Alignment Handbook: Reproducible Post-Training Recipes for Open LLMs

What the Handbook Actually Solves

The Alignment Handbook addresses a gap that appeared after ChatGPT and the Llama releases: many open models were fine-tuned only with supervised instruction data, but papers like InstructGPT and Llama 2 showed that adding preference alignment improves helpfulness and safety. Public, reproducible recipes for that alignment were scarce. The handbook fills that gap with a series of training pipelines that cover the full post-training stack, from continued pretraining to preference optimization. It is for engineers and researchers who want to train their own chat models, not for end users of chatbots. The target audience is someone who has a base model, a dataset, and access to GPUs, and who wants a proven path to an aligned model without inventing every hyperparameter from scratch.

The Architecture: Scripts, Recipes, and YAML

The project is intentionally simple. It contains two main directories. The scripts directory holds Python scripts for training and evaluation. Four training steps are supported: continued pretraining (causal language modeling), supervised fine-tuning for chat, preference alignment with DPO, and a combined SFT plus preference alignment with ORPO. Each script supports distributed training of full model weights using DeepSpeed ZeRO-3, or parameter-efficient training with LoRA or QLoRA. The recipes directory contains one subdirectory per reproduced model, such as Zephyr-7b-beta, SmolLM, or StarChat2. Each recipe is a YAML file that captures all parameters for a single training run. This separation means that the code is generic and the specificity lives in the configuration. To train a model, you pick a recipe and run the corresponding script with that YAML. The design is close to a Makefile approach: the logic is fixed, and the recipe is the input.

Getting It Running: Installation and Reproducibility

The README gives a concrete installation path. You start by creating a Python 3.11 virtual environment with uv, then install PyTorch 2.6.0 from the CUDA 12.6 index. The version is pinned for reproducibility, which is a strong point. Next you install the package itself with uv pip install ., then Flash Attention 2.7.4.post1 with --no-build-isolation. You log in to the Hugging Face Hub with huggingface-cli login, and install Git LFS so you can push models. After that, you follow a recipe's README. For example, to replicate Zephyr-7b-beta, you go to recipes/zephyr-7b-beta and follow its instructions. The installation is not trivial; it requires a GPU with supported CUDA, and Flash Attention is a known source of build problems. The README warns that the PyTorch version is hardware-dependent and points you to the official PyTorch installation page if your setup differs.

The Recipes: From Zephyr to SmolLM3

The handbook's value is in its recipes, which are tied to real released models. The news section lists several: Zephyr 7B Beta (November 2023), Zephyr 7B Gemma (March 2024), StarChat2 15B (March 2024), Zephyr 141B (A35B) with ORPO (April 2024), SmolLM-Instruct v0.2 (August 2024), SmolLM2-Instruct (November 2024), and SmolLM3-3B (July 2025). Each recipe is a YAML configuration plus a README that explains the dataset, hyperparameters, and training order. For example, the Zephyr recipe includes the No Robots dataset of 10,000 human-written instructions. The SmolLM3 recipe is described as a full post-training recipe behind a state-of-the-art hybrid reasoning model. These recipes are not abstract examples; they are the exact configurations used to produce the released models. That is the core promise: you can reproduce a known model, not just follow a generic tutorial.

A Real Limitation: It Is Not a Library

The handbook is a collection of scripts and configs, not a pip-installable framework with a clean API. You will be editing YAML files and reading Python scripts. The README says the project is simple by design, which is a strength for transparency but a weakness for integration. There is no high-level train() function. If you want to change a loss function or add a new alignment method, you must modify the scripts. Also, the recipes are tied to Hugging Face ecosystem components: transformers, datasets, and the Hub. If your stack is not Hugging Face-centric, you will fight the tooling. Another limitation is that the handbook does not provide a one-command training pipeline; you must manually run each step (continued pretraining, SFT, DPO, etc.) in sequence, which requires understanding the order and the data formats. The README points to a separate dataset formatting guide for custom chat datasets, but that guide is not in the provided material, so you must follow the link.

Alternatives: What Else Exists and How It Differs

The most direct alternative is to use the TRL library directly, which provides the underlying training methods (SFT, DPO, ORPO) as Python classes. The Alignment Handbook is built on top of TRL, but it adds reproducible recipes and a higher-level structure. If you want full control over the training loop, you would use TRL's Trainer classes and write your own configuration. Another alternative is to use Axolotl, a popular open-source fine-tuning framework that also uses YAML configs and supports SFT, DPO, and other methods. Axolotl is more of a general-purpose tool with a larger feature set, while the Alignment Handbook is focused on Hugging Face's own recipes and models. The key difference is that Axolotl is a standalone framework, whereas the Alignment Handbook is a set of scripts that you are expected to read and modify. If you want to reproduce a specific Hugging Face model exactly, the handbook is the better choice. If you want a broader tool with more community support and more model architectures, Axolotl may be more suitable.

Maintenance and License Considerations

The repository is actively maintained; the last push was May 2026, and the news items show regular releases of new recipes. The project is not archived. However, there are no formal releases or version tags listed, so you cannot pin to a stable release. You must clone the main branch or use a specific commit. This is a maintenance cost: recipes may change as new models are added, and the installation steps may evolve. The license is Apache-2.0, which is permissive for commercial use, but note that the models and datasets referenced in the recipes have their own licenses. For example, the No Robots dataset has its own terms, and base models like Llama have their own licenses. The handbook's code is Apache-2.0, but you must check the license of any model you fine-tune and any dataset you use. The README does not provide legal advice, and neither does this review. The practical implication is that you can freely use the code, but your trained model may be subject to the base model's license.

Who Should Use It and What to Verify First

The handbook is best for a team that wants to replicate a specific aligned model on its own infrastructure, or that wants a proven starting point for custom post-training. It is less suitable for someone who wants to experiment with a novel alignment objective, because that would require writing new training logic. Before adopting it, verify that your GPU supports Flash Attention and that you can install the pinned versions of PyTorch and Flash Attention. Also, check the dataset licenses for the recipe you plan to use; some datasets are research-only. If you plan to train on your own data, read the dataset formatting guide in scripts/README.md, which is linked but not included in the provided material. Finally, note that the handbook assumes a Hugging Face account and Git LFS for pushing models, so your workflow must include those tools.

Editorial conclusion

Adopt the Alignment Handbook if you need to reproduce a known post-training pipeline (Zephyr, SmolLM, StarChat) on your own hardware or with your own dataset, and if you are comfortable with Python, PyTorch, DeepSpeed, and Hugging Face tooling. Do not use it if you want a plug-and-play GUI or if you need to train with a non-Hugging Face stack. Before you start, verify that your GPU driver and CUDA version match the pinned PyTorch 2.6.0 and Flash Attention 2.7.4, and that you have enough VRAM for either full fine-tuning with ZeRO-3 or for LoRA/QLoRA. The handbook is a recipe collection, not a library; you will be editing YAML files and reading scripts, not calling a high-level API. Its value is in the exact, tested configurations, so follow the installation steps as written or expect to debug environment issues.

Official sources

1. huggingface/alignment-handbook on GitHub
2. Issues
3. License: Apache-2.0
4. Project website
5. README