--- title: "LlamaFactory vs surogate" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-invergent-ai-surogate" tools: ["hiyouga-llamafactory", "invergent-ai-surogate"] --- # LlamaFactory vs surogate *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when llamaFactory is primarily Python; surogate is C++; pick surogate when surogate is primarily C++; LlamaFactory is Python. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [surogate](https://surogate.ai) has 806 stars, 6 forks, and 7 open issues, last pushed Jul 7, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [surogate's repository](https://github.com/invergent-ai/surogate). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [surogate](/tools/invergent-ai-surogate.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Training/Fine-tuning at the speed of light | | Stars | 73,157 | 806 | | Forks | 8,937 | 6 | | Open issues | 1,067 | 7 | | Language | Python | C++ | | 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 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Model Training, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [surogate](/tools/invergent-ai-surogate.md) | | --- | --- | --- | | Days since push | 0d | 4d | | Open issues (now) | 1.1k | 7 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/invergent-ai-surogate/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 LlamaFactory if… - LlamaFactory is primarily Python; surogate is C++. - Tags unique to LlamaFactory: gemma, deepseek, ai, instruction-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose surogate if… - surogate is primarily C++; LlamaFactory is Python. - Tags unique to surogate: llms, llama, deep-learning, llm. - Leaner open-issue backlog (7). ## 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 ## When NOT to use surogate - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between LlamaFactory and surogate? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. surogate: Training/Fine-tuning at the speed of light. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over surogate? Choose LlamaFactory over surogate when LlamaFactory is primarily Python; surogate is C++; Tags unique to LlamaFactory: gemma, deepseek, ai, instruction-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose surogate over LlamaFactory? Choose surogate over LlamaFactory when surogate is primarily C++; LlamaFactory is Python; Tags unique to surogate: llms, llama, deep-learning, llm; Leaner open-issue backlog (7). ### 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 ### When should I avoid surogate? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is LlamaFactory or surogate more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 806). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and surogate open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, surogate: Apache-2.0). ### Where can I find alternatives to LlamaFactory or surogate? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [surogate alternatives](/tools/invergent-ai-surogate/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [surogate markdown twin](/tools/invergent-ai-surogate/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-invergent-ai-surogate.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or surogate? LlamaFactory: Very active. surogate: 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 surogate? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [surogate trust report](/tools/invergent-ai-surogate/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/_