Open-Source AI · Fine-tuning

TRL vs Llama Cookbook

TRL vs Llama Cookbook compared for 2026 — features, license, ease of use, performance and which one to choose. Align LLMs (SFT, DPO, PPO) vs Official recipes to fine-tune Llama.

Updated regularly · curated by OpenSourceAI.tech

Choose TRL for RLHF, DPO and alignment training. Choose Llama Cookbook for fine-tuning Llama models the supported way.

TRL vs Llama Cookbook at a glance

Spec	TRL	Llama Cookbook
Category	Fine-tuning	Fine-tuning
Type	RLHF / alignment library	Recipes & scripts
License	Apache-2.0	MIT
Runs locally	Yes	Yes
Primary language	Python	Python
Ease of use	Advanced	Intermediate
Best for	RLHF, DPO and alignment training	fine-tuning Llama models the supported way
GitHub stars	18.9k	18.4k

How TRL and Llama Cookbook score

🤝 Too close to call — TRL and Llama Cookbook land within a hair (4.2 vs 4.3 / 5). Pick on fit, not on score.

Criterion	TRL	Llama Cookbook
Popularity	3.5	3.5
Maintenance	5.0	4.5
Ease of use	2.5	3.5
Privacy	5.0	5.0
License freedom	5.0	5.0

Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.

What each one is

TRL

RLHF / alignment library · Apache-2.0

TRL is Hugging Face's library for post-training and aligning language models with supervised fine-tuning, DPO and reinforcement learning methods like PPO.

SFT, DPO and PPO in one library
Integrates with PEFT and Accelerate
Maintained by Hugging Face

See the TRL page →

Llama Cookbook

Recipes & scripts · MIT

The official Meta cookbook of scripts and notebooks for fine-tuning, evaluating and deploying Llama models.

Official, maintained recipes
Covers fine-tuning to deployment
Well-documented notebooks

See the Llama Cookbook page →

Key differences

TRL is rLHF / alignment library, while Llama Cookbook is recipes & scripts. Their licenses differ (Apache-2.0 vs MIT), which matters if you ship a commercial product. TRL leans more advanced-friendly, whereas Llama Cookbook is more suited to intermediate users. In short, TRL fits RLHF, DPO and alignment training, and Llama Cookbook fits fine-tuning Llama models the supported way.

Which should you choose?

Choose TRL for RLHF, DPO and alignment training. Choose Llama Cookbook for fine-tuning Llama models the supported way.

There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.

Frequently asked questions

Is TRL or Llama Cookbook easier to use?

Llama Cookbook is generally the easier of the two to get started with, while TRL rewards more setup with more control.

Are TRL and Llama Cookbook free?

TRL is free and open source (Apache-2.0), and Llama Cookbook is free and open source (MIT). Neither charges for the core software.

Can I run TRL and Llama Cookbook locally?

TRL: yes · Llama Cookbook: yes. Both can be used without sending your data to a third-party cloud where their setup allows.

TRL vs Llama Cookbook — which should I pick in 2026?

Choose TRL for RLHF, DPO and alignment training. Choose Llama Cookbook for fine-tuning Llama models the supported way.

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