--- title: "LLM-Finetuning vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ashishpatel26-llm-finetuning-vs-tloen-alpaca-lora" tools: ["ashishpatel26-llm-finetuning", "tloen-alpaca-lora"] --- # LLM-Finetuning vs alpaca-lora *GraphCanon updated Aug 23, 2026* ## Verdict Pick LLM-Finetuning if jupyter Notebook repository for fine-tuning large language models via PEFT and LoRA using Hugging Face Transformers; pick alpaca-lora if alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. [LLM-Finetuning](https://github.com/ashishpatel26/LLM-Finetuning) reports 3.0k GitHub stars, 771 forks, and 3 open issues, last pushed Aug 1, 2025. [alpaca-lora](https://github.com/tloen/alpaca-lora) has 19k stars, 2.2k forks, and 365 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [LLM-Finetuning's repository](https://github.com/ashishpatel26/LLM-Finetuning) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [LLM-Finetuning](/tools/ashishpatel26-llm-finetuning.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | LLM Finetuning with PEFT | Instruct-tune LLaMA on consumer hardware | | Stars | 2,979 | 18,912 | | Forks | 771 | 2,180 | | Open issues | 3 | 365 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | Jupyter Notebook repository for fine-tuning large language models via PEFT and LoRA using Hugging Face Transformers. | alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. | | Persona | - | developer harness | | Runtime | - | - | | License | - | The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM-Finetuning](/tools/ashishpatel26-llm-finetuning.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Days since push | 387d | 734d | | Open issues (now) | 3 | 365 | | Stars delta | +13 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/ashishpatel26-llm-finetuning/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Decision facts: LLM-Finetuning - **Adopt for:** Jupyter Notebook repository for fine-tuning large language models via PEFT and LoRA using Hugging Face Transformers. ## Decision facts: alpaca-lora - **Pricing:** freemium - The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply. - **Adopt for:** alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. - **License detail:** The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. - **Persona:** developer harness ## Choose when ### Choose LLM-Finetuning if… - Tags unique to LLM-Finetuning: falcon, fine-tuning, huggingface, llama2. - Need to specialize a pre-trained model with specific datasets or tasks using advanced techniques like PEFT and LoRA. - More recently updated (last pushed Aug 1, 2025). ### Choose alpaca-lora if… - Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.. - Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, lora. - Also covers Inference & Serving. - alpaca-lora ships Docker support for self-hosted deployment. - When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker. ## When NOT to use LLM-Finetuning - Looking for a framework that automates the entire fine-tuning process with minimal user interaction. - Prefer a text-generation pipeline where fine-grained control over PEFT and LoRA is not necessary. ## When NOT to use alpaca-lora - When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. - For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. ## Common questions ### What is the difference between LLM-Finetuning and alpaca-lora? LLM-Finetuning: LLM Finetuning with PEFT. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLM-Finetuning over alpaca-lora? Choose LLM-Finetuning over alpaca-lora when Tags unique to LLM-Finetuning: falcon, fine-tuning, huggingface, llama2; Need to specialize a pre-trained model with specific datasets or tasks using advanced techniques like PEFT and LoRA; More recently updated (last pushed Aug 1, 2025). ### When should I choose alpaca-lora over LLM-Finetuning? Choose alpaca-lora over LLM-Finetuning when Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.; Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, lora; Also covers Inference & Serving; alpaca-lora ships Docker support for self-hosted deployment; When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker. ### When should I avoid LLM-Finetuning? Looking for a framework that automates the entire fine-tuning process with minimal user interaction. Prefer a text-generation pipeline where fine-grained control over PEFT and LoRA is not necessary. ### When should I avoid alpaca-lora? When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. ### Is LLM-Finetuning or alpaca-lora more popular on GitHub? alpaca-lora has more GitHub stars (18,912 vs 2,979). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM-Finetuning and alpaca-lora open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLM-Finetuning or alpaca-lora? GraphCanon lists graph-backed alternatives at [LLM-Finetuning alternatives](/tools/ashishpatel26-llm-finetuning/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([LLM-Finetuning markdown twin](/tools/ashishpatel26-llm-finetuning/alternatives.md), [alpaca-lora markdown twin](/tools/tloen-alpaca-lora/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/ashishpatel26-llm-finetuning-vs-tloen-alpaca-lora.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLM-Finetuning or alpaca-lora? LLM-Finetuning: Dormant. alpaca-lora: 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 LLM-Finetuning and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Finetuning trust report](/tools/ashishpatel26-llm-finetuning/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ashishpatel26-llm-finetuning`](/api/graphcanon/graph?tool=ashishpatel26-llm-finetuning) - 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/_