[Inference API] Add image-text-to-text task and fix generate script

docs/api-inference/_toctree.yml

@@ -30,6 +30,8 @@
       title: Image Segmentation
     - local: tasks/image-to-image
       title: Image to Image
+    - local: tasks/image-text-to-text
+      title: Image-Text to Text
     - local: tasks/object-detection
       title: Object Detection
     - local: tasks/question-answering

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

@@ -43,7 +43,6 @@ curl https://api-inference.huggingface.co/models/<REPO_ID> \
 	-X POST \
 	--data-binary '@sample1.flac' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>
 

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

@@ -30,6 +30,7 @@ For more details about the `automatic-speech-recognition` task, check out its [d
 ### Recommended models
 
 - [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3): A powerful ASR model by OpenAI.
+- [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.
 - [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1): Powerful speaker diarization model.
 
 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).
@@ -45,7 +46,6 @@ curl https://api-inference.huggingface.co/models/openai/whisper-large-v3 \
 	-X POST \
 	--data-binary '@sample1.flac' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>
 
@@ -108,7 +108,7 @@ To use the JavaScript client, see `huggingface.js`'s [package reference](https:/
 | **inputs*** | _string_ | The input audio data as a base64-encoded string. If no `parameters` are provided, you can also provide the audio data as a raw bytes payload. |
 | **parameters** | _object_ | Additional inference parameters for Automatic Speech Recognition |
 | **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return_timestamps** | _boolean_ | Whether to output corresponding timestamps with the generated text |
-| **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;generate** | _object_ | Ad-hoc parametrization of the text generation process |
+| **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;generation_parameters** | _object_ | Ad-hoc parametrization of the text generation process |
 | **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;temperature** | _number_ | The value used to modulate the next token probabilities. |
 | **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;top_k** | _integer_ | The number of highest probability vocabulary tokens to keep for top-k-filtering. |
 | **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;top_p** | _number_ | If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. |

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

@@ -14,20 +14,20 @@ For more details, check out:
 
 ## Chat Completion
 
-Generate a response given a list of messages.
-This is a subtask of [`text-generation`](./text_generation) designed to generate responses in a conversational context.
-
-
+Generate a response given a list of messages in a conversational context, supporting both conversational Language Models (LLMs) and conversational Vision-Language Models (VLMs).
+This is a subtask of [`text-generation`](./text_generation) and [`image-text-to-text`](./image_text_to_text).
 
 ### Recommended models
 
+#### Conversational Large Language Models (LLMs)
 - [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.
 - [HuggingFaceH4/starchat2-15b-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1): Strong coding assistant model.
 - [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407): Very strong open-source large language model.
 
-
+#### Conversational Vision-Language Models (VLMs)
+- [microsoft/Phi-3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct): Strong image-text-to-text model.
 
 ### Using the API
 
@@ -37,6 +37,8 @@ The API supports:
 * Using grammars, constraints, and tools.
 * Streaming the output
 
+#### Code snippet example for conversational LLMs
+
 
 <inferencesnippet>
 
@@ -59,21 +61,18 @@ curl 'https://api-inference.huggingface.co/models/google/gemma-2-2b-it/v1/chat/c
 ```py
 from huggingface_hub import InferenceClient
 
-client = InferenceClient(
-    "google/gemma-2-2b-it",
-    token="hf_***",
-)
+client = InferenceClient(api_key="hf_***")
 
 for message in client.chat_completion(
+	model="google/gemma-2-2b-it",
 	messages=[{"role": "user", "content": "What is the capital of France?"}],
 	max_tokens=500,
 	stream=True,
 ):
     print(message.choices[0].delta.content, end="")
-
 ```
 
-To use the Python client, see `huggingface_hub`'s [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.chat_completion).
+To use the Python client, see `huggingface_hub`'s [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.conversational_text-generation).
 </python>
 
 <js>
@@ -89,10 +88,96 @@ for await (const chunk of inference.chatCompletionStream({
 })) {
 	process.stdout.write(chunk.choices[0]?.delta?.content || "");
 }
+```
+
+To use the JavaScript client, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#conversationaltext-generation).
+</js>
+
+</inferencesnippet>
+
+
+
+#### Code snippet example for conversational VLMs
+
 
+<inferencesnippet>
+
+<curl>
+```bash
+curl 'https://api-inference.huggingface.co/models/microsoft/Phi-3.5-vision-instruct/v1/chat/completions' \
+-H "Authorization: Bearer hf_***" \
+-H 'Content-Type: application/json' \
+-d '{
+	"model": "microsoft/Phi-3.5-vision-instruct",
+	"messages": [
+		{
+			"role": "user",
+			"content": [
+				{"type": "image_url", "image_url": {"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"}},
+				{"type": "text", "text": "Describe this image in one sentence."}
+			]
+		}
+	],
+	"max_tokens": 500,
+	"stream": false
+}'
+
+```
+</curl>
+
+<python>
+```py
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(api_key="hf_***")
+
+image_url = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+
+for message in client.chat_completion(
+	model="microsoft/Phi-3.5-vision-instruct",
+	messages=[
+		{
+			"role": "user",
+			"content": [
+				{"type": "image_url", "image_url": {"url": image_url}},
+				{"type": "text", "text": "Describe this image in one sentence."},
+			],
+		}
+	],
+	max_tokens=500,
+	stream=True,
+):
+	print(message.choices[0].delta.content, end="")
+```
+
+To use the Python client, see `huggingface_hub`'s [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.conversational_image-text-to-text).
+</python>
+
+<js>
+```js
+import { HfInference } from "@huggingface/inference";
+
+const inference = new HfInference("hf_***");
+const imageUrl = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg";
+
+for await (const chunk of inference.chatCompletionStream({
+	model: "microsoft/Phi-3.5-vision-instruct",
+	messages: [
+		{
+			"role": "user",
+			"content": [
+				{"type": "image_url", "image_url": {"url": imageUrl}},
+				{"type": "text", "text": "Describe this image in one sentence."},
+			],
+		}
+	],
+	max_tokens: 500,
+})) {
+	process.stdout.write(chunk.choices[0]?.delta?.content || "");
+}
 ```
 
-To use the JavaScript client, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#chatcompletion).
+To use the JavaScript client, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#conversationalimage-text-to-text).
 </js>
 
 </inferencesnippet>

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

@@ -45,7 +45,6 @@ curl https://api-inference.huggingface.co/models/thenlper/gte-large \
 	-d '{"inputs": "Today is a sunny day and I will get some ice cream."}' \
 	-H 'Content-Type: application/json' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>
 

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

@@ -41,7 +41,6 @@ curl https://api-inference.huggingface.co/models/google-bert/bert-base-uncased \
 	-d '{"inputs": "The answer to the universe is [MASK]."}' \
 	-H 'Content-Type: application/json' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>
 

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

@@ -39,7 +39,6 @@ curl https://api-inference.huggingface.co/models/google/vit-base-patch16-224 \
 	-X POST \
 	--data-binary '@cats.jpg' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>
 

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

@@ -39,7 +39,6 @@ curl https://api-inference.huggingface.co/models/nvidia/segformer-b0-finetuned-a
 	-X POST \
 	--data-binary '@cats.jpg' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>
 

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

@@ -0,0 +1,114 @@
+<!---
+This markdown file has been generated from a script. Please do not edit it directly.
+For more details, check out:
+- the `generate.ts` script: https://github.com/huggingface/hub-docs/blob/main/scripts/api-inference/scripts/generate.ts
+- the task template defining the sections in the page: https://github.com/huggingface/hub-docs/tree/main/scripts/api-inference/templates/task/image-text-to-text.handlebars
+- the input jsonschema specifications used to generate the input markdown table: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/tasks/image-text-to-text/spec/input.json
+- the output jsonschema specifications used to generate the output markdown table: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/tasks/image-text-to-text/spec/output.json
+- the snippets used to generate the example:
+  - curl: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/snippets/curl.ts
+  - python: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/snippets/python.ts
+  - javascript: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/snippets/js.ts
+- the "tasks" content for recommended models: https://huggingface.co/api/tasks
+--->
+
+## Image-Text to Text
+
+Image-text-to-text models take in an image and text prompt and output text. These models are also called vision-language models, or VLMs. The difference from image-to-text models is that these models take an additional text input, not restricting the model to certain use cases like image captioning, and may also be trained to accept a conversation as input.
+
+<Tip>
+
+For more details about the `image-text-to-text` task, check out its [dedicated page](https://huggingface.co/tasks/image-text-to-text)! You will find examples and related materials.
+
+</Tip>
+
+### Recommended models
+
+- [HuggingFaceM4/idefics2-8b-chatty](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty): Cutting-edge conversational vision language model that can take multiple image inputs.
+- [microsoft/Phi-3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct): Strong image-text-to-text model.
+
+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).
+
+### Using the API
+
+
+<inferencesnippet>
+
+<curl>
+```bash
+curl https://api-inference.huggingface.co/models/HuggingFaceM4/idefics2-8b-chatty \
+	-X POST \
+	-d '{"inputs": No input example has been defined for this model task.}' \
+	-H 'Content-Type: application/json' \
+	-H "Authorization: Bearer hf_***"
+```
+</curl>
+
+<python>
+```py
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/HuggingFaceM4/idefics2-8b-chatty"
+headers = {"Authorization": "Bearer hf_***"}
+
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(api_key="hf_***")
+
+image_url = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+
+for message in client.chat_completion(
+	model="HuggingFaceM4/idefics2-8b-chatty",
+	messages=[
+		{
+			"role": "user",
+			"content": [
+				{"type": "image_url", "image_url": {"url": image_url}},
+				{"type": "text", "text": "Describe this image in one sentence."},
+			],
+		}
+	],
+	max_tokens=500,
+	stream=True,
+):
+	print(message.choices[0].delta.content, end="")
+```
+
+To use the Python client, see `huggingface_hub`'s [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.image_text-to-text).
+</python>
+
+<js>
+```js
+async function query(data) {
+	const response = await fetch(
+		"https://api-inference.huggingface.co/models/HuggingFaceM4/idefics2-8b-chatty",
+		{
+			headers: {
+				Authorization: "Bearer hf_***"
+				"Content-Type": "application/json",
+			},
+			method: "POST",
+			body: JSON.stringify(data),
+		}
+	);
+	const result = await response.json();
+	return result;
+}
+
+query({"inputs": No input example has been defined for this model task.}).then((response) => {
+	console.log(JSON.stringify(response));
+});
+```
+
+To use the JavaScript client, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#imagetext-to-text).
+</js>
+
+</inferencesnippet>
+
+
+
+### API specification
+
+For the API specification of conversational image-text-to-text models, please refer to the [Chat Completion API documentation](https://huggingface.co/docs/api-inference/tasks/chat-completion#api-specification).
+
+

docs/api-inference/tasks/object-detection.md

@@ -40,7 +40,6 @@ curl https://api-inference.huggingface.co/models/facebook/detr-resnet-50 \
 	-X POST \
 	--data-binary '@cats.jpg' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>
 

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

@@ -26,6 +26,7 @@ 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.
 
 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=question-answering&sort=trending).
 
@@ -41,7 +42,6 @@ curl https://api-inference.huggingface.co/models/deepset/roberta-base-squad2 \
 	-d '{"inputs": { "question": "What is my name?", "context": "My name is Clara and I live in Berkeley." }}' \
 	-H 'Content-Type: application/json' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>
 

docs/api-inference/tasks/summarization.md

@@ -40,7 +40,6 @@ curl https://api-inference.huggingface.co/models/facebook/bart-large-cnn \
 	-d '{"inputs": "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."}' \
 	-H 'Content-Type: application/json' \
 	-H "Authorization: Bearer hf_***"
-
 ```
 </curl>