--- title: "TinyEngram vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autoark-tinyengram-vs-hiyouga-llamafactory" tools: ["autoark-tinyengram", "hiyouga-llamafactory"] --- # TinyEngram vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick TinyEngram when tags unique to TinyEngram: deepseek-ai, engram, llm, llm-memory; pick LlamaFactory when tags unique to LlamaFactory: agent, ai, gemma, gpt. [TinyEngram](https://github.com/AutoArk/TinyEngram) reports 736 GitHub stars, 51 forks, and 10 open issues, last pushed May 21, 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 [TinyEngram's repository](https://github.com/AutoArk/TinyEngram) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [TinyEngram](/tools/autoark-tinyengram.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Research of DeepSeek Engram Architecture based on Qwen-3 and Stable Diffusion series. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 736 | 73,157 | | Forks | 51 | 8,937 | | Open issues | 10 | 1,067 | | Language | Python | 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 | Computer Vision, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TinyEngram](/tools/autoark-tinyengram.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 51d | 0d | | Open issues (now) | 10 | 1.1k | | Owner type | Organization | User | | Full report | [trust report](/tools/autoark-tinyengram/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 TinyEngram if… - Tags unique to TinyEngram: deepseek-ai, engram, llm, llm-memory. - Also covers Computer Vision. - Leaner open-issue backlog (10). ### Choose LlamaFactory if… - Tags unique to LlamaFactory: agent, ai, gemma, gpt. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (73k vs 736) - visibility, not fit. ## When NOT to use TinyEngram - 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 TinyEngram and LlamaFactory? TinyEngram: Research of DeepSeek Engram Architecture based on Qwen-3 and Stable Diffusion series.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose TinyEngram over LlamaFactory? Choose TinyEngram over LlamaFactory when Tags unique to TinyEngram: deepseek-ai, engram, llm, llm-memory; Also covers Computer Vision; Leaner open-issue backlog (10). ### When should I choose LlamaFactory over TinyEngram? Choose LlamaFactory over TinyEngram when Tags unique to LlamaFactory: agent, ai, gemma, gpt; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 736) - visibility, not fit. ### When should I avoid TinyEngram? 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 TinyEngram or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 736). Stars measure visibility, not whether either tool fits your constraints. ### Are TinyEngram and LlamaFactory open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to TinyEngram or LlamaFactory? GraphCanon lists graph-backed alternatives at [TinyEngram alternatives](/tools/autoark-tinyengram/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([TinyEngram markdown twin](/tools/autoark-tinyengram/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/autoark-tinyengram-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, TinyEngram or LlamaFactory? TinyEngram: Steady. 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 TinyEngram and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TinyEngram trust report](/tools/autoark-tinyengram/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autoark-tinyengram`](/api/graphcanon/graph?tool=autoark-tinyengram) - 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/_