This release is a pre-release and may not be stable for production use.
🚀 What is inference-models?
inference-models is the library to make predictions from computer vision models provided by Roboflow — designed to
be fast, reliable, and user-friendly. It offers:
- Multi-Backend Support: Run models with PyTorch, ONNX, TensorRT, or Hugging Face backends
- Automatic Model Loading: Smart model resolution and backend selection
- Minimal Dependencies: Composable extras system for installing only what you need
- Behavior-Based Interfaces: Models with similar behavior share consistent APIs; custom models can define their own
- Full Roboflow Platform Support: Run any model trained on Roboflow
Visit our documentation for more information.
🛣️ Roadmap
With release 0.19.0, we have reached the first stable release of inference-models and fully integrated
the package to inference - our main inference package, making it selectable backend for running predictions
from models.
We are still making changes to add new features and models. API should be fairly stable already, but the problems may still occur. If you encounter any issues, please report them.
💻 Installation
CPU installation:
uv pip install inference-models
# or with pip
pip install inference-models
inference-models can be installed with CUDA and TensorRT support - see Installation Guide for more options.
🏃➡️ Usage
Pretrained Models
Load and run a pretrained model:
import cv2
import supervision as sv
from inference_models import AutoModel
# Load pretrained model from Roboflow
model = AutoModel.from_pretrained("rfdetr-base")
# Run inference (works with numpy arrays or torch.Tensor)
image = cv2.imread("<path-to-your-image>")
predictions = model(image)
# Use with supervision
annotator = sv.BoxAnnotator()
annotated = annotator.annotate(image, predictions[0].to_supervision())
Your Roboflow Models
Load and run models trained on the Roboflow platform:
import cv2
import supervision as sv
from inference_models import AutoModel
# Load your custom model from Roboflow
model = AutoModel.from_pretrained(
"<your-project>/<version>",
api_key="<your-api-key>" # model access secured with API key
)
# Run inference (works with numpy arrays or torch.Tensor)
image = cv2.imread("<path-to-your-image>")
predictions = model(image)
# Use with supervision
annotator = sv.BoxAnnotator()
annotated = annotator.annotate(image, predictions[0].to_supervision())
🧠 Supported Model Architectures
- RFDetr
- SAM models family
- Vision-Language Models (Florence, PaliGemma, Qwen, SmolVLM, Moondream)
- OCR (DocTR, EasyOCR, TrOCR)
- YOLO
- and many more
For detailed model documentation, see Supported Models.
🔧 Run your local models
Load your own model implementations from a local directory - models with architectures not in the main inference-models package. This is especially valuable for production deployment of custom models.
from inference_models import AutoModel
model = AutoModel.from_pretrained(
"/path/to/my_custom_model",
allow_local_code_packages=True
)
See Load Models from Local Packages for complete details on creating custom model packages.
📄 License
The inference-models package is licensed under Apache 2.0. Individual models may have different licenses - see the Supported Models for details.
Ready to get started? Head to the Quick Overview →
1 maintainer
Release files for inference-models 0.36.0rc1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| inference_models-0.36.0rc1.tar.gz | 1.8 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| inference_models-0.36.0rc1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.9 MB
Release files / inference_models-0.36.0rc1.tar.gz
| Download URL | inference_models-0.36.0rc1.tar.gz |
|---|---|
| Size | 1.8 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
fa12d0a228b7f8415af72cbc6a7b326e80dbcb25093a19eeed1a5f5eb8167215
|
|
BLAKE2b-256 checksum How to use checksums |
31932ec3710ee283ebb4c29b5078354c0354bc1745f812b90a3349a27a871794
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 21, 2026.
Transparency logRelease files / inference_models-0.36.0rc1-py3-none-any.whl
| Download URL | inference_models-0.36.0rc1-py3-none-any.whl |
|---|---|
| Size | 2.1 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
585f213ffd76e87acf1df372dd7079e77ca6d25345e4bf058b2226d6bbb11f69
|
|
BLAKE2b-256 checksum How to use checksums |
3609a80e8ed83b84f316d7fe9dd1bcf9a747fab08fea6567fab41779cd27f6d3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 21, 2026.
Transparency log
