--- title: "LlamaFactory vs HRM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-sapientinc-hrm" tools: ["hiyouga-llamafactory", "sapientinc-hrm"] --- # LlamaFactory vs HRM *GraphCanon updated Aug 17, 2026* ## Verdict Pick LlamaFactory if 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; pick HRM if hierarchical Reasoning Model (HRM) is a brain-inspired AI tool centered on deep learning and hierarchical reasoning. It necessitates CUDA 12.6 for its PyTorch-based environment setup, making it uniquely. [LlamaFactory](https://llamafactory.readthedocs.io) reports 74k GitHub stars, 9.1k forks, and 1.1k open issues, last pushed Aug 13, 2026. [HRM](https://sapient.inc) has 13k stars, 1.8k forks, and 75 open issues, last pushed Mar 31, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [HRM's repository](https://github.com/sapientinc/HRM). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [HRM](/tools/sapientinc-hrm.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Hierarchical Reasoning Model Official Release | | Stars | 74,132 | 12,613 | | Forks | 9,071 | 1,825 | | Open issues | 1,113 | 75 | | Language | Python | 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. | Hierarchical Reasoning Model (HRM) is a brain-inspired AI tool centered on deep learning and hierarchical reasoning. It necessitates CUDA 12.6 for its PyTorch-based environment setup, making it uniquely optimized for GPU | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [HRM](/tools/sapientinc-hrm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 138d | | Open issues (now) | 1.1k | 75 | | Stars delta | +803 (30d) | +17 (30d) | | Open issues delta | +39 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/sapientinc-hrm/trust.md) | ## Shared compatibility - **Python**: [LlamaFactory](/tools/hiyouga-llamafactory.md) - Python runtime; [HRM](/tools/sapientinc-hrm.md) - Python runtime ## 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. ## Decision facts: HRM - **Adopt for:** Hierarchical Reasoning Model (HRM) is a brain-inspired AI tool centered on deep learning and hierarchical reasoning. It necessitates CUDA 12.6 for its PyTorch-based environment setup, making it uniquely optimized for GPU ## Choose when ### Choose LlamaFactory if… - Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (74k vs 13k) - visibility, not fit. ### Choose HRM if… - Tags unique to HRM: brain-inspired-ai, deep-learning, reasoning. - Consider HRM when you need to leverage a highly specific GPU version (CUDA 12.6) which can potentially offer the latest in computational capabilities tailored for deep learning tasks. - Leaner open-issue backlog (75). ## 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 HRM - Avoid using HRM if you face limitations or challenges in accessing CUDA 12.6 specifically, as the model is tightly coupled with this version of CUDA and other versions will not be compatible. - Do not use HRM if your project does not benefit from hierarchical reasoning models; its specialized architecture could represent an unnecessary complexity. ## Common questions ### What is the difference between LlamaFactory and HRM? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. HRM: Hierarchical Reasoning Model Official Release. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over HRM? Choose LlamaFactory over HRM when Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (74k vs 13k) - visibility, not fit. ### When should I choose HRM over LlamaFactory? Choose HRM over LlamaFactory when Tags unique to HRM: brain-inspired-ai, deep-learning, reasoning; Consider HRM when you need to leverage a highly specific GPU version (CUDA 12.6) which can potentially offer the latest in computational capabilities tailored for deep learning tasks; Leaner open-issue backlog (75). ### 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 HRM? Avoid using HRM if you face limitations or challenges in accessing CUDA 12.6 specifically, as the model is tightly coupled with this version of CUDA and other versions will not be compatible. Do not use HRM if your project does not benefit from hierarchical reasoning models; its specialized architecture could represent an unnecessary complexity. ### Is LlamaFactory or HRM more popular on GitHub? LlamaFactory has more GitHub stars (74,132 vs 12,613). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and HRM open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, HRM: Apache-2.0). ### Where can I find alternatives to LlamaFactory or HRM? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [HRM alternatives](/tools/sapientinc-hrm/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [HRM markdown twin](/tools/sapientinc-hrm/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-sapientinc-hrm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or HRM? LlamaFactory: Very active. HRM: Slowing. 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 HRM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [HRM trust report](/tools/sapientinc-hrm/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/_