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[pypi_badge]: https://img.shields.io/pypi/v/xinfer.svg?style=for-the-badge&logo=pypi&logoColor=white&label=PyPI&color=blue
[downloads_badge]: https://img.shields.io/pepy/dt/xinfer.svg?style=for-the-badge&logo=pypi&logoColor=white&label=Downloads&color=purple
[license_badge]: https://img.shields.io/badge/License-Apache%202.0-green.svg?style=for-the-badge&logo=apache&logoColor=white
[transformers_badge]: https://img.shields.io/badge/Transformers-yellow?style=for-the-badge&logo=huggingface&logoColor=white
[timm_badge]: https://img.shields.io/badge/TIMM-limegreen?style=for-the-badge&logo=pytorch&logoColor=white
[ultralytics_badge]: https://img.shields.io/badge/Ultralytics-red?style=for-the-badge&logo=udacity&logoColor=white
[vllm_badge]: https://img.shields.io/badge/vLLM-purple?style=for-the-badge&logo=v&logoColor=white
[ollama_badge]: https://img.shields.io/badge/Ollama-darkgreen?style=for-the-badge&logo=llama&logoColor=white
[transformers_badge]: https://img.shields.io/github/stars/huggingface/transformers?style=for-the-badge&logo=huggingface&label=Transformers%20⭐&color=yellow
[timm_badge]: https://img.shields.io/github/stars/huggingface/pytorch-image-models?style=for-the-badge&logo=pytorch&label=TIMM%20⭐&color=limegreen
[ultralytics_badge]: https://img.shields.io/github/stars/ultralytics/ultralytics?style=for-the-badge&logo=udacity&label=Ultralytics%20⭐&color=red
[vllm_badge]: https://img.shields.io/github/stars/vllm-project/vllm?style=for-the-badge&logo=v&label=vLLM%20⭐&color=purple
[ollama_badge]: https://img.shields.io/github/stars/ollama/ollama?style=for-the-badge&logo=llama&label=Ollama%20⭐&color=darkgreen
[colab_badge]: https://img.shields.io/badge/Open%20In-Colab-blue?style=for-the-badge&logo=google-colab
[kaggle_badge]: https://img.shields.io/badge/Open%20In-Kaggle-blue?style=for-the-badge&logo=kaggle
[back_to_top_badge]: https://img.shields.io/badge/Back_to_Top-↑-blue?style=for-the-badge
Expand Down Expand Up @@ -61,11 +61,11 @@ x.infer is a simple library that allows you to run inference with any computer v

Out of the box, x.infer supports the following frameworks:

[![Transformers](https://img.shields.io/badge/Transformers-yellow?style=for-the-badge&logo=huggingface&logoColor=white)](https://github.com/huggingface/transformers)
[![TIMM](https://img.shields.io/badge/TIMM-limegreen?style=for-the-badge&logo=pytorch&logoColor=white)](https://github.com/huggingface/pytorch-image-models)
[![Ultralytics](https://img.shields.io/badge/Ultralytics-red?style=for-the-badge&logo=udacity&logoColor=white)](https://github.com/ultralytics/ultralytics)
[![vLLM](https://img.shields.io/badge/vLLM-purple?style=for-the-badge&logo=v&logoColor=white)](https://github.com/vllm-project/vllm)
[![Ollama](https://img.shields.io/badge/Ollama-darkgreen?style=for-the-badge&logo=Ollama&logoColor=white)](https://github.com/ollama/ollama)
[![Transformers][transformers_badge]](https://github.com/huggingface/transformers)
[![TIMM][timm_badge]](https://github.com/huggingface/pytorch-image-models)
[![Ultralytics][ultralytics_badge]](https://github.com/ultralytics/ultralytics)
[![vLLM][vllm_badge]](https://github.com/vllm-project/vllm)
[![Ollama][ollama_badge]](https://github.com/ollama/ollama)

Combined, x.infer supports over 1000+ models from all the above frameworks.

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Available Models
┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Implementation ┃ Model ID ┃ Input --> Output ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━��━┩
│ timm │ timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k │ image --> categories │
│ timm │ timm/eva02_large_patch14_448.mim_m38m_ft_in1k │ image --> categories │
│ timm │ timm/eva02_large_patch14_448.mim_in22k_ft_in22k_in1k │ image --> categories │
Expand All @@ -181,7 +181,7 @@ xinfer.list_models()
│ ultralytics │ ultralytics/yolov8n │ image --> boxes │
│ ... │ ... │ ... │
│ ... │ ... │ ... │
└────────────────┴───────────────────────────────────────────────────────┴──────────────────────┘
└────────────────┴─────────────────────────────────────────────────────────────┴──────────────────────┘
```

If you're running in a Juypter Notebook environment, you can specify `interactive=True` to list and search supported models interactively.
Expand Down Expand Up @@ -315,7 +315,8 @@ See an example implementation of the Molmo model [here](https://github.com/dnth/


<details>
<summary><a href="https://github.com/huggingface/transformers"><img src="https://img.shields.io/badge/Transformers-yellow?style=for-the-badge&logo=huggingface&logoColor=white" alt="Transformers"></a></summary>
<summary><a href="https://github.com/huggingface/transformers">
<img src="https://img.shields.io/github/stars/huggingface/transformers?style=for-the-badge&logo=huggingface&label=Transformers%20⭐&color=yellow" alt="Transformers"></a></summary>

<!DOCTYPE html>
<html lang="en">
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</details>

<details>
<summary><a href="https://github.com/huggingface/pytorch-image-models"><img src="https://img.shields.io/badge/TIMM-green?style=for-the-badge&logo=pytorch&logoColor=white" alt="TIMM"></a></summary>
<summary><a href="https://github.com/huggingface/pytorch-image-models">
<img src="https://img.shields.io/github/stars/huggingface/pytorch-image-models?style=for-the-badge&logo=pytorch&label=TIMM%20⭐&color=limegreen" alt="TIMM"></a></summary>

All models from [TIMM](https://github.com/huggingface/pytorch-image-models) fine-tuned for ImageNet 1k are supported.

Expand All @@ -393,7 +395,8 @@ model = xinfer.create_model(model)
</details>

<details>
<summary><a href="https://github.com/ultralytics/ultralytics"><img src="https://img.shields.io/badge/Ultralytics-red?style=for-the-badge&logo=udacity&logoColor=white" alt="Ultralytics"></a></summary>
<summary><a href="https://github.com/ultralytics/ultralytics">
<img src="https://img.shields.io/github/stars/ultralytics/ultralytics?style=for-the-badge&logo=udacity&label=Ultralytics%20⭐&color=red" alt="Ultralytics"></a></summary>

<table>
<thead>
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</details>

<details>
<summary><a href="https://github.com/vllm-project/vllm"><img src="https://img.shields.io/badge/vLLM-purple?style=for-the-badge&logo=v&logoColor=white" alt="vLLM"></a></summary>
<summary><a href="https://github.com/vllm-project/vllm">
<img src="https://img.shields.io/github/stars/vllm-project/vllm?style=for-the-badge&logo=v&label=vLLM%20⭐&color=purple" alt="vLLM"></a></summary>

<table>
<thead>
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</details>

<details>
<summary><a href="https://github.com/ollama/ollama"><img src="https://img.shields.io/badge/Ollama-darkgreen?style=for-the-badge&logo=Ollama&logoColor=white" alt="Ollama"></a></summary>
<summary><a href="https://github.com/ollama/ollama">
<img src="https://img.shields.io/github/stars/ollama/ollama?style=for-the-badge&logo=llama&label=Ollama%20⭐&color=darkgreen" alt="Ollama"></a></summary>

To use Ollama models, you'll need to install the Ollama on your machine. See [Ollama Installation Guide](https://ollama.com/download) for more details.

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