api/graphcanon/graph?tool=curated-awesome-lists-awesome-llms-fine-tuning) - 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/_