--- title: "LlamaFactory vs DS-1000" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-xlang-ai-ds-1000" tools: ["hiyouga-llamafactory", "xlang-ai-ds-1000"] --- # LlamaFactory vs DS-1000 *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when license: LlamaFactory is Apache-2.0, DS-1000 is CC-BY-SA-4.0; pick DS-1000 when license: DS-1000 is CC-BY-SA-4.0, LlamaFactory is Apache-2.0. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [DS-1000](https://ds1000-code-gen.github.io) has 273 stars, 31 forks, and 2 open issues, last pushed Oct 30, 2024. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [DS-1000's repository](https://github.com/xlang-ai/DS-1000). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | [ICML 2023] Data and code release for the paper "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation". | | Stars | 73,157 | 273 | | Forks | 8,937 | 31 | | Open issues | 1,067 | 2 | | 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 | CC-BY-SA-4.0 | | Categories | LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 619d | | Open issues (now) | 1.1k | 2 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/xlang-ai-ds-1000/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 LlamaFactory if… - License: LlamaFactory is Apache-2.0, DS-1000 is CC-BY-SA-4.0. - 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. ### Choose DS-1000 if… - License: DS-1000 is CC-BY-SA-4.0, LlamaFactory is Apache-2.0. - Tags unique to DS-1000: benchmark, code-generation, data-science, python. - Also covers Evaluation & Observability. ## 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 ## When NOT to use DS-1000 - Last GitHub push was 619 days ago (dormant maintenance, Oct 30, 2024). Validate activity before betting a new project on DS-1000. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - 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. ## Common questions ### What is the difference between LlamaFactory and DS-1000? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. DS-1000: [ICML 2023] Data and code release for the paper "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation".. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over DS-1000? Choose LlamaFactory over DS-1000 when License: LlamaFactory is Apache-2.0, DS-1000 is CC-BY-SA-4.0; 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 choose DS-1000 over LlamaFactory? Choose DS-1000 over LlamaFactory when License: DS-1000 is CC-BY-SA-4.0, LlamaFactory is Apache-2.0; Tags unique to DS-1000: benchmark, code-generation, data-science, python; Also covers Evaluation & Observability. ### 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 ### When should I avoid DS-1000? Last GitHub push was 619 days ago (dormant maintenance, Oct 30, 2024). Validate activity before betting a new project on DS-1000. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. 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. ### Is LlamaFactory or DS-1000 more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 273). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and DS-1000 open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, DS-1000: CC-BY-SA-4.0). ### Where can I find alternatives to LlamaFactory or DS-1000? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [DS-1000 alternatives](/tools/xlang-ai-ds-1000/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [DS-1000 markdown twin](/tools/xlang-ai-ds-1000/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/hiyouga-llamafactory-vs-xlang-ai-ds-1000.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or DS-1000? LlamaFactory: Very active. DS-1000: Dormant. 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 LlamaFactory and DS-1000? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [DS-1000 trust report](/tools/xlang-ai-ds-1000/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-llamafactory`](/api/graphcanon/graph?tool=hiyouga-llamafactory) - 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/_