Blog

Bytez

Welcome

Install our API libraries and run inference in seconds

Get started

An Overview of CRUD Operations & Unified Input Schemas for SOTA AI models

Overview

LangChain

API Status Dashboard

✨ Access 40k+ open source and proprietary AI models through a standard API. Achieve GPU-backed performance at CPU pricing ✨

Retrieve a list of available models for various tasks. Use the query parameter `task` to filter by task type, e.g. `chat`.

Models

Quickstart

Get your API key and access `open source` and `closed-source` models through a standard format

API Keys

Download or Install Libraries to Access Bytez

Libraries

API Status

Generate text with chat models using structured inputs and streaming.

Text

Use chat + vision models (`image-as-input`) to generate text-based responses.

Vision

Use chat + audio models (`audio-as-input`) to transcribe and analyze audio.

Audio

Use chat + video mpdels (`video-as-input`) to generate insightful responses.

Video

Generate images using Bytez API with `base64` or `URL` inputs.

Image Generation

Generate `text` and `vector` embeddings with the Bytez API.

Embeddings

Execute `code` or `actions` based on model-generated outputs

Function Calling

Audio Classification

Automatic Speech Recognition

Depth Estimation

Document Question Answering

Feature Extraction

Fill Mask

Image Classification

Image Feature Extraction

Image Segmentation

Image-to-Text

Mask Generation

Object Detection

Question Answering

Sentence Similarity

Summarization

Text Classification

Text Generation

Text-to-Speech

Text-to-Text Generation

Text-to-Video

Token Classification

Translation

Unconditional Image Generation

Video Classification

Visual Question Answering

Zero Shot Classification

Zero Shot Image Classification

Zero Shot Object Detection

Our pricing follows standard Cloud Compute costs you know and are familiar with

Pricing

Learn how to implement streaming with Bytez API

Streaming

Retrieve a list of tasks supported by the platform.

Tasks

Create an auto-scaling cluster for the specified model.

Create Cluster

Use POST to run any open source model. This will automatically create an auto-scaling cluster of the model and run inference on that cluster.

Run Inference

Retrieve information about the specified model cluster.

Read Cluster

Update the capacity or configuration of the specified model cluster.

Update Cluster

Delete Cluster

Retrieve a list of available model clusters.

Clusters

Send requests to any OpenAI model by specifying the `{model}` placeholder in the path.

OpenAI

Send requests to any Google model by specifying the `{model}` placeholder in the path.

Google

Send requests to any Cohere model by specifying the `{model}` placeholder in the path.

Cohere

Send requests to any Anthropic model by specifying the `{model}` placeholder in the path.

Anthropic

Send requests to any Mistral model by specifying the `{model}` placeholder in the path.

Mistral

Generate text using the Phi-3-mini-4k-instruct model.

Chat + Text

Analyze images using the Llama-3.2-11B-Vision-Instruct model.

Chat + Vision

Analyze audio using the Qwen2-Audio-7B-Instruct model.

Chat + Audio

Analyze video using the LLaVA-NeXT-Video-7B-hf model.

Chat + Video

Run the `camembert-base` model to predict masked tokens in the input text.

Run the `mms-tts-eng` model to convert text into speech.

Run the `musicgen-melody` model to convert text into audio.

Text-to-Audio

Run the `stable-diffusion-xl-base-1.0` model to generate images from text.

Text-to-Image

Run the `En-Fr_Translation_Model` to translate English text to French.

Run the `bart-base-cnn` model to summarize input text.

Run the `specter2_base` model to extract features from input text.

Run the `distilbert-base-uncased-finetuned-sentiment-amazon` model to classify input text.

Run the `mn-xlm-roberta-base-named-entity` model to perform named entity recognition.

Run the `mt0-small` model to generate text-to-text transformations.

Text2Text Generation

Run the `gpt2` model to generate text from an input prompt.

Generate captions for images using the `caption-gen` model.

Image to Text

Classify images into categories using the `vit-base-cats-vs-dogs` model.

Perform image segmentation using the `deeplabv3-mobilevit-small` model.

Estimate depth in images using the `Depth-Anything-V2-Base-hf` model.

Detect objects in an image using the `aisak-detect` model.

Generate a mask for the given image using the `skinsam` model.

Extract features from an image using the `dinov2-base` model.

Answer a question given a context using the `xlm-roberta-base-finetune-qa` model.

Answer a question based on the content of a document using the `CQI_Visual_Question_Awnser_PT_v0` model.

Answer a question based on an image using the `Vilt_fine_tune_2000` model.

Detect objects in an image using the `owlv2-base-patch16-finetuned` model.

Classify images into candidate labels using the `clip-hugging-face-finetuned` model.

Classify text into candidate labels using the `DistilBERT_eco_ZeroShot` model.

The story, mission, and community behind Bytez

Endpoints

Examples

Blog

Bytez Powers NeurIPS 2024