--- title: "great_expectations vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fivetran-great-expectations-vs-hiyouga-llamafactory" tools: ["fivetran-great-expectations", "hiyouga-llamafactory"] --- # great_expectations vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick great_expectations when tags unique to great_expectations: data-science, data-engineering, data-unit-tests, data-profiling; pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai. [great_expectations](https://docs.greatexpectations.io/) reports 12k GitHub stars, 1.8k forks, and 46 open issues, last pushed Jul 10, 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 [great_expectations's repository](https://github.com/fivetran/great_expectations) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [great_expectations](/tools/fivetran-great-expectations.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Always know what to expect from your data. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 11,635 | 73,157 | | Forks | 1,778 | 8,937 | | Open issues | 46 | 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 | Apache-2.0 | | Categories | Vector Databases, LLM Frameworks, Model Training | Model Training, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [great_expectations](/tools/fivetran-great-expectations.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 46 | 1.1k | | Owner type | Organization | User | | Security scan | 51 low (51 low) | No lockfile | | Full report | [trust report](/tools/fivetran-great-expectations/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 great_expectations if… - Tags unique to great_expectations: data-science, data-engineering, data-unit-tests, data-profiling. - Also covers Vector Databases. - More recently updated (last pushed Jul 10, 2026). ### 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 12k) - visibility, not fit. ## When NOT to use great_expectations - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - 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 great_expectations and LlamaFactory? great_expectations: Always know what to expect from your data.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose great_expectations over LlamaFactory? Choose great_expectations over LlamaFactory when Tags unique to great_expectations: data-science, data-engineering, data-unit-tests, data-profiling; Also covers Vector Databases; More recently updated (last pushed Jul 10, 2026). ### When should I choose LlamaFactory over great_expectations? Choose LlamaFactory over great_expectations 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 12k) - visibility, not fit. ### When should I avoid great_expectations? Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. 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 great_expectations or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 11,635). Stars measure visibility, not whether either tool fits your constraints. ### Are great_expectations and LlamaFactory open source? Yes - both are open-source projects on GitHub (great_expectations: Apache-2.0, LlamaFactory: Apache-2.0). ### Where can I find alternatives to great_expectations or LlamaFactory? GraphCanon lists graph-backed alternatives at [great_expectations alternatives](/tools/fivetran-great-expectations/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([great_expectations markdown twin](/tools/fivetran-great-expectations/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/fivetran-great-expectations-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, great_expectations or LlamaFactory? great_expectations: Very 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 great_expectations and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [great_expectations trust report](/tools/fivetran-great-expectations/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fivetran-great-expectations`](/api/graphcanon/graph?tool=fivetran-great-expectations) - 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/_