# FlagAI: a BAAI toolkit for training, fine-tuning and serving large models

> FlagAI bundles download helpers, parallel training wrappers and Chinese-first model examples behind one Python package. It is useful if you want Aquila, AltCLIP or GLM in a single repo, and awkward if you only need a standard Transformers pipeline.

**FlagAI-Open/FlagAI** — FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model.

- Repository: https://github.com/FlagAI-Open/FlagAI
- Stars: 3,869 · Forks: 417
- Language: Python
- License: Apache-2.0
- Published: 2026-09-23 · Updated: 2026-09-23 · Language: en
- Canonical page: https://hysenlabs.com/projects/flagai-open-flagai

## The gap FlagAI fills for Chinese and bilingual model work

Most large-model tooling is organised around one library's checkpoints. FlagAI is organised around a catalogue. The README lists more than 30 models, including Aquila for language, AltCLIP and AltCLIP-m18 for image-text matching, AltDiffusion and AltDiffusion-m18 for text-to-image generation, EVA-CLIP, WuDao GLM with up to 10 billion parameters, OPT, BERT, RoBERTa, GPT2, T5 and ALM for Arabic text generation. The stated goal is training, fine-tuning and deployment of large-scale models on downstream tasks with multi-modality, and the README says the project is "particularly good at Chinese tasks" for classification, information extraction, question answering, summarization and text generation.

The audience follows from that catalogue. If you are building a Chinese-language classifier, or you need a bilingual image-text model, you would otherwise assemble checkpoints from several vendors and write your own loading code for each. FlagAI puts them behind one package and one set of example directories. If your work is English-only and already fits a single Transformers checkpoint, the catalogue is overhead rather than help.

## How the toolkit is organised: a model catalogue, a training wrapper, a prompt layer

The repository splits into flagai/ for the library code, examples/ for per-model runnable code, docs/ and doc_zh/ for tutorials, and quickstart/ for entry-level material. Each model family has its own example directory, so examples/Aquila, examples/AltCLIP, examples/AltDiffusion, examples/EVA_CLIP, examples/cpm3_finetune and others carry their own README with the commands for that model. That layout is the real interface: you pick a directory, not a config flag.

Training parallelism is the second layer. The README states that FlagAI is backed by PyTorch, DeepSpeed, Megatron-LM and BMTrain, and that this integration lets you parallelise training or testing "with fewer than ten lines of code." The Dockerfile shows what that costs in practice: it installs DeepSpeed from requirements.txt, clones NVIDIA/apex and builds it with --cpp_ext and --cuda_ext, then clones OpenBMB/BMTrain, checks out 0.2.2 and installs it. So the parallelism claim rests on a stack of separately compiled dependencies rather than pure Python.

The third layer is few-shot work. The README points to a prompt-learning toolkit in docs/TUTORIAL_7_PROMPT_LEARNING.md, and the toolkit table lists GLM_custom_pvp for customising PET templates, GLM_ptuning for p-tuning, and BMInf-generate for accelerating generation. These are narrow, GLM-shaped tools, not a general prompt framework.

## Installing FlagAI and running a first model

The package is on PyPI as flagai, and setup.py declares python_requires=">=3.8" and version="v1.8.5". The simplest install is pip:

```bash
pip install flagai
```

Be aware that this pulls the install_requires list from setup.py, which includes transformers>=4.31.0, datasets>=2.0.0, diffusers>=0.7.2, pytorch-lightning>=1.6.5, timm, safetensors and taming-transformers-rom1504==0.0.6. Torch itself is not in that list, so you supply your own build.

For the full training stack, the repository ships a separate requirements.txt that pins the parallel libraries:

```bash
pip install -r requirements.txt
```

That file contains deepspeed==0.6.5, flash-attn==1.0.2, bminf, PyYAML==5.4.1 and torch. The Dockerfile is the reference environment for this path: it starts from nvcr.io/nvidia/cuda:11.7.0-devel-ubuntu20.04, installs Python 3.9, then torch==1.13.0+cu117 with torchvision==0.14.0+cu117 and torchaudio==0.13.0 from the PyTorch cu117 index, before running requirements.txt.

Once installed, the intended first step is to pick a model directory and follow its README. The repository also ships a prepare_test.sh script at the top level, which is the test-preparation entry point rather than a model demo. The README does not document a single universal quickstart command, so expect to read the per-model README under examples/ before you run anything.

## Where FlagAI gets in your way

The dependency pins are the first problem. requirements.txt fixes deepspeed==0.6.5 and flash-attn==1.0.2, both old relative to the rest of the ecosystem, and the Dockerfile builds apex and BMTrain from source at image build time. If your cluster already runs a newer DeepSpeed for another project, you are choosing between two environments or a dependency conflict. The README does not document a supported upgrade path for these pins.

The second issue is documentation shape. The README is a catalogue with links, and the substance lives in per-model directories and docs/ tutorials. There is no single page that explains the flagai package API surface, and the README does not state which models are covered by tests versus which are examples only. The model table does mark Train, Finetune and Inference/Generate per model, which is genuinely useful, but it does not say how recently each was exercised.

Third, the release cadence is uneven. The listed releases are v1.8.5 on 2024-11-07, v1.8.4 on 2023-11-30 and v1.8.2 on 2023-10-12, while setup.py still declares version="v1.8.5". The repository is not archived, and the last push was on 2026-07-13, so code is still landing, but the tagged releases do not track that activity closely. If you need a versioned artifact with a changelog you can audit, check CHANGELOG.md and the tag you intend to pin rather than assuming the master branch and the release match.

Finally, multilingual coverage is uneven by design. AltDiffusion-m18 is described as supporting 18 languages, and ALM targets Arabic text generation, but the model table marks ALM as Train and Inference only, with no fine-tuning column filled. The README's claim of particular strength on Chinese tasks is consistent with the model list, which is heavy on GLM, CPM and Chinese title and poetry generation examples.

## FlagAI compared with a plain Transformers plus DeepSpeed setup

The obvious alternative is Hugging Face Transformers for model loading and DeepSpeed for parallelism, wired together yourself. The difference is where the integration lives. With Transformers you get one consistent API across checkpoints and a large ecosystem of trainers; you write the DeepSpeed configuration and the launch script, and you own the glue. FlagAI inverts that: it owns the glue, and you get a curated catalogue where Aquila, AltCLIP, AltDiffusion, EVA-CLIP and the GLM family already have example directories and, per the model table, defined Train/Finetune/Inference support.

The cost of the inversion is that you inherit FlagAI's version choices. Its setup.py requires transformers>=4.31.0 and its requirements.txt pins deepspeed==0.6.5, so you are not free to track either library's latest release without testing. It also means anything outside the catalogue is your problem; the README notes that the code is partially based on GLM, Transformers, timm and DeepSpeedExamples, so the underlying components are the same ones you would use directly.

The choice is therefore about catalogue breadth versus API uniformity. If you need three or four of the models FlagAI lists, especially the Chinese-oriented ones, the example directories save real setup time. If you need one model and long-term control over your dependency graph, the direct route is simpler.

## Licence and the cost of keeping FlagAI current

The package is Apache-2.0: setup.py declares license="Apache 2.0", the LICENSE file is at the repository root, and the source headers carry the Apache 2.0 notice. That is permissive for commercial use, but the repository also contains BAAI_Aquila_Model_License.pdf at the top level, which is a separate document governing Aquila model weights rather than the code. If you plan to use Aquila, read that licence on its own terms; the Apache-2.0 grant on the toolkit does not automatically extend to every checkpoint the examples download. This is a description of what the repository contains, not legal advice.

Upgrade cost is dominated by the compiled stack. Apex and BMTrain are built from source in the Dockerfile, and flash-attn is pinned. Moving to a newer CUDA or PyTorch means rebuilding those pieces, and the README does not describe a supported matrix beyond the Dockerfile's cuda:11.7.0 base with torch==1.13.0+cu117. For inference-only use, the pip install path with torch supplied by you avoids most of this. For training, budget time for the image build, not just the pip install.

## Conclusion

Adopt FlagAI if you need Aquila, AltCLIP, AltDiffusion or GLM behind one import and you accept that the pinned requirements.txt (deepspeed==0.6.5, flash-attn==1.0.2) is the environment you will be debugging against. Do not adopt it if your work is plain English Transformers fine-tuning, where the extra abstraction buys nothing. Verify your CUDA and PyTorch versions against the Dockerfile's torch==1.13.0+cu117 before you start, and check the examples/Aquila and examples/AltCLIP READMEs for the model weights you intend to use.

## FAQ

### What is FlagAI used for?

It is a toolkit for training, fine-tuning and deploying large-scale models on downstream tasks, with multi-modality. The README lists over 30 supported models, including Aquila, AltCLIP, AltDiffusion, EVA-CLIP and WuDao GLM, and states a particular focus on Chinese tasks.

### How do I install FlagAI?

Install the package with pip install flagai, which pulls the dependencies declared in setup.py and requires Python 3.8 or later. For the parallel training stack, install requirements.txt instead, which pins deepspeed==0.6.5 and flash-attn==1.0.2; the repository's Dockerfile shows the reference CUDA 11.7 and PyTorch 1.13.0 environment.

### Which models does FlagAI support?

The README lists more than 30, including Aquila, ALM, AltCLIP, AltCLIP-m18, AltDiffusion, AltDiffusion-m18, CLIP, EVA-CLIP, the CPM3 and CPM_1 families, Galactica, several GLM variants, GPT-2, OPT, BERT, RoBERTa and T5. The model table marks Train, Finetune and Inference support separately for each entry.

## Sources

- [FlagAI-Open/FlagAI on GitHub](https://github.com/FlagAI-Open/FlagAI)
- [Issues](https://github.com/FlagAI-Open/FlagAI/issues)
- [License: Apache-2.0](https://github.com/FlagAI-Open/FlagAI/blob/master/LICENSE)
- [README](https://github.com/FlagAI-Open/FlagAI/blob/master/README.md)
- [Releases](https://github.com/FlagAI-Open/FlagAI/releases)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/flagai-open-flagai