--- title: "awesome-llms-fine-tuning vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/curated-awesome-lists-awesome-llms-fine-tuning-vs-hiyouga-llamafactory" tools: ["curated-awesome-lists-awesome-llms-fine-tuning", "hiyouga-llamafactory"] --- # awesome-llms-fine-tuning vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-llms-fine-tuning when tags unique to awesome-llms-fine-tuning: awesome-list, deep-learning, llms, machine-learning; pick LlamaFactory when tags unique to LlamaFactory: agent, deepseek, gemma, instruction-tuning. [awesome-llms-fine-tuning](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) reports 521 GitHub stars, 77 forks, and 8 open issues, last pushed Dec 2, 2024. [LlamaFactory](https://llamafactory.readthedocs.io) has 73k stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [awesome-llms-fine-tuning's repository](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Explore a comprehensive collection of resources, tutorials, papers, tools, and best practices for fine-tuning Large Language Models (LLMs). Perfect for ML practitioners and researchers! | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 521 | 73,157 | | Forks | 77 | 8,937 | | Open issues | 8 | 1,067 | | Language | - | Python | | Adopt for | - | LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | 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) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 585d | 0d | | Open issues (now) | 8 | 1.1k | | Owner type | Organization | User | | Full report | [trust report](/tools/curated-awesome-lists-awesome-llms-fine-tuning/trust.md) | [trust report](/tools/hiyouga-llamafactory/trust.md) | ## Decision facts: LlamaFactory - **Adopt for:** LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. ## Choose when ### Choose awesome-llms-fine-tuning if… - Tags unique to awesome-llms-fine-tuning: awesome-list, deep-learning, llms, machine-learning. - Leaner open-issue backlog (8). ### Choose LlamaFactory if… - Tags unique to LlamaFactory: agent, deepseek, gemma, instruction-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (73k vs 521) - visibility, not fit. ## When NOT to use awesome-llms-fine-tuning - Last GitHub push was 586 days ago (dormant maintenance, Dec 2, 2024). Validate activity before betting a new project on awesome-llms-fine-tuning. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use LlamaFactory - When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. - If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa ## Common questions ### What is the difference between awesome-llms-fine-tuning and LlamaFactory? awesome-llms-fine-tuning: Explore a comprehensive collection of resources, tutorials, papers, tools, and best practices for fine-tuning Large Language Models (LLMs). Perfect for ML practitioners and researchers!. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-llms-fine-tuning over LlamaFactory? Choose awesome-llms-fine-tuning over LlamaFactory when Tags unique to awesome-llms-fine-tuning: awesome-list, deep-learning, llms, machine-learning; Leaner open-issue backlog (8). ### When should I choose LlamaFactory over awesome-llms-fine-tuning? Choose LlamaFactory over awesome-llms-fine-tuning when Tags unique to LlamaFactory: agent, deepseek, gemma, instruction-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 521) - visibility, not fit. ### When should I avoid awesome-llms-fine-tuning? Last GitHub push was 586 days ago (dormant maintenance, Dec 2, 2024). Validate activity before betting a new project on awesome-llms-fine-tuning. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid LlamaFactory? When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa ### Is awesome-llms-fine-tuning or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 521). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-llms-fine-tuning and LlamaFactory open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-llms-fine-tuning or LlamaFactory? GraphCanon lists graph-backed alternatives at [awesome-llms-fine-tuning alternatives](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([awesome-llms-fine-tuning markdown twin](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives.md), [LlamaFactory markdown twin](/tools/hiyouga-llamafactory/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-hiyouga-llamafactory.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 LlamaFactory? awesome-llms-fine-tuning: Dormant. LlamaFactory: Very active. 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 LlamaFactory? 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); [LlamaFactory trust report](/tools/hiyouga-llamafactory/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/_