--- title: "LlamaFactory vs pallms" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-mik0w-pallms" tools: ["hiyouga-llamafactory", "mik0w-pallms"] --- # LlamaFactory vs pallms *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when license: LlamaFactory is Apache-2.0, pallms is MIT; pick pallms when license: pallms is MIT, LlamaFactory is Apache-2.0. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [pallms](https://github.com/mik0w/pallms) has 141 stars, 17 forks, and 0 open issues, last pushed Jan 13, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [pallms's repository](https://github.com/mik0w/pallms). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [pallms](/tools/mik0w-pallms.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Payloads for Attacking Large Language Models | | Stars | 73,157 | 141 | | Forks | 8,937 | 17 | | Open issues | 1,067 | 0 | | 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 | MIT | | 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) | [pallms](/tools/mik0w-pallms.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 179d | | Open issues (now) | 1.1k | 0 | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/mik0w-pallms/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… - License: LlamaFactory is Apache-2.0, pallms is MIT. - 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. ### Choose pallms if… - License: pallms is MIT, LlamaFactory is Apache-2.0. - Leaner open-issue backlog (0). ## 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 pallms - Last GitHub push was 179 days ago (slowing maintenance, Jan 13, 2026). Validate activity before betting a new project on pallms. - 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. ## Common questions ### What is the difference between LlamaFactory and pallms? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. pallms: Payloads for Attacking Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over pallms? Choose LlamaFactory over pallms when License: LlamaFactory is Apache-2.0, pallms is MIT; 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. ### When should I choose pallms over LlamaFactory? Choose pallms over LlamaFactory when License: pallms is MIT, LlamaFactory is Apache-2.0; Leaner open-issue backlog (0). ### 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 pallms? Last GitHub push was 179 days ago (slowing maintenance, Jan 13, 2026). Validate activity before betting a new project on pallms. 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. ### Is LlamaFactory or pallms more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 141). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and pallms open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, pallms: MIT). ### Where can I find alternatives to LlamaFactory or pallms? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [pallms alternatives](/tools/mik0w-pallms/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [pallms markdown twin](/tools/mik0w-pallms/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-mik0w-pallms.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or pallms? LlamaFactory: Very active. pallms: 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 pallms? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [pallms trust report](/tools/mik0w-pallms/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/_