Pre-trained models

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Pre-trained models
Deploy a pre-trained model to kickstart your ML-powered application.
Baseten provides a growing set of pre-trained models that solve common ML tasks. These models are a great way to kickstart your ML application and showcase the features and functionality of Baseten - you can deploy pre-trained models along with optional application templates to add ML into your application in minutes.
Workspaces on the free Personal plan are limited to a single deployed model. Pre-trained models provided by Baseten do not count against this limit. To increase the number of models for your workspace, upgrade to a paid Baseten plan.
Pre-trained models offer many of the same features as your own models deployed on Baseten:
You can invoke the model directly through the Baseten Python client or an API call
You can create an application for the model
You can use the model in worklets
They come with editable readmes
Pre-trained models have some limitations that your models do not have:
You can't view health metrics or logs for pre-trained models
Pre-trained models do not have multiple versions
Pre-trained models have fixed model resources and auto-scaling settings
Pre-trained models do not count against the models limit in your workspace
Deployment
To deploy a pre-trained model, head to the Explore tab on the Models page. Scroll through pre-trained models, filter by tags, or search for the model you want to deploy. Click on that model,
Deploying a pre-trained model
By default, every pre-trained model comes with a starter application demonstrating how to use the model for a common use case. When you're in the deployment modal, you can opt out of creating this starter application by toggling off the "Create a starter application" option.
Once the model is deployed, you can edit the starter app or use the model in your applications.
Available models
Pre-trained models can be applied to common ML tasks. From speech transcription to sentiment analysis, image classification to photo restoration, pre-trained models deliver powerful ML capabilities directly to your applications. Baseten currently offers 23 pre-trained models:
CLIP: connecting text and images (Custom): Classify images with zero-shot-like functionality.
CodeGen mono 2B (Custom): Generate Python code from natural language or code prompts. This version of the model was fine-trained on Python code.
CodeGen multi 2B (Custom): Generate Python code from natural language or code prompts. This version of the model was fine-trained on natural language and broad programming languages.
Dall·E Mini (Custom): Generate novel images from a text prompt.
ELMo word representations (TensorFlow): Generate embeddings from a language model trained on the 1 Billion Word Benchmark.
English to French translation (Hugging Face: Transformer): Translate between English and French.
Extractive question answering (Hugging Face: Transformer): Extract an answer from a given text when provided a question.
Faster R-CNN Inception V2 (TensorFlow): Object detection model using Faster R-CNN with Inception
GFP-GAN (Custom): Restore photos with GFP-GAN.
GPT-J (Custom): Generate text with GPT-J. This is an implementation of EleutherAI GPT-J-6B.
Image Segmentation (Custom): Identify classes of objects in an image.
Iris random forest classifier (scikit-learn): Predict Iris class with a random forest classifier.
Masked language modeling (Hugging Face: Transformer): Fill a masked token in sequences based on the context around it.
MNIST digit classifier (scikit-learn): Logistic Regression model for classification of MNIST database of handwritten digits.
PDF Invoice parser (Google): A PDF parsing model that extracts structured data from invoices.
ResNet50 V2 (TensorFlow): Image detection model using ResNet V2 neural network architecture.
Sentiment analyzer (Hugging Face: Transformer): Analyze sentiment.
Style transfer (Custom): Apply one image's style onto another.
Summarizer (Hugging Face: Transformer): Summarize a text into a shorter text.
Text generation (Hugging Face: Transformer): Generate language given a prompt.
Token classification (Hugging Face: Transformer): Classify tokens in strings.
Wav2vec 2.0 speech transcription (Custom): Transcribe audio files with wav2vec 2.0.
Zero-shot classification (Hugging Face: Transformer): Classify snippets of text into unseen categories.
 
If you'd like Baseten to offer a specific pre-trained model, or if you'd like to fine-tune these models on your data, reach out.
 
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