--- title: "FineTuningLLMs vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dvgodoy-finetuningllms-vs-kaito-project-aikit" tools: ["dvgodoy-finetuningllms", "kaito-project-aikit"] --- # FineTuningLLMs vs aikit *GraphCanon updated Aug 24, 2026* ## Verdict Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. [FineTuningLLMs](https://github.com/dvgodoy/FineTuningLLMs) reports 855 GitHub stars, 116 forks, and 4 open issues, last pushed Feb 28, 2026. [aikit](https://kaito-project.github.io/aikit/) has 537 stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [FineTuningLLMs's repository](https://github.com/dvgodoy/FineTuningLLMs) and [aikit's repository](https://github.com/kaito-project/aikit). | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face' | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 855 | 537 | | Forks | 116 | 57 | | Open issues | 4 | 40 | | Language | Jupyter Notebook | Go | | Adopt for | FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 176d | 0d | | Open issues (now) | 4 | 40 | | Stars delta | +4 (30d) | +3 (30d) | | Open issues delta | 0 (30d) | -3 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/dvgodoy-finetuningllms/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) | ## Decision facts: FineTuningLLMs - **Adopt for:** FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. ## Decision facts: aikit - **Adopt for:** Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. ## Choose when ### Choose FineTuningLLMs if… - FineTuningLLMs is primarily Jupyter Notebook; aikit is Go. - Tags unique to FineTuningLLMs: bitsandbytes, hugging-face, large language models, llamacpp. - You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem ### Choose aikit if… - aikit is primarily Go; FineTuningLLMs is Jupyter Notebook. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers Inference & Serving. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ## When NOT to use FineTuningLLMs - Not interested in PyTorch; prefer TensorFlow or another framework - Seek theoretical background over practical applications ## When NOT to use aikit - - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ## Common questions ### What is the difference between FineTuningLLMs and aikit? FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. aikit: Fine-tune, build, and deploy open-source LLMs easily!. See the comparison table for live GitHub stats and shared categories. ### When should I choose FineTuningLLMs over aikit? Choose FineTuningLLMs over aikit when FineTuningLLMs is primarily Jupyter Notebook; aikit is Go; Tags unique to FineTuningLLMs: bitsandbytes, hugging-face, large language models, llamacpp; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem. ### When should I choose aikit over FineTuningLLMs? Choose aikit over FineTuningLLMs when aikit is primarily Go; FineTuningLLMs is Jupyter Notebook; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers Inference & Serving; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model. ### When should I avoid FineTuningLLMs? Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications ### When should I avoid aikit? - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ### Is FineTuningLLMs or aikit more popular on GitHub? FineTuningLLMs has more GitHub stars (855 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are FineTuningLLMs and aikit open source? Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, aikit: MIT). ### Where can I find alternatives to FineTuningLLMs or aikit? GraphCanon lists graph-backed alternatives at [FineTuningLLMs alternatives](/tools/dvgodoy-finetuningllms/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([FineTuningLLMs markdown twin](/tools/dvgodoy-finetuningllms/alternatives.md), [aikit markdown twin](/tools/kaito-project-aikit/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/dvgodoy-finetuningllms-vs-kaito-project-aikit.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FineTuningLLMs or aikit? FineTuningLLMs: Slowing. aikit: 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 FineTuningLLMs and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FineTuningLLMs trust report](/tools/dvgodoy-finetuningllms/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dvgodoy-finetuningllms`](/api/graphcanon/graph?tool=dvgodoy-finetuningllms) - 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/_