--- title: "awesome-llms-fine-tuning vs WizardLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/curated-awesome-lists-awesome-llms-fine-tuning-vs-nlpxucan-wizardlm" tools: ["curated-awesome-lists-awesome-llms-fine-tuning", "nlpxucan-wizardlm"] --- # awesome-llms-fine-tuning vs WizardLM *GraphCanon updated Aug 24, 2026* ## Verdict Pick awesome-llms-fine-tuning if a curated list for LLM fine-tuning resources including tutorials, papers, and tools; pick WizardLM if wizardLM powers language models like WizardCoder and WizardMath to excel in complex instruction handling. [awesome-llms-fine-tuning](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) reports 525 GitHub stars, 79 forks, and 10 open issues, last pushed Dec 2, 2024. [WizardLM](https://github.com/nlpxucan/WizardLM) has 9.5k stars, 749 forks, and 169 open issues, last pushed Jun 7, 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 [WizardLM's repository](https://github.com/nlpxucan/WizardLM). | | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [WizardLM](/tools/nlpxucan-wizardlm.md) | | --- | --- | --- | | Tagline | A comprehensive collection of resources for fine-tuning Large Language Models. | Empowering Large Pre-Trained Language Models to Follow Complex Instructions | | Stars | 525 | 9,484 | | Forks | 79 | 749 | | Open issues | 10 | 169 | | Language | - | Python | | Adopt for | A curated list for LLM fine-tuning resources including tutorials, papers, and tools. | WizardLM powers language models like WizardCoder and WizardMath to excel in complex instruction handling. | | Persona | - | - | | Runtime | - | - | | License | (unknown) - (unknown) | (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) | [WizardLM](/tools/nlpxucan-wizardlm.md) | | --- | --- | --- | | Days since push | 629d | 424d | | Open issues (now) | 10 | 169 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | +1 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/curated-awesome-lists-awesome-llms-fine-tuning/trust.md) | [trust report](/tools/nlpxucan-wizardlm/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: WizardLM - **Adopt for:** WizardLM powers language models like WizardCoder and WizardMath to excel in complex instruction handling. - **License detail:** (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 - Leaner open-issue backlog (10). ### Choose WizardLM if… - Tags unique to WizardLM: instruction-following, wizardcoder, wizardmath. - When advanced coding tasks need precise solutions, surpassing GPT-3.5-Turbo and Gemini Pro - More GitHub stars (9.5k vs 525) - visibility, not fit. ## 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 WizardLM - If real-time updates are needed beyond Nov 2023, as performance is based on past benchmarks - When looking for broad language capabilities of GPT-4, which outperformance in some benchmarks ## Common questions ### What is the difference between awesome-llms-fine-tuning and WizardLM? awesome-llms-fine-tuning: A comprehensive collection of resources for fine-tuning Large Language Models.. WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-llms-fine-tuning over WizardLM? Choose awesome-llms-fine-tuning over WizardLM when Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning; Need extensive guidance on LLM-specific fine-tuning strategies; Leaner open-issue backlog (10). ### When should I choose WizardLM over awesome-llms-fine-tuning? Choose WizardLM over awesome-llms-fine-tuning when Tags unique to WizardLM: instruction-following, wizardcoder, wizardmath; When advanced coding tasks need precise solutions, surpassing GPT-3.5-Turbo and Gemini Pro; More GitHub stars (9.5k vs 525) - visibility, not fit. ### 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 WizardLM? If real-time updates are needed beyond Nov 2023, as performance is based on past benchmarks When looking for broad language capabilities of GPT-4, which outperformance in some benchmarks ### Is awesome-llms-fine-tuning or WizardLM more popular on GitHub? WizardLM has more GitHub stars (9,484 vs 525). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-llms-fine-tuning and WizardLM open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-llms-fine-tuning or WizardLM? GraphCanon lists graph-backed alternatives at [awesome-llms-fine-tuning alternatives](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives) and [WizardLM alternatives](/tools/nlpxucan-wizardlm/alternatives) ([awesome-llms-fine-tuning markdown twin](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives.md), [WizardLM markdown twin](/tools/nlpxucan-wizardlm/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-nlpxucan-wizardlm.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 WizardLM? awesome-llms-fine-tuning: Dormant. WizardLM: 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 WizardLM? 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); [WizardLM trust report](/tools/nlpxucan-wizardlm/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/_