Skip to content

[v1.0] Remove HfFolder and InferenceAPI classes #3344

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 9 commits into from
Sep 11, 2025
+88 −1,036
Merged

[v1.0] Remove HfFolder and InferenceAPI classes #3344

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
113b750
Remove HfFolder
Wauplin Sep 10, 2025
0d2855c
Remove InferenceAPI
Wauplin Sep 10, 2025
da1df36
more recent gradio
Wauplin Sep 10, 2025
1fc1281
bump pytest
Wauplin Sep 10, 2025
78e4d6c
fix python 3.9?
Wauplin Sep 11, 2025
fe5651f
install gradio only on python 3.10+
Wauplin Sep 11, 2025
eafacc2
fix tests
Wauplin Sep 11, 2025
18df74c
fix tests
Wauplin Sep 11, 2025
1e4d2f0
fix
Wauplin Sep 11, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
147 changes: 28 additions & 119 deletions docs/source/de/guides/inference.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@ Inferenz ist der Prozess, bei dem ein trainiertes Modell verwendet wird, um Vorh
- [Inferenz API](https://huggingface.co/docs/api-inference/index): ein Service, der Ihnen ermöglicht, beschleunigte Inferenz auf der Infrastruktur von Hugging Face kostenlos auszuführen. Dieser Service ist eine schnelle Möglichkeit, um anzufangen, verschiedene Modelle zu testen und AI-Produkte zu prototypisieren.
- [Inferenz Endpunkte](https://huggingface.co/inference-endpoints/index): ein Produkt zur einfachen Bereitstellung von Modellen im Produktivbetrieb. Die Inferenz wird von Hugging Face in einer dedizierten, vollständig verwalteten Infrastruktur auf einem Cloud-Anbieter Ihrer Wahl durchgeführt.

Diese Dienste können mit dem [`InferenceClient`] Objekt aufgerufen werden. Dieser fungiert als Ersatz für den älteren [`InferenceApi`] Client und fügt spezielle Unterstützung für Aufgaben und das Ausführen von Inferenz hinzu, sowohl auf [Inferenz API](https://huggingface.co/docs/api-inference/index) als auch auf [Inferenz Endpunkten](https://huggingface.co/docs/inference-endpoints/index). Im Abschnitt [Legacy InferenceAPI client](#legacy-inferenceapi-client) erfahren Sie, wie Sie zum neuen Client migrieren können.

<Tip>

Expand Down Expand Up @@ -89,34 +88,34 @@ Die Authentifizierung ist NICHT zwingend erforderlich, wenn Sie die Inferenz API

Das Ziel von [`InferenceClient`] ist es, die einfachste Schnittstelle zum Ausführen von Inferenzen auf Hugging Face-Modellen bereitzustellen. Es verfügt über eine einfache API, die die gebräuchlichsten Aufgaben unterstützt. Hier ist eine Liste der derzeit unterstützten Aufgaben:

| Domäne | Aufgabe | Unterstützt | Dokumentation |
|--------|--------------------------------|--------------|------------------------------------|
| Audio | [Audio Classification](https://huggingface.co/tasks/audio-classification) | ✅ | [`~InferenceClient.audio_classification`] |
| | [Automatic Speech Recognition](https://huggingface.co/tasks/automatic-speech-recognition) | ✅ | [`~InferenceClient.automatic_speech_recognition`] |
| | [Text-to-Speech](https://huggingface.co/tasks/text-to-speech) | ✅ | [`~InferenceClient.text_to_speech`] |
| Computer Vision | [Image Classification](https://huggingface.co/tasks/image-classification) | ✅ | [`~InferenceClient.image_classification`] |
| | [Image Segmentation](https://huggingface.co/tasks/image-segmentation) | ✅ | [`~InferenceClient.image_segmentation`] |
| | [Image-to-Image](https://huggingface.co/tasks/image-to-image) | ✅ | [`~InferenceClient.image_to_image`] |
| | [Image-to-Text](https://huggingface.co/tasks/image-to-text) | ✅ | [`~InferenceClient.image_to_text`] |
| | [Object Detection](https://huggingface.co/tasks/object-detection) | ✅ | [`~InferenceClient.object_detection`] |
| | [Text-to-Image](https://huggingface.co/tasks/text-to-image) | ✅ | [`~InferenceClient.text_to_image`] |
| | [Zero-Shot-Image-Classification](https://huggingface.co/tasks/zero-shot-image-classification) | ✅ | [`~InferenceClient.zero_shot_image_classification`] |
| Multimodal | [Documentation Question Answering](https://huggingface.co/tasks/document-question-answering) | ✅ | [`~InferenceClient.document_question_answering`] |
| | [Visual Question Answering](https://huggingface.co/tasks/visual-question-answering) | ✅ | [`~InferenceClient.visual_question_answering`] |
| NLP | [Conversational](https://huggingface.co/tasks/conversational) | ✅ | [`~InferenceClient.conversational`] |
| | [Feature Extraction](https://huggingface.co/tasks/feature-extraction) | ✅ | [`~InferenceClient.feature_extraction`] |
| | [Fill Mask](https://huggingface.co/tasks/fill-mask) | ✅ | [`~InferenceClient.fill_mask`] |
| | [Question Answering](https://huggingface.co/tasks/question-answering) | ✅ | [`~InferenceClient.question_answering`] |
| | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | ✅ | [`~InferenceClient.sentence_similarity`] |
| | [Summarization](https://huggingface.co/tasks/summarization) | ✅ | [`~InferenceClient.summarization`] |
| | [Table Question Answering](https://huggingface.co/tasks/table-question-answering) | ✅ | [`~InferenceClient.table_question_answering`] |
| | [Text Classification](https://huggingface.co/tasks/text-classification) | ✅ | [`~InferenceClient.text_classification`] |
| | [Text Generation](https://huggingface.co/tasks/text-generation) | ✅ | [`~InferenceClient.text_generation`] |
| | [Token Classification](https://huggingface.co/tasks/token-classification) | ✅ | [`~InferenceClient.token_classification`] |
| | [Translation](https://huggingface.co/tasks/translation) | ✅ | [`~InferenceClient.translation`] |
| | [Zero Shot Classification](https://huggingface.co/tasks/zero-shot-classification) | ✅ | [`~InferenceClient.zero_shot_classification`] |
| Tabular | [Tabular Classification](https://huggingface.co/tasks/tabular-classification) | ✅ | [`~InferenceClient.tabular_classification`] |
| | [Tabular Regression](https://huggingface.co/tasks/tabular-regression) | ✅ | [`~InferenceClient.tabular_regression`] |
| Domäne | Aufgabe | Unterstützt | Dokumentation |
| --------------- | --------------------------------------------------------------------------------------------- | ----------- | --------------------------------------------------- |
| Audio | [Audio Classification](https://huggingface.co/tasks/audio-classification) | ✅ | [`~InferenceClient.audio_classification`] |
| | [Automatic Speech Recognition](https://huggingface.co/tasks/automatic-speech-recognition) | ✅ | [`~InferenceClient.automatic_speech_recognition`] |
| | [Text-to-Speech](https://huggingface.co/tasks/text-to-speech) | ✅ | [`~InferenceClient.text_to_speech`] |
| Computer Vision | [Image Classification](https://huggingface.co/tasks/image-classification) | ✅ | [`~InferenceClient.image_classification`] |
| | [Image Segmentation](https://huggingface.co/tasks/image-segmentation) | ✅ | [`~InferenceClient.image_segmentation`] |
| | [Image-to-Image](https://huggingface.co/tasks/image-to-image) | ✅ | [`~InferenceClient.image_to_image`] |
| | [Image-to-Text](https://huggingface.co/tasks/image-to-text) | ✅ | [`~InferenceClient.image_to_text`] |
| | [Object Detection](https://huggingface.co/tasks/object-detection) | ✅ | [`~InferenceClient.object_detection`] |
| | [Text-to-Image](https://huggingface.co/tasks/text-to-image) | ✅ | [`~InferenceClient.text_to_image`] |
| | [Zero-Shot-Image-Classification](https://huggingface.co/tasks/zero-shot-image-classification) | ✅ | [`~InferenceClient.zero_shot_image_classification`] |
| Multimodal | [Documentation Question Answering](https://huggingface.co/tasks/document-question-answering) | ✅ | [`~InferenceClient.document_question_answering`] |
| | [Visual Question Answering](https://huggingface.co/tasks/visual-question-answering) | ✅ | [`~InferenceClient.visual_question_answering`] |
| NLP | [Conversational](https://huggingface.co/tasks/conversational) | ✅ | [`~InferenceClient.conversational`] |
| | [Feature Extraction](https://huggingface.co/tasks/feature-extraction) | ✅ | [`~InferenceClient.feature_extraction`] |
| | [Fill Mask](https://huggingface.co/tasks/fill-mask) | ✅ | [`~InferenceClient.fill_mask`] |
| | [Question Answering](https://huggingface.co/tasks/question-answering) | ✅ | [`~InferenceClient.question_answering`] |
| | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | ✅ | [`~InferenceClient.sentence_similarity`] |
| | [Summarization](https://huggingface.co/tasks/summarization) | ✅ | [`~InferenceClient.summarization`] |
| | [Table Question Answering](https://huggingface.co/tasks/table-question-answering) | ✅ | [`~InferenceClient.table_question_answering`] |
| | [Text Classification](https://huggingface.co/tasks/text-classification) | ✅ | [`~InferenceClient.text_classification`] |
| | [Text Generation](https://huggingface.co/tasks/text-generation) | ✅ | [`~InferenceClient.text_generation`] |
| | [Token Classification](https://huggingface.co/tasks/token-classification) | ✅ | [`~InferenceClient.token_classification`] |
| | [Translation](https://huggingface.co/tasks/translation) | ✅ | [`~InferenceClient.translation`] |
| | [Zero Shot Classification](https://huggingface.co/tasks/zero-shot-classification) | ✅ | [`~InferenceClient.zero_shot_classification`] |
| Tabular | [Tabular Classification](https://huggingface.co/tasks/tabular-classification) | ✅ | [`~InferenceClient.tabular_classification`] |
| | [Tabular Regression](https://huggingface.co/tasks/tabular-regression) | ✅ | [`~InferenceClient.tabular_regression`] |


<Tip>
Expand Down Expand Up @@ -190,93 +189,3 @@ Einige Aufgaben erfordern binäre Eingaben, zum Beispiel bei der Arbeit mit Bild
[{'score': 0.9779096841812134, 'label': 'Blenheim spaniel'}, ...]
```

## Legacy InferenceAPI client

Der [`InferenceClient`] dient als Ersatz für den veralteten [`InferenceApi`]-Client. Er bietet spezifische Unterstützung für Aufgaben und behandelt Inferenz sowohl auf der [Inferenz API](https://huggingface.co/docs/api-inference/index) als auch auf den [Inferenz Endpunkten](https://huggingface.co/docs/inference-endpoints/index).

Hier finden Sie eine kurze Anleitung, die Ihnen hilft, von [`InferenceApi`] zu [`InferenceClient`] zu migrieren.

### Initialisierung

Ändern Sie von

```python
>>> from huggingface_hub import InferenceApi
>>> inference = InferenceApi(repo_id="bert-base-uncased", token=API_TOKEN)
```

zu

```python
>>> from huggingface_hub import InferenceClient
>>> inference = InferenceClient(model="bert-base-uncased", token=API_TOKEN)
```

### Ausführen einer bestimmten Aufgabe

Ändern Sie von

```python
>>> from huggingface_hub import InferenceApi
>>> inference = InferenceApi(repo_id="paraphrase-xlm-r-multilingual-v1", task="feature-extraction")
>>> inference(...)
```

zu

```python
>>> from huggingface_hub import InferenceClient
>>> inference = InferenceClient()
>>> inference.feature_extraction(..., model="paraphrase-xlm-r-multilingual-v1")
```

<Tip>

Dies ist der empfohlene Weg, um Ihren Code an [`InferenceClient`] anzupassen. Dadurch können Sie von den aufgabenspezifischen Methoden wie `feature_extraction` profitieren.

</Tip>

### Eigene Anfragen ausführen

Ändern Sie von

```python
>>> from huggingface_hub import InferenceApi
>>> inference = InferenceApi(repo_id="bert-base-uncased")
>>> inference(inputs="The goal of life is [MASK].")
[{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]
```
zu

```python
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> response = client.post(json={"inputs": "The goal of life is [MASK]."}, model="bert-base-uncased")
>>> response.json()
[{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]
```

### Mit Parametern ausführen

Ändern Sie von

```python
>>> from huggingface_hub import InferenceApi
>>> inference = InferenceApi(repo_id="typeform/distilbert-base-uncased-mnli")
>>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
>>> params = {"candidate_labels":["refund", "legal", "faq"]}
>>> inference(inputs, params)
{'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}
```

zu

```python
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
>>> params = {"candidate_labels":["refund", "legal", "faq"]}
>>> response = client.post(json={"inputs": inputs, "parameters": params}, model="typeform/distilbert-base-uncased-mnli")
>>> response.json()
{'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}
```
4 changes: 0 additions & 4 deletions docs/source/en/guides/inference.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,10 +11,6 @@ The `huggingface_hub` library provides a unified interface to run inference acro
2. [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index): a product to easily deploy models to production. Inference is run by Hugging Face in a dedicated, fully managed infrastructure on a cloud provider of your choice.
3. Local endpoints: you can also run inference with local inference servers like [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://ollama.com/), [vLLM](https://github.com/vllm-project/vllm), [LiteLLM](https://docs.litellm.ai/docs/simple_proxy), or [Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) by connecting the client to these local endpoints.

These services can all be called from the [`InferenceClient`] object. It acts as a replacement for the legacy
[`InferenceApi`] client, adding specific support for tasks and third-party providers.
Learn how to migrate to the new client in the [Legacy InferenceAPI client](#legacy-inferenceapi-client) section.

<Tip>

[`InferenceClient`] is a Python client making HTTP calls to our APIs. If you want to make the HTTP calls directly using
Expand Down
13 changes: 0 additions & 13 deletions docs/source/en/package_reference/inference_client.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -34,16 +34,3 @@ pip install --upgrade huggingface_hub[inference]
## InferenceTimeoutError

[[autodoc]] InferenceTimeoutError

## InferenceAPI

[`InferenceAPI`] is the legacy way to call the Inference API. The interface is more simplistic and requires knowing
the input parameters and output format for each task. It also lacks the ability to connect to other services like
Inference Endpoints or AWS SageMaker. [`InferenceAPI`] will soon be deprecated so we recommend using [`InferenceClient`]
whenever possible. Check out [this guide](../guides/inference#legacy-inferenceapi-client) to learn how to switch from
[`InferenceAPI`] to [`InferenceClient`] in your scripts.

[[autodoc]] InferenceApi
- __init__
- __call__
- all
Loading
Loading