---
title: "Awesome-Federated-Learning vs LlamaFactory"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/chaoyanghe-awesome-federated-learning-vs-hiyouga-llamafactory"
tools: ["chaoyanghe-awesome-federated-learning", "hiyouga-llamafactory"]
---

# Awesome-Federated-Learning vs LlamaFactory

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick Awesome-Federated-Learning when tags unique to Awesome-Federated-Learning: communication-efficiency, continual-learning, federated-learning, computation-efficiency; pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai.

[Awesome-Federated-Learning](https://github.com/chaoyanghe/Awesome-Federated-Learning) reports 2.0k GitHub stars, 332 forks, and 3 open issues, last pushed Sep 3, 2022. [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 [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs |
| Stars | 2,015 | 73,157 |
| Forks | 332 | 8,937 |
| Open issues | 3 | 1,067 |
| 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 |
| Categories | Model Training, LLM Frameworks, Computer Vision | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 1407d | 0d |
| Open issues (now) | 3 | 1.1k |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/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 Awesome-Federated-Learning if…

- Tags unique to Awesome-Federated-Learning: communication-efficiency, continual-learning, federated-learning, computation-efficiency.
- Also covers Computer Vision.
- Leaner open-issue backlog (3).

### Choose LlamaFactory if…

- Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai.
- When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
- More GitHub stars (73k vs 2.0k) - visibility, not fit.

## When NOT to use Awesome-Federated-Learning

- Last GitHub push was 1407 days ago (dormant maintenance, Sep 3, 2022). Validate activity before betting a new project on Awesome-Federated-Learning.
- 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.

## 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 Awesome-Federated-Learning and LlamaFactory?

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-Federated-Learning over LlamaFactory?

Choose Awesome-Federated-Learning over LlamaFactory when Tags unique to Awesome-Federated-Learning: communication-efficiency, continual-learning, federated-learning, computation-efficiency; Also covers Computer Vision; Leaner open-issue backlog (3).

### When should I choose LlamaFactory over Awesome-Federated-Learning?

Choose LlamaFactory over Awesome-Federated-Learning when Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 2.0k) - visibility, not fit.

### When should I avoid Awesome-Federated-Learning?

Last GitHub push was 1407 days ago (dormant maintenance, Sep 3, 2022). Validate activity before betting a new project on Awesome-Federated-Learning. 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.

### 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 Awesome-Federated-Learning or LlamaFactory more popular on GitHub?

LlamaFactory has more GitHub stars (73,157 vs 2,015). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Federated-Learning and LlamaFactory open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Awesome-Federated-Learning or LlamaFactory?

GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/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/chaoyanghe-awesome-federated-learning-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-Federated-Learning or LlamaFactory?

Awesome-Federated-Learning: 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 Awesome-Federated-Learning and LlamaFactory?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Federated-Learning trust report](/tools/chaoyanghe-awesome-federated-learning/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=chaoyanghe-awesome-federated-learning`](/api/graphcanon/graph?tool=chaoyanghe-awesome-federated-learning)
- 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/_