--- title: "awadb vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/awa-ai-awadb-vs-hiyouga-llamafactory" tools: ["awa-ai-awadb", "hiyouga-llamafactory"] --- # awadb vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick awadb when awadb is primarily C++; LlamaFactory is Python; pick LlamaFactory when llamaFactory is primarily Python; awadb is C++. [awadb](https://ljeagle.github.io/awadb) reports 175 GitHub stars, 16 forks, and 4 open issues, last pushed Nov 4, 2024. [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 [awadb's repository](https://github.com/awa-ai/awadb) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [awadb](/tools/awa-ai-awadb.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | AI Native database for embedding vectors | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 175 | 73,157 | | Forks | 16 | 8,937 | | Open issues | 4 | 1,067 | | Language | C++ | 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 | 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._ | | [awadb](/tools/awa-ai-awadb.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 614d | 0d | | Open issues (now) | 4 | 1.1k | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/awa-ai-awadb/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 awadb if… - awadb is primarily C++; LlamaFactory is Python. - Tags unique to awadb: ai-native, aigc, c++, chatgpt. - Also covers Vector Databases. ### Choose LlamaFactory if… - LlamaFactory is primarily Python; awadb is C++. - 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 awadb - Last GitHub push was 615 days ago (dormant maintenance, Nov 4, 2024). Validate activity before betting a new project on awadb. - 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 awadb and LlamaFactory? awadb: AI Native database for embedding vectors. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose awadb over LlamaFactory? Choose awadb over LlamaFactory when awadb is primarily C++; LlamaFactory is Python; Tags unique to awadb: ai-native, aigc, c++, chatgpt; Also covers Vector Databases. ### When should I choose LlamaFactory over awadb? Choose LlamaFactory over awadb when LlamaFactory is primarily Python; awadb is C++; 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 awadb? Last GitHub push was 615 days ago (dormant maintenance, Nov 4, 2024). Validate activity before betting a new project on awadb. 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 awadb or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 175). Stars measure visibility, not whether either tool fits your constraints. ### Are awadb and LlamaFactory open source? Yes - both are open-source projects on GitHub (awadb: Apache-2.0, LlamaFactory: Apache-2.0). ### Where can I find alternatives to awadb or LlamaFactory? GraphCanon lists graph-backed alternatives at [awadb alternatives](/tools/awa-ai-awadb/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([awadb markdown twin](/tools/awa-ai-awadb/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/awa-ai-awadb-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awadb or LlamaFactory? awadb: 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 awadb and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awadb trust report](/tools/awa-ai-awadb/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=awa-ai-awadb`](/api/graphcanon/graph?tool=awa-ai-awadb) - 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/_