Update API inference documentation (automated) (#1588)

Co-authored-by: hanouticelina <[email protected]>

Author: HuggingFaceInfra

docs/api-inference/tasks/chat-completion.md

@@ -23,13 +23,15 @@ This is a subtask of [`text-generation`](https://huggingface.co/docs/api-inferen
 
 - [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it): A text-generation model trained to follow instructions.
 - [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct): Very powerful text generation model trained to follow instructions.
-- [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct): Small yet powerful text generation model.
+- [microsoft/phi-4](https://huggingface.co/microsoft/phi-4): Powerful text generation model by Microsoft.
+- [PowerInfer/SmallThinker-3B-Preview](https://huggingface.co/PowerInfer/SmallThinker-3B-Preview): A very powerful model with reasoning capabilities.
 - [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct): Strong text generation model to follow instructions.
+- [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct): Text generation model used to write code.
 
 #### Conversational Vision-Language Models (VLMs)
 
-- [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.
 - [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct): Strong image-text-to-text model.
+- [Qwen/QVQ-72B-Preview](https://huggingface.co/Qwen/QVQ-72B-Preview): Image-text-to-text model with reasoning capabilities.
 
 ### API Playground
 
@@ -208,11 +210,11 @@ To use the JavaScript client, see `huggingface.js`'s [package reference](https:/
 
 <curl>
 ```bash
-curl 'https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-11B-Vision-Instruct/v1/chat/completions' \
+curl 'https://api-inference.huggingface.co/models/Qwen/Qwen2-VL-7B-Instruct/v1/chat/completions' \
 -H 'Authorization: Bearer hf_***' \
 -H 'Content-Type: application/json' \
 --data '{
-    "model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+    "model": "Qwen/Qwen2-VL-7B-Instruct",
     "messages": [
 		{
 			"role": "user",
@@ -262,7 +264,7 @@ messages = [
 ]
 
 stream = client.chat.completions.create(
-    model="meta-llama/Llama-3.2-11B-Vision-Instruct", 
+    model="Qwen/Qwen2-VL-7B-Instruct", 
 	messages=messages, 
 	max_tokens=500,
 	stream=True
@@ -300,7 +302,7 @@ messages = [
 ]
 
 stream = client.chat.completions.create(
-    model="meta-llama/Llama-3.2-11B-Vision-Instruct", 
+    model="Qwen/Qwen2-VL-7B-Instruct", 
 	messages=messages, 
 	max_tokens=500,
 	stream=True
@@ -323,7 +325,7 @@ const client = new HfInference("hf_***");
 let out = "";
 
 const stream = client.chatCompletionStream({
-	model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
+	model: "Qwen/Qwen2-VL-7B-Instruct",
 	messages: [
 		{
 			role: "user",
@@ -365,7 +367,7 @@ const client = new OpenAI({
 let out = "";
 
 const stream = await client.chat.completions.create({
-	model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
+	model: "Qwen/Qwen2-VL-7B-Instruct",
 	messages: [
 		{
 			role: "user",

docs/api-inference/tasks/fill-mask.md

@@ -24,7 +24,6 @@ For more details about the `fill-mask` task, check out its [dedicated page](http
 
 ### Recommended models
 
-- [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased): The famous BERT model.
 - [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base): A multilingual model trained on 100 languages.
 
 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).
@@ -36,7 +35,7 @@ Explore all available models and find the one that suits you best [here](https:/
 
 <curl>
 ```bash
-curl https://api-inference.huggingface.co/models/google-bert/bert-base-uncased \
+curl https://api-inference.huggingface.co/models/FacebookAI/xlm-roberta-base \
 	-X POST \
 	-d '{"inputs": "The answer to the universe is [MASK]."}' \
 	-H 'Content-Type: application/json' \
@@ -48,7 +47,7 @@ curl https://api-inference.huggingface.co/models/google-bert/bert-base-uncased \
 ```py
 import requests
 
-API_URL = "https://api-inference.huggingface.co/models/google-bert/bert-base-uncased"
+API_URL = "https://api-inference.huggingface.co/models/FacebookAI/xlm-roberta-base"
 headers = {"Authorization": "Bearer hf_***"}
 
 def query(payload):
@@ -67,7 +66,7 @@ To use the Python client, see `huggingface_hub`'s [package reference](https://hu
 ```js
 async function query(data) {
 	const response = await fetch(
-		"https://api-inference.huggingface.co/models/google-bert/bert-base-uncased",
+		"https://api-inference.huggingface.co/models/FacebookAI/xlm-roberta-base",
 		{
 			headers: {
 				Authorization: "Bearer hf_***",

docs/api-inference/tasks/image-text-to-text.md

@@ -24,8 +24,8 @@ For more details about the `image-text-to-text` task, check out its [dedicated p
 
 ### Recommended models
 
-- [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.
 - [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct): Strong image-text-to-text model.
+- [Qwen/QVQ-72B-Preview](https://huggingface.co/Qwen/QVQ-72B-Preview): Image-text-to-text model with reasoning capabilities.
 
 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).
 
@@ -36,7 +36,7 @@ Explore all available models and find the one that suits you best [here](https:/
 
 <curl>
 ```bash
-curl https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-11B-Vision-Instruct \
+curl https://api-inference.huggingface.co/models/Qwen/Qwen2-VL-7B-Instruct \
 	-X POST \
 	-d '{"inputs": "Can you please let us know more details about your "}' \
 	-H 'Content-Type: application/json' \
@@ -54,7 +54,7 @@ client = InferenceClient(api_key="hf_***")
 messages = "\"Can you please let us know more details about your \""
 
 stream = client.chat.completions.create(
-    model="meta-llama/Llama-3.2-11B-Vision-Instruct", 
+    model="Qwen/Qwen2-VL-7B-Instruct", 
 	messages=messages, 
 	max_tokens=500,
 	stream=True
@@ -76,7 +76,7 @@ client = OpenAI(
 messages = "\"Can you please let us know more details about your \""
 
 stream = client.chat.completions.create(
-    model="meta-llama/Llama-3.2-11B-Vision-Instruct", 
+    model="Qwen/Qwen2-VL-7B-Instruct", 
 	messages=messages, 
 	max_tokens=500,
 	stream=True
@@ -93,7 +93,7 @@ To use the Python client, see `huggingface_hub`'s [package reference](https://hu
 ```js
 async function query(data) {
 	const response = await fetch(
-		"https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-11B-Vision-Instruct",
+		"https://api-inference.huggingface.co/models/Qwen/Qwen2-VL-7B-Instruct",
 		{
 			headers: {
 				Authorization: "Bearer hf_***",

docs/api-inference/tasks/image-to-image.md

@@ -29,7 +29,6 @@ For more details about the `image-to-image` task, check out its [dedicated page]
 
 ### Recommended models
 
-- [timbrooks/instruct-pix2pix](https://huggingface.co/timbrooks/instruct-pix2pix): A model that takes an image and an instruction to edit the image.
 
 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).
 
@@ -49,7 +48,7 @@ No snippet available for this task.
 | **inputs*** | _string_ | The input image data as a base64-encoded string. If no `parameters` are provided, you can also provide the image data as a raw bytes payload. |
 | **parameters** | _object_ |  |
 | **        guidance_scale** | _number_ | For diffusion models. A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. |
-| **        negative_prompt** | _string[]_ | One or several prompt to guide what NOT to include in image generation. |
+| **        negative_prompt** | _string_ | One prompt to guide what NOT to include in image generation. |
 | **        num_inference_steps** | _integer_ | For diffusion models. The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. |
 | **        target_size** | _object_ | The size in pixel of the output image. |
 | **                width*** | _integer_ |  |

docs/api-inference/tasks/question-answering.md

@@ -26,7 +26,6 @@ For more details about the `question-answering` task, check out its [dedicated p
 
 - [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2): A robust baseline model for most question answering domains.
 - [distilbert/distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad): Small yet robust model that can answer questions.
-- [google/tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq): A special model that can answer questions from tables.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=question-answering&sort=trending).
 

docs/api-inference/tasks/table-question-answering.md

@@ -24,7 +24,6 @@ For more details about the `table-question-answering` task, check out its [dedic
 
 ### Recommended models
 
-- [google/tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq): A robust table question answering model.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=table-question-answering&sort=trending).
 
@@ -35,7 +34,7 @@ Explore all available models and find the one that suits you best [here](https:/
 
 <curl>
 ```bash
-curl https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq \
+curl https://api-inference.huggingface.co/models/<REPO_ID> \
 	-X POST \
 	-d '{"inputs": { "query": "How many stars does the transformers repository have?", "table": { "Repository": ["Transformers", "Datasets", "Tokenizers"], "Stars": ["36542", "4512", "3934"], "Contributors": ["651", "77", "34"], "Programming language": [ "Python", "Python", "Rust, Python and NodeJS" ] } }}' \
 	-H 'Content-Type: application/json' \
@@ -47,7 +46,7 @@ curl https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq
 ```py
 import requests
 
-API_URL = "https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq"
+API_URL = "https://api-inference.huggingface.co/models/<REPO_ID>"
 headers = {"Authorization": "Bearer hf_***"}
 
 def query(payload):
@@ -78,7 +77,7 @@ To use the Python client, see `huggingface_hub`'s [package reference](https://hu
 ```js
 async function query(data) {
 	const response = await fetch(
-		"https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq",
+		"https://api-inference.huggingface.co/models/<REPO_ID>",
 		{
 			headers: {
 				Authorization: "Bearer hf_***",

docs/api-inference/tasks/text-generation.md

@@ -28,8 +28,10 @@ For more details about the `text-generation` task, check out its [dedicated page
 
 - [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it): A text-generation model trained to follow instructions.
 - [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct): Very powerful text generation model trained to follow instructions.
-- [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct): Small yet powerful text generation model.
+- [microsoft/phi-4](https://huggingface.co/microsoft/phi-4): Powerful text generation model by Microsoft.
+- [PowerInfer/SmallThinker-3B-Preview](https://huggingface.co/PowerInfer/SmallThinker-3B-Preview): A very powerful model with reasoning capabilities.
 - [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct): Strong text generation model to follow instructions.
+- [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct): Text generation model used to write code.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-generation&sort=trending).
 

docs/api-inference/tasks/text-to-image.md

@@ -115,7 +115,7 @@ To use the JavaScript client, see `huggingface.js`'s [package reference](https:/
 | **inputs*** | _string_ | The input text data (sometimes called "prompt") |
 | **parameters** | _object_ |  |
 | **        guidance_scale** | _number_ | A higher guidance scale value encourages the model to generate images closely linked to the text prompt, but values too high may cause saturation and other artifacts. |
-| **        negative_prompt** | _string[]_ | One or several prompt to guide what NOT to include in image generation. |
+| **        negative_prompt** | _string_ | One prompt to guide what NOT to include in image generation. |
 | **        num_inference_steps** | _integer_ | The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. |
 | **        target_size** | _object_ | The size in pixel of the output image |
 | **                width*** | _integer_ |  |

docs/api-inference/tasks/zero-shot-classification.md

@@ -25,7 +25,6 @@ For more details about the `zero-shot-classification` task, check out its [dedic
 ### Recommended models
 
 - [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli): Powerful zero-shot text classification model.
-- [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7): Powerful zero-shot multilingual text classification model that can accomplish multiple tasks.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=zero-shot-classification&sort=trending).
 

scripts/api-inference/package.json

@@ -14,7 +14,7 @@
   "author": "",
   "license": "ISC",
   "dependencies": {
-    "@huggingface/tasks": "^0.13.14",
+    "@huggingface/tasks": "^0.14.0",
     "@types/node": "^22.5.0",
     "handlebars": "^4.7.8",
     "node": "^20.17.0",