--- title: "LlamaFactory vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-wangrongsheng-awesome-llm-resources" tools: ["hiyouga-llamafactory", "wangrongsheng-awesome-llm-resources"] --- # LlamaFactory vs awesome-LLM-resources *GraphCanon updated Sep 20, 2026* ## Verdict Pick LlamaFactory if llamaFactory is a Python-based tool for efficient fine-tuning of over 100 large language models and vision-language models, supporting various methods like LoRA, QLoRA, and quantization; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. [LlamaFactory](https://llamafactory.readthedocs.io) reports 75k GitHub stars, 9.2k forks, and 1.2k open issues, last pushed Sep 14, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 9.0k stars, 993 forks, and 40 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Summary of the world's best LLM resources. | | Stars | 74,849 | 8,968 | | Forks | 9,170 | 993 | | Open issues | 1,156 | 40 | | Language | Python | - | | Adopt for | LlamaFactory is a Python-based tool for efficient fine-tuning of over 100 large language models and vision-language models, supporting various methods like LoRA, QLoRA, and quantization. | awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | LLM Frameworks, Model Training | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Days since push | 4d | 3d | | Open issues (now) | 1.2k | 40 | | Stars delta | +717 (30d) | +123 (30d) | | Open issues delta | +43 (30d) | +17 (30d) | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: LlamaFactory - **Requirements:** Min 4 GB RAM; Requires Docker; Hardware requirements vary based on the method and model size. For example, full fine-tuning of a 7B model requires 120GB of memory, while QLoRA with 2-bit can ; Installation is mandatory and can be done from source or via Docker. Additional dependencies like `metrics` and `deepspeed` are optional.; For Windows users, manual installation of PyTorch with CUDA support is required, and bitsandbytes must be installed for QLoRA support. - **Adopt for:** LlamaFactory is a Python-based tool for efficient fine-tuning of over 100 large language models and vision-language models, supporting various methods like LoRA, QLoRA, and quantization. ## Decision facts: awesome-LLM-resources - **Pricing:** freemium - The repository itself is free to use, but some linked resources may require payment or have associated costs. - **Requirements:** The repository does not specify any technical requirements for accessing its content. - **Adopt for:** awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. - **License detail:** The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### Choose LlamaFactory if… - Requirements: Min 4 GB RAM; Requires Docker; Hardware requirements vary based on the method and model size. For example, full fine-tuning of a 7B model requires 120GB of memory, while QLoRA with 2-bit can ; Installation is mandatory and can be done from source or via Docker. Additional dependencies like `metrics` and `deepspeed` are optional.; For Windows users, manual installation of PyTorch with CUDA support is required, and bitsandbytes must be installed for QLoRA support.. - Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning. - Use LlamaFactory when you need to fine-tune a wide range of large language models and vision-language models with methods like LoRA, QLoRA, and quantization. ### Choose awesome-LLM-resources if… - Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.. - Requirements: The repository does not specify any technical requirements for accessing its content.. - Tags unique to awesome-LLM-resources: awesome-list, book, course, llm. - Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving. - When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ## When NOT to use LlamaFactory - Avoid LlamaFactory if you are working with models not included in its supported list of over 100 models, as it may not provide the necessary fine-tuning capabilities. - Do not use LlamaFactory if you are working in an environment where Python or specific dependencies like PyTorch and bitsandbytes cannot be installed or are not supported. - LlamaFactory may not be suitable if you require real-time or near-real-time fine-tuning capabilities, as the setup and installation process can be complex and time-consuming. ## When NOT to use awesome-LLM-resources - If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. - When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ## Common questions ### What is the difference between LlamaFactory and awesome-LLM-resources? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over awesome-LLM-resources? Choose LlamaFactory over awesome-LLM-resources when Requirements: Min 4 GB RAM; Requires Docker; Hardware requirements vary based on the method and model size. For example, full fine-tuning of a 7B model requires 120GB of memory, while QLoRA with 2-bit can ; Installation is mandatory and can be done from source or via Docker. Additional dependencies like `metrics` and `deepspeed` are optional.; For Windows users, manual installation of PyTorch with CUDA support is required, and bitsandbytes must be installed for QLoRA support.; Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; Use LlamaFactory when you need to fine-tune a wide range of large language models and vision-language models with methods like LoRA, QLoRA, and quantization. ### When should I choose awesome-LLM-resources over LlamaFactory? Choose awesome-LLM-resources over LlamaFactory when Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, llm; Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### When should I avoid LlamaFactory? Avoid LlamaFactory if you are working with models not included in its supported list of over 100 models, as it may not provide the necessary fine-tuning capabilities. Do not use LlamaFactory if you are working in an environment where Python or specific dependencies like PyTorch and bitsandbytes cannot be installed or are not supported. LlamaFactory may not be suitable if you require real-time or near-real-time fine-tuning capabilities, as the setup and installation process can be complex and time-consuming. ### When should I avoid awesome-LLM-resources? If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ### Is LlamaFactory or awesome-LLM-resources more popular on GitHub? LlamaFactory has more GitHub stars (74,849 vs 8,968). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to LlamaFactory or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/hiyouga-llamafactory-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or awesome-LLM-resources? LlamaFactory: Very active. awesome-LLM-resources: 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 LlamaFactory and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-llamafactory`](/api/graphcanon/graph?tool=hiyouga-llamafactory) - 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/_