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regenerate inference api docs
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‎docs/api-inference/tasks/audio-classification.md‎

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### Recommended models
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- [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition): An emotion recognition model.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=audio-classification&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=audio-classification&sort=trending).
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### Using the API
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<curl>
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```bash
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curl https://api-inference.huggingface.co/models/<REPO_ID> \
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curl https://api-inference.huggingface.co/models/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition \
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-X POST \
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--data-binary '@sample1.flac' \
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-H "Authorization: Bearer hf_***"
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```py
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import requests
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API_URL = "https://api-inference.huggingface.co/models/<REPO_ID>"
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API_URL = "https://api-inference.huggingface.co/models/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
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headers = {"Authorization": "Bearer hf_***"}
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def query(filename):
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async function query(filename) {
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const data = fs.readFileSync(filename);
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const response = await fetch(
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"https://api-inference.huggingface.co/models/<REPO_ID>",
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"https://api-inference.huggingface.co/models/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition",
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{
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headers: {
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Authorization: "Bearer hf_***"

‎docs/api-inference/tasks/automatic-speech-recognition.md‎

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@@ -30,10 +30,9 @@ For more details about the `automatic-speech-recognition` task, check out its [d
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### Recommended models
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- [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3): A powerful ASR model by OpenAI.
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- [facebook/seamless-m4t-v2-large](https://huggingface.co/facebook/seamless-m4t-v2-large): An end-to-end model that performs ASR and Speech Translation by MetaAI.
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- [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1): Powerful speaker diarization model.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=automatic-speech-recognition&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=automatic-speech-recognition&sort=trending).
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### Using the API
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‎docs/api-inference/tasks/chat-completion.md‎

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@@ -29,6 +29,7 @@ This is a subtask of [`text-generation`](https://huggingface.co/docs/api-inferen
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#### Conversational Vision-Language Models (VLMs)
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- [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct): Powerful vision language model with great visual understanding and reasoning capabilities.
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- [microsoft/Phi-3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct): Strong image-text-to-text model.
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### Using the API
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<curl>
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```bash
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curl 'https://api-inference.huggingface.co/models/microsoft/Phi-3.5-vision-instruct/v1/chat/completions' \
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curl 'https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-11B-Vision-Instruct/v1/chat/completions' \
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-H "Authorization: Bearer hf_***" \
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-H 'Content-Type: application/json' \
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-d '{
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"model": "microsoft/Phi-3.5-vision-instruct",
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"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
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"messages": [
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{
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"role": "user",
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image_url = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
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for message in client.chat_completion(
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model="microsoft/Phi-3.5-vision-instruct",
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model="meta-llama/Llama-3.2-11B-Vision-Instruct",
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messages=[
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{
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"role": "user",
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const imageUrl = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg";
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for await (const chunk of inference.chatCompletionStream({
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model: "microsoft/Phi-3.5-vision-instruct",
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model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
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messages: [
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{
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"role": "user",

‎docs/api-inference/tasks/feature-extraction.md‎

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- [thenlper/gte-large](https://huggingface.co/thenlper/gte-large): A powerful feature extraction model for natural language processing tasks.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=feature-extraction&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=feature-extraction&sort=trending).
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### Using the API
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‎docs/api-inference/tasks/fill-mask.md‎

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- [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased): The famous BERT model.
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- [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base): A multilingual model trained on 100 languages.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=fill-mask&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=fill-mask&sort=trending).
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### Using the API
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‎docs/api-inference/tasks/image-classification.md‎

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### Recommended models
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- [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224): A strong image classification model.
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- [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224): A robust image classification model.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-classification&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-classification&sort=trending).
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### Using the API
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‎docs/api-inference/tasks/image-segmentation.md‎

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- [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512): Semantic segmentation model trained on ADE20k benchmark dataset with 512x512 resolution.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-segmentation&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-segmentation&sort=trending).
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### Using the API
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‎docs/api-inference/tasks/image-text-to-text.md‎

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### Recommended models
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- [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct): Powerful vision language model with great visual understanding and reasoning capabilities.
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- [HuggingFaceM4/idefics2-8b-chatty](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty): Cutting-edge conversational vision language model that can take multiple image inputs.
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- [microsoft/Phi-3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct): Strong image-text-to-text model.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-text-to-text&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-text-to-text&sort=trending).
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### Using the API
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<curl>
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curl https://api-inference.huggingface.co/models/HuggingFaceM4/idefics2-8b-chatty \
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API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-11B-Vision-Instruct"
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from huggingface_hub import InferenceClient
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model="HuggingFaceM4/idefics2-8b-chatty",
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model="meta-llama/Llama-3.2-11B-Vision-Instruct",
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"https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-11B-Vision-Instruct",
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{
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‎docs/api-inference/tasks/image-to-image.md‎

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- [timbrooks/instruct-pix2pix](https://huggingface.co/timbrooks/instruct-pix2pix): A model that takes an image and an instruction to edit the image.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-to-image&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-to-image&sort=trending).
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### Using the API
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‎docs/api-inference/tasks/object-detection.md‎

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- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50): Solid object detection model trained on the benchmark dataset COCO 2017.
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- [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k): Strong object detection model trained on ImageNet-21k dataset.
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This is only a subset of the supported models. Find the model that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=object-detection&sort=trending).
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Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=object-detection&sort=trending).
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### Using the API
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