🔖 @hugginface/inference v2.0.0

Author: machineuser

README.md

@@ -64,7 +64,7 @@ You can run our packages with vanilla JS, without any bundler, by using a CDN or
 ```html
 
 <script type="module">
-    import { HfInference } from 'https://cdn.jsdelivr.net/npm/@huggingface/inference@1.8.0/+esm';
+    import { HfInference } from 'https://cdn.jsdelivr.net/npm/@huggingface/inference@2.0.0/+esm';
     import { createRepo, commit, deleteRepo, listFiles } from "https://cdn.jsdelivr.net/npm/@huggingface/[email protected]/+esm";
 </script>
 ```

docs/_toctree.yml

@@ -62,51 +62,49 @@
       sections:
         - title: HfInference
           local: inference/classes/HfInference
-    - title: Enums
-      sections:
-        - title: TextGenerationStreamFinishReason
-          local: inference/enums/TextGenerationStreamFinishReason
+        - title: HfInferenceEndpoint
+          local: inference/classes/HfInferenceEndpoint
     - title: Interfaces
       sections:
-        - title: Args
-          local: inference/interfaces/Args
-        - title: AudioClassificationReturnValue
-          local: inference/interfaces/AudioClassificationReturnValue
-        - title: AutomaticSpeechRecognitionReturn
-          local: inference/interfaces/AutomaticSpeechRecognitionReturn
-        - title: ConversationalReturn
-          local: inference/interfaces/ConversationalReturn
-        - title: ImageClassificationReturnValue
-          local: inference/interfaces/ImageClassificationReturnValue
-        - title: ImageSegmentationReturnValue
-          local: inference/interfaces/ImageSegmentationReturnValue
-        - title: ImageToTextReturn
-          local: inference/interfaces/ImageToTextReturn
-        - title: ObjectDetectionReturnValue
-          local: inference/interfaces/ObjectDetectionReturnValue
+        - title: AudioClassificationOutputValue
+          local: inference/interfaces/AudioClassificationOutputValue
+        - title: AutomaticSpeechRecognitionOutput
+          local: inference/interfaces/AutomaticSpeechRecognitionOutput
+        - title: BaseArgs
+          local: inference/interfaces/BaseArgs
+        - title: ConversationalOutput
+          local: inference/interfaces/ConversationalOutput
+        - title: ImageClassificationOutputValue
+          local: inference/interfaces/ImageClassificationOutputValue
+        - title: ImageSegmentationOutputValue
+          local: inference/interfaces/ImageSegmentationOutputValue
+        - title: ImageToTextOutput
+          local: inference/interfaces/ImageToTextOutput
+        - title: ObjectDetectionOutputValue
+          local: inference/interfaces/ObjectDetectionOutputValue
         - title: Options
           local: inference/interfaces/Options
-        - title: QuestionAnswerReturn
-          local: inference/interfaces/QuestionAnswerReturn
-        - title: SummarizationReturn
-          local: inference/interfaces/SummarizationReturn
-        - title: TableQuestionAnswerReturn
-          local: inference/interfaces/TableQuestionAnswerReturn
-        - title: TextGenerationReturn
-          local: inference/interfaces/TextGenerationReturn
+        - title: QuestionAnsweringOutput
+          local: inference/interfaces/QuestionAnsweringOutput
+        - title: SummarizationOutput
+          local: inference/interfaces/SummarizationOutput
+        - title: TableQuestionAnsweringOutput
+          local: inference/interfaces/TableQuestionAnsweringOutput
+        - title: TextGenerationOutput
+          local: inference/interfaces/TextGenerationOutput
         - title: TextGenerationStreamBestOfSequence
           local: inference/interfaces/TextGenerationStreamBestOfSequence
         - title: TextGenerationStreamDetails
           local: inference/interfaces/TextGenerationStreamDetails
+        - title: TextGenerationStreamOutput
+          local: inference/interfaces/TextGenerationStreamOutput
         - title: TextGenerationStreamPrefillToken
           local: inference/interfaces/TextGenerationStreamPrefillToken
-        - title: TextGenerationStreamReturn
-          local: inference/interfaces/TextGenerationStreamReturn
         - title: TextGenerationStreamToken
           local: inference/interfaces/TextGenerationStreamToken
-        - title: TokenClassificationReturnValue
-          local: inference/interfaces/TokenClassificationReturnValue
-        - title: TranslationReturn
-          local: inference/interfaces/TranslationReturn
-        - title: ZeroShotClassificationReturnValue
-          local: inference/interfaces/ZeroShotClassificationReturnValue
+        - title: TokenClassificationOutputValue
+          local: inference/interfaces/TokenClassificationOutputValue
+        - title: TranslationOutput
+          local: inference/interfaces/TranslationOutput
+        - title: ZeroShotClassificationOutputValue
+          local: inference/interfaces/ZeroShotClassificationOutputValue

docs/index.md

@@ -9,12 +9,28 @@
   <br/>
 </p>
 
+```ts
+await inference.translation({
+  model: 't5-base',
+  inputs: 'My name is Wolfgang and I live in Berlin'
+})
+
+await inference.textToImage({
+  model: 'stabilityai/stable-diffusion-2',
+  inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
+  parameters: {
+    negative_prompt: 'blurry',
+  }
+})
+```
+
 # Hugging Face JS libraries
 
 This is a collection of JS libraries to interact with the Hugging Face API, with TS types included.
 
+- [@huggingface/inference](inference/README): Use the Inference API to make calls to 100,000+ Machine Learning models, or your own [inference endpoints](https://hf.co/docs/inference-endpoints/)!
 - [@huggingface/hub](hub/README): Interact with huggingface.co to create or delete repos and commit / download files
-- [@huggingface/inference](inference/README): Use the Inference API to make calls to 100,000+ Machine Learning models!
+
 
 With more to come, like `@huggingface/endpoints` to manage your HF Endpoints!
 
@@ -29,15 +45,15 @@ The libraries are still very young, please help us by opening issues!
 To install via NPM, you can download the libraries as needed:
 
 ```bash
-npm install @huggingface/hub
 npm install @huggingface/inference
+npm install @huggingface/hub
 ```
 
 Then import the libraries in your code:
 
 ```ts
-import { createRepo, commit, deleteRepo, listFiles } from "@huggingface/hub";
 import { HfInference } from "@huggingface/inference";
+import { createRepo, commit, deleteRepo, listFiles } from "@huggingface/hub";
 import type { RepoId, Credentials } from "@huggingface/hub";
 ```
 
@@ -48,18 +64,52 @@ You can run our packages with vanilla JS, without any bundler, by using a CDN or
 ```html
 
 <script type="module">
-    import { HfInference } from 'https://cdn.jsdelivr.net/npm/@huggingface/inference@1.8.0/+esm';
+    import { HfInference } from 'https://cdn.jsdelivr.net/npm/@huggingface/inference@2.0.0/+esm';
     import { createRepo, commit, deleteRepo, listFiles } from "https://cdn.jsdelivr.net/npm/@huggingface/[email protected]/+esm";
 </script>
 ```
 
-## Usage example
+## Usage examples
+
+Get your HF access token in your [account settings](https://huggingface.co/settings/tokens).
+
+### @huggingface/inference examples
 
 ```ts
-import { createRepo, uploadFile, deleteFiles } from "@huggingface/hub";
 import { HfInference } from "@huggingface/inference";
 
-// use an access token from your free account
+const HF_ACCESS_TOKEN = "hf_...";
+
+const inference = new HfInference(HF_ACCESS_TOKEN);
+
+await inference.translation({
+  model: 't5-base',
+  inputs: 'My name is Wolfgang and I live in Berlin'
+})
+
+await inference.textToImage({
+  model: 'stabilityai/stable-diffusion-2',
+  inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
+  parameters: {
+    negative_prompt: 'blurry',
+  }
+})
+
+await inference.imageToText({
+  data: await (await fetch('https://picsum.photos/300/300')).blob(),
+  model: 'nlpconnect/vit-gpt2-image-captioning',  
+})
+
+// Using your own inference endpoint: https://hf.co/docs/inference-endpoints/
+const gpt2 = inference.endpoint('https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2');
+const { generated_text } = await gpt2.textGeneration({inputs: 'The answer to the universe is'});
+```
+
+### @huggingface/hub examples
+
+```ts
+import { createRepo, uploadFile, deleteFiles } from "@huggingface/hub";
+
 const HF_ACCESS_TOKEN = "hf_...";
 
 await createRepo({
@@ -82,26 +132,6 @@ await deleteFiles({
   credentials: {accessToken: HF_ACCESS_TOKEN},
   paths: ["README.md", ".gitattributes"]
 });
-
-const inference = new HfInference(HF_ACCESS_TOKEN);
-
-await inference.translation({
-  model: 't5-base',
-  inputs: 'My name is Wolfgang and I live in Berlin'
-})
-
-await inference.textToImage({
-  inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
-  model: 'stabilityai/stable-diffusion-2',
-  parameters: {
-    negative_prompt: 'blurry',
-  }
-})
-
-await inference.imageToText({
-  data: await (await fetch('https://picsum.photos/300/300')).blob(),
-  model: 'nlpconnect/vit-gpt2-image-captioning',  
-})
 ```
 
 There are more features of course, check each library's README!

docs/inference/README.md

@@ -1,6 +1,8 @@
 # 🤗 Hugging Face Inference API
 
-A Typescript powered wrapper for the Hugging Face Inference API. Learn more about the Inference API at [Hugging Face](https://huggingface.co/docs/api-inference/index).
+A Typescript powered wrapper for the Hugging Face Inference API. Learn more about the Inference API at [Hugging Face](https://huggingface.co/docs/api-inference/index). It also works with [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index).
+
+You can also try out a live [interactive notebook](https://observablehq.com/@huggingface/hello-huggingface-js-inference) or see some demos on [hf.co/huggingfacejs](https://huggingface.co/huggingfacejs).
 
 ## Install
 
@@ -14,16 +16,16 @@ pnpm add @huggingface/inference
 
 ## Usage
 
-❗**Important note:** Using an API key is optional to get started, however you will be rate limited eventually. Join [Hugging Face](https://huggingface.co/join) and then visit [access tokens](https://huggingface.co/settings/tokens) to generate your API key for **free**.
+❗**Important note:** Using an access token is optional to get started, however you will be rate limited eventually. Join [Hugging Face](https://huggingface.co/join) and then visit [access tokens](https://huggingface.co/settings/tokens) to generate your access token for **free**.
 
-Your API key should be kept private. If you need to protect it in front-end applications, we suggest setting up a proxy server that stores the API key.
+Your access token should be kept private. If you need to protect it in front-end applications, we suggest setting up a proxy server that stores the access token.
 
 ### Basic examples
 
 ```typescript
 import { HfInference } from '@huggingface/inference'
 
-const hf = new HfInference('your api key')
+const hf = new HfInference('your access token')
 
 // Natural Language
 
@@ -41,15 +43,15 @@ await hf.summarization({
   }
 })
 
-await hf.questionAnswer({
+await hf.questionAnswering({
   model: 'deepset/roberta-base-squad2',
   inputs: {
     question: 'What is the capital of France?',
     context: 'The capital of France is Paris.'
   }
 })
 
-await hf.tableQuestionAnswer({
+await hf.tableQuestionAnswering({
   model: 'google/tapas-base-finetuned-wtq',
   inputs: {
     query: 'How many stars does the transformers repository have?',
@@ -107,7 +109,7 @@ await hf.conversational({
   }
 })
 
-await hf.featureExtraction({
+await hf.sentenceSimilarity({
   model: 'sentence-transformers/paraphrase-xlm-r-multilingual-v1',
   inputs: {
     source_sentence: 'That is a happy person',
@@ -119,6 +121,11 @@ await hf.featureExtraction({
   }
 })
 
+await hf.featureExtraction({
+    model: "sentence-transformers/distilbert-base-nli-mean-tokens",
+    inputs: "That is a happy person",
+});
+
 // Audio
 
 await hf.automaticSpeechRecognition({
@@ -160,6 +167,30 @@ await hf.imageToText({
   data: readFileSync('test/cats.png'),
   model: 'nlpconnect/vit-gpt2-image-captioning'
 })
+
+// Custom call, for models with custom parameters / outputs
+await hf.request({
+  model: 'my-custom-model',
+  inputs: 'hello world',
+  parameters: {
+    custom_param: 'some magic',
+  }
+})
+
+// Custom streaming call, for models with custom parameters / outputs
+for await (const output of hf.streamingRequest({
+  model: 'my-custom-model',
+  inputs: 'hello world',
+  parameters: {
+    custom_param: 'some magic',
+  }
+})) {
+  ...
+}
+
+// Using your own inference endpoint: https://hf.co/docs/inference-endpoints/
+const gpt2 = hf.endpoint('https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2');
+const { generated_text } = await gpt2.textGeneration({inputs: 'The answer to the universe is'});
 ```
 
 ## Supported Tasks
@@ -179,6 +210,7 @@ await hf.imageToText({
 - [x] Zero-shot classification
 - [x] Conversational
 - [x] Feature extraction
+- [x] Sentence Similarity
 
 ### Audio
 
@@ -193,6 +225,23 @@ await hf.imageToText({
 - [x] Text to image
 - [x] Image to text
 
+## Tree-shaking
+
+You can import the functions you need directly from the module, rather than using the `HfInference` class:
+
+```ts
+import {textGeneration} from "@huggingface/inference";
+
+await textGeneration({
+  accessToken: "hf_...",
+  model: "model_or_endpoint",
+  inputs: ...,
+  parameters: ...
+})
+```
+
+This will enable tree-shaking by your bundler.
+
 ## Running tests
 
 ```console