--- title: "LLM-Adapters vs FineTuningLLMs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-dvgodoy-finetuningllms" tools: ["agi-edgerunners-llm-adapters", "dvgodoy-finetuningllms"] --- # LLM-Adapters vs FineTuningLLMs *GraphCanon updated Aug 24, 2026* ## Verdict Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. [LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 115 forks, and 55 open issues, last pushed Mar 10, 2024. [FineTuningLLMs](https://github.com/dvgodoy/FineTuningLLMs) has 855 stars, 116 forks, and 4 open issues, last pushed Feb 28, 2026. Figures are from public GitHub metadata via [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [FineTuningLLMs's repository](https://github.com/dvgodoy/FineTuningLLMs). | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | | --- | --- | --- | | Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs | Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face' | | Stars | 1,233 | 855 | | Forks | 115 | 116 | | Open issues | 55 | 4 | | Language | Python | Jupyter Notebook | | Adopt for | LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. | FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 896d | 176d | | Open issues (now) | 55 | 4 | | Stars delta | -1 (30d) | +4 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) | [trust report](/tools/dvgodoy-finetuningllms/trust.md) | ## Shared compatibility - **ChatGPT**: [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) - Works with ChatGPT; [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) - Works with ChatGPT ## Decision facts: LLM-Adapters - **Adopt for:** LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. ## Decision facts: FineTuningLLMs - **Adopt for:** FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. ## Choose when ### Choose LLM-Adapters if… - LLM-Adapters is primarily Python; FineTuningLLMs is Jupyter Notebook. - License: LLM-Adapters is Apache-2.0, FineTuningLLMs is MIT. - Tags unique to LLM-Adapters: adapters, parameter-efficient. - Optimizing resource usage when you need to fine-tune large language models without altering their core parameters ### Choose FineTuningLLMs if… - FineTuningLLMs is primarily Jupyter Notebook; LLM-Adapters is Python. - License: FineTuningLLMs is MIT, LLM-Adapters is Apache-2.0. - Tags unique to FineTuningLLMs: bitsandbytes, finetuning, hugging-face, llamacpp. - You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem ## When NOT to use LLM-Adapters - You require a full retraining approach that modifies all model weights, not just adapters - Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023 ## When NOT to use FineTuningLLMs - Not interested in PyTorch; prefer TensorFlow or another framework - Seek theoretical background over practical applications ## Common questions ### What is the difference between LLM-Adapters and FineTuningLLMs? LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLM-Adapters over FineTuningLLMs? Choose LLM-Adapters over FineTuningLLMs when LLM-Adapters is primarily Python; FineTuningLLMs is Jupyter Notebook; License: LLM-Adapters is Apache-2.0, FineTuningLLMs is MIT; Tags unique to LLM-Adapters: adapters, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters. ### When should I choose FineTuningLLMs over LLM-Adapters? Choose FineTuningLLMs over LLM-Adapters when FineTuningLLMs is primarily Jupyter Notebook; LLM-Adapters is Python; License: FineTuningLLMs is MIT, LLM-Adapters is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, finetuning, hugging-face, llamacpp; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem. ### When should I avoid LLM-Adapters? You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023 ### When should I avoid FineTuningLLMs? Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications ### Is LLM-Adapters or FineTuningLLMs more popular on GitHub? LLM-Adapters has more GitHub stars (1,233 vs 855). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM-Adapters and FineTuningLLMs open source? Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, FineTuningLLMs: MIT). ### Where can I find alternatives to LLM-Adapters or FineTuningLLMs? GraphCanon lists graph-backed alternatives at [LLM-Adapters alternatives](/tools/agi-edgerunners-llm-adapters/alternatives) and [FineTuningLLMs alternatives](/tools/dvgodoy-finetuningllms/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/alternatives.md), [FineTuningLLMs markdown twin](/tools/dvgodoy-finetuningllms/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/agi-edgerunners-llm-adapters-vs-dvgodoy-finetuningllms.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLM-Adapters or FineTuningLLMs? LLM-Adapters: Dormant. FineTuningLLMs: Slowing. 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-Adapters and FineTuningLLMs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Adapters trust report](/tools/agi-edgerunners-llm-adapters/trust); [FineTuningLLMs trust report](/tools/dvgodoy-finetuningllms/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters`](/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters) - 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/_