--- title: "FineTuningLLMs vs trl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dvgodoy-finetuningllms-vs-huggingface-trl" tools: ["dvgodoy-finetuningllms", "huggingface-trl"] --- # FineTuningLLMs vs trl *GraphCanon updated Aug 6, 2026* ## Verdict Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick trl if tRL (Train Reinforcement Learning) by Hugging Face provides specialized trainer classes designed for fine-tuning or PEFT adapter post-training on custom datasets, including support for multiple distributed training modes. [FineTuningLLMs](https://github.com/dvgodoy/FineTuningLLMs) reports 851 GitHub stars, 114 forks, and 4 open issues, last pushed Feb 28, 2026. [trl](http://hf.co/docs/trl) has 19k stars, 2.9k forks, and 250 open issues, last pushed Aug 6, 2026. Figures are from public GitHub metadata via [FineTuningLLMs's repository](https://github.com/dvgodoy/FineTuningLLMs) and [trl's repository](https://github.com/huggingface/trl). | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [trl](/tools/huggingface-trl.md) | | --- | --- | --- | | Tagline | Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face' | Train transformer language models with reinforcement learning. | | Stars | 851 | 19,016 | | Forks | 114 | 2,891 | | Open issues | 4 | 250 | | Language | Jupyter Notebook | Python | | Adopt for | FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. | TRL (Train Reinforcement Learning) by Hugging Face provides specialized trainer classes designed for fine-tuning or PEFT adapter post-training on custom datasets, including support for multiple distributed training modes | | Persona | - | - | | Runtime | - | - | | License | MIT | TRL operates under the Apache-2.0 License, allowing for broad usage and modification under specific conditions including copyright preservation and license notices. | | Categories | LLM Frameworks, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [trl](/tools/huggingface-trl.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 146d | 0d | | Open issues (now) | 4 | 250 | | Owner type | User | Organization | | Full report | [trust report](/tools/dvgodoy-finetuningllms/trust.md) | [trust report](/tools/huggingface-trl/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: trl - **Requirements:** Min 8 GB RAM - **Adopt for:** TRL (Train Reinforcement Learning) by Hugging Face provides specialized trainer classes designed for fine-tuning or PEFT adapter post-training on custom datasets, including support for multiple distributed training modes - **License detail:** TRL operates under the Apache-2.0 License, allowing for broad usage and modification under specific conditions including copyright preservation and license notices. ## Choose when ### Choose FineTuningLLMs if… - FineTuningLLMs is primarily Jupyter Notebook; trl is Python. - License: FineTuningLLMs is MIT, trl is Apache-2.0. - Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face. - Also covers LLM Frameworks. - You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem ### Choose trl if… - trl is primarily Python; FineTuningLLMs is Jupyter Notebook. - License: trl is Apache-2.0, FineTuningLLMs is MIT. - Requirements: Min 8 GB RAM. - Tags unique to trl: distributed-training, reinforcement-learning, transformers. - You need to fine-tune transformer language models with reinforcement learning using Python. ## When NOT to use FineTuningLLMs - Not interested in PyTorch; prefer TensorFlow or another framework - Seek theoretical background over practical applications ## When NOT to use trl - If your task does not involve transformer language models or if you do not plan to use reinforcement learning for model fine-tuning. - When strict control over training parameters is less critical and a more streamlined framework suffices. - Your project's dataset size and computational requirements don't necessitate sophisticated distributed training mechanisms like DDP, DeepSpeed ZeRO, or FSDP. ## Common questions ### What is the difference between FineTuningLLMs and trl? FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. trl: Train transformer language models with reinforcement learning.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FineTuningLLMs over trl? Choose FineTuningLLMs over trl when FineTuningLLMs is primarily Jupyter Notebook; trl is Python; License: FineTuningLLMs is MIT, trl is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face; Also covers LLM Frameworks; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem. ### When should I choose trl over FineTuningLLMs? Choose trl over FineTuningLLMs when trl is primarily Python; FineTuningLLMs is Jupyter Notebook; License: trl is Apache-2.0, FineTuningLLMs is MIT; Requirements: Min 8 GB RAM; Tags unique to trl: distributed-training, reinforcement-learning, transformers; You need to fine-tune transformer language models with reinforcement learning using Python. ### When should I avoid FineTuningLLMs? Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications ### When should I avoid trl? If your task does not involve transformer language models or if you do not plan to use reinforcement learning for model fine-tuning. When strict control over training parameters is less critical and a more streamlined framework suffices. Your project's dataset size and computational requirements don't necessitate sophisticated distributed training mechanisms like DDP, DeepSpeed ZeRO, or FSDP. ### Is FineTuningLLMs or trl more popular on GitHub? trl has more GitHub stars (19,016 vs 851). Stars measure visibility, not whether either tool fits your constraints. ### Are FineTuningLLMs and trl open source? Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, trl: Apache-2.0). ### Where can I find alternatives to FineTuningLLMs or trl? GraphCanon lists graph-backed alternatives at [FineTuningLLMs alternatives](/tools/dvgodoy-finetuningllms/alternatives) and [trl alternatives](/tools/huggingface-trl/alternatives) ([FineTuningLLMs markdown twin](/tools/dvgodoy-finetuningllms/alternatives.md), [trl markdown twin](/tools/huggingface-trl/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-huggingface-trl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FineTuningLLMs or trl? FineTuningLLMs: Slowing. trl: 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 trl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FineTuningLLMs trust report](/tools/dvgodoy-finetuningllms/trust); [trl trust report](/tools/huggingface-trl/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/_