---
title: "LLM-RL-Visualized vs LlamaFactory"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/changyeyu-llm-rl-visualized-vs-hiyouga-llamafactory"
tools: ["changyeyu-llm-rl-visualized", "hiyouga-llamafactory"]
---

# LLM-RL-Visualized vs LlamaFactory

*GraphCanon updated Aug 16, 2026*

## Verdict

Pick LLM-RL-Visualized if lLM-RL-Visualized offers over 100 diagrams for understanding LLM, RL algorithms, and training methods including SFT, DPO and optimization techniques; 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.

[LLM-RL-Visualized](https://book.douban.com/subject/37331056/) reports 4.8k GitHub stars, 455 forks, and 3 open issues, last pushed Jul 27, 2026. [LlamaFactory](https://llamafactory.readthedocs.io) has 74k stars, 9.1k forks, and 1.1k open issues, last pushed Aug 13, 2026. Figures are from public GitHub metadata via [LLM-RL-Visualized's repository](https://github.com/changyeyu/LLM-RL-Visualized) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory).

| | [LLM-RL-Visualized](/tools/changyeyu-llm-rl-visualized.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) |
| --- | --- | --- |
| Tagline | Provides over 100 diagrams illustrating LLM and RL algorithms | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs |
| Stars | 4,750 | 74,132 |
| Forks | 455 | 9,071 |
| Open issues | 3 | 1,113 |
| Language | Python | Python |
| Adopt for | LLM-RL-Visualized offers over 100 diagrams for understanding LLM, RL algorithms, and training methods including SFT, DPO and optimization techniques. | 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 | Other | Apache-2.0 |
| Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

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

| | [LLM-RL-Visualized](/tools/changyeyu-llm-rl-visualized.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Very active (96%) |
| Days since push | 11d | 2d |
| Open issues (now) | 3 | 1.1k |
| Stars delta | Unknown | +803 (30d) |
| Open issues delta | Unknown | +39 (30d) |
| Full report | [trust report](/tools/changyeyu-llm-rl-visualized/trust.md) | [trust report](/tools/hiyouga-llamafactory/trust.md) |

## Decision facts: LLM-RL-Visualized

- **Adopt for:** LLM-RL-Visualized offers over 100 diagrams for understanding LLM, RL algorithms, and training methods including SFT, DPO and optimization techniques.

## 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 LLM-RL-Visualized if…

- License: LLM-RL-Visualized is Other, LlamaFactory is Apache-2.0.
- Tags unique to LLM-RL-Visualized: algorithm, deep-learning, llm, machine-learning.
- When detailed visual explanations of LLM and RL algorithms are needed

### Choose LlamaFactory if…

- License: LlamaFactory is Apache-2.0, LLM-RL-Visualized is Other.
- Tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma.
- When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.

## When NOT to use LLM-RL-Visualized

- If looking for executable code or tools rather than diagrams and visual explanations alone
- For datasets or large-scale experimental setups that require more interactive coding environments

## 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 LLM-RL-Visualized and LlamaFactory?

LLM-RL-Visualized: Provides over 100 diagrams illustrating LLM and RL algorithms. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories.

### When should I choose LLM-RL-Visualized over LlamaFactory?

Choose LLM-RL-Visualized over LlamaFactory when License: LLM-RL-Visualized is Other, LlamaFactory is Apache-2.0; Tags unique to LLM-RL-Visualized: algorithm, deep-learning, llm, machine-learning; When detailed visual explanations of LLM and RL algorithms are needed.

### When should I choose LlamaFactory over LLM-RL-Visualized?

Choose LlamaFactory over LLM-RL-Visualized when License: LlamaFactory is Apache-2.0, LLM-RL-Visualized is Other; Tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.

### When should I avoid LLM-RL-Visualized?

If looking for executable code or tools rather than diagrams and visual explanations alone For datasets or large-scale experimental setups that require more interactive coding environments

### 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 LLM-RL-Visualized or LlamaFactory more popular on GitHub?

LlamaFactory has more GitHub stars (74,132 vs 4,750). Stars measure visibility, not whether either tool fits your constraints.

### Are LLM-RL-Visualized and LlamaFactory open source?

Yes - both are open-source projects on GitHub (LLM-RL-Visualized: Other, LlamaFactory: Apache-2.0).

### Where can I find alternatives to LLM-RL-Visualized or LlamaFactory?

GraphCanon lists graph-backed alternatives at [LLM-RL-Visualized alternatives](/tools/changyeyu-llm-rl-visualized/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([LLM-RL-Visualized markdown twin](/tools/changyeyu-llm-rl-visualized/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/changyeyu-llm-rl-visualized-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, LLM-RL-Visualized or LlamaFactory?

LLM-RL-Visualized: Active. 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 LLM-RL-Visualized and LlamaFactory?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-RL-Visualized trust report](/tools/changyeyu-llm-rl-visualized/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=changyeyu-llm-rl-visualized`](/api/graphcanon/graph?tool=changyeyu-llm-rl-visualized)
- 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/_