--- title: "octopack vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-octopack-vs-hiyouga-llamafactory" tools: ["bigcode-project-octopack", "hiyouga-llamafactory"] --- # octopack vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick octopack when octopack is primarily Jupyter Notebook; LlamaFactory is Python; pick LlamaFactory when llamaFactory is primarily Python; octopack is Jupyter Notebook. [octopack](https://arxiv.org/abs/2308.07124) reports 479 GitHub stars, 29 forks, and 14 open issues, last pushed Feb 5, 2025. [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 [octopack's repository](https://github.com/bigcode-project/octopack) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [octopack](/tools/bigcode-project-octopack.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | 🐙 OctoPack: Instruction Tuning Code Large Language Models | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 479 | 73,157 | | Forks | 29 | 8,937 | | Open issues | 14 | 1,067 | | Language | Jupyter Notebook | 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 | MIT | Apache-2.0 | | Categories | LLM Frameworks, Model Training, Vector Databases | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [octopack](/tools/bigcode-project-octopack.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 521d | 0d | | Open issues (now) | 14 | 1.1k | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-octopack/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 octopack if… - octopack is primarily Jupyter Notebook; LlamaFactory is Python. - License: octopack is MIT, LlamaFactory is Apache-2.0. - Tags unique to octopack: jupyter notebook. - Also covers Vector Databases. ### Choose LlamaFactory if… - LlamaFactory is primarily Python; octopack is Jupyter Notebook. - License: LlamaFactory is Apache-2.0, octopack 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 NOT to use octopack - Last GitHub push was 521 days ago (dormant maintenance, Feb 5, 2025). Validate activity before betting a new project on octopack. - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 octopack and LlamaFactory? octopack: 🐙 OctoPack: Instruction Tuning Code Large Language Models. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose octopack over LlamaFactory? Choose octopack over LlamaFactory when octopack is primarily Jupyter Notebook; LlamaFactory is Python; License: octopack is MIT, LlamaFactory is Apache-2.0; Tags unique to octopack: jupyter notebook; Also covers Vector Databases. ### When should I choose LlamaFactory over octopack? Choose LlamaFactory over octopack when LlamaFactory is primarily Python; octopack is Jupyter Notebook; License: LlamaFactory is Apache-2.0, octopack 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 avoid octopack? Last GitHub push was 521 days ago (dormant maintenance, Feb 5, 2025). Validate activity before betting a new project on octopack. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 octopack or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 479). Stars measure visibility, not whether either tool fits your constraints. ### Are octopack and LlamaFactory open source? Yes - both are open-source projects on GitHub (octopack: MIT, LlamaFactory: Apache-2.0). ### Where can I find alternatives to octopack or LlamaFactory? GraphCanon lists graph-backed alternatives at [octopack alternatives](/tools/bigcode-project-octopack/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([octopack markdown twin](/tools/bigcode-project-octopack/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/bigcode-project-octopack-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, octopack or LlamaFactory? octopack: 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 octopack and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [octopack trust report](/tools/bigcode-project-octopack/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigcode-project-octopack`](/api/graphcanon/graph?tool=bigcode-project-octopack) - 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/_