--- title: "Made-With-ML vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-hiyouga-llamafactory" tools: ["gokumohandas-made-with-ml", "hiyouga-llamafactory"] --- # Made-With-ML vs LlamaFactory *GraphCanon updated Jul 15, 2026* ## Verdict Pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; LlamaFactory is Python; pick LlamaFactory when llamaFactory is primarily Python; Made-With-ML is Jupyter Notebook. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. [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 [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Learn how to develop, deploy and iterate on production-grade ML applications. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 48,703 | 73,157 | | Forks | 7,661 | 8,937 | | Open issues | 27 | 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 | AI Agents, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 132d | 0d | | Open issues (now) | 27 | 1.1k | | Full report | [trust report](/tools/gokumohandas-made-with-ml/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 Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; LlamaFactory is Python. - License: Made-With-ML is MIT, LlamaFactory is Apache-2.0. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning. - Also covers AI Agents. ### Choose LlamaFactory if… - LlamaFactory is primarily Python; Made-With-ML is Jupyter Notebook. - License: LlamaFactory is Apache-2.0, Made-With-ML 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 Made-With-ML - Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - 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. ## 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 Made-With-ML and LlamaFactory? Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over LlamaFactory? Choose Made-With-ML over LlamaFactory when Made-With-ML is primarily Jupyter Notebook; LlamaFactory is Python; License: Made-With-ML is MIT, LlamaFactory is Apache-2.0; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers AI Agents. ### When should I choose LlamaFactory over Made-With-ML? Choose LlamaFactory over Made-With-ML when LlamaFactory is primarily Python; Made-With-ML is Jupyter Notebook; License: LlamaFactory is Apache-2.0, Made-With-ML 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 Made-With-ML? Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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. ### 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 Made-With-ML or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 48,703). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and LlamaFactory open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, LlamaFactory: Apache-2.0). ### Where can I find alternatives to Made-With-ML or LlamaFactory? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/gokumohandas-made-with-ml-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Made-With-ML or LlamaFactory? Made-With-ML: Slowing. 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 Made-With-ML and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - 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/_