Integrate Gemini and Hugging Face
Connect Gemini and Hugging Face nodes in your workflow. Integrate with any tool or database and ship powerful backend logic and APIs instantly - No code required!
Node stack
Supported Gemini and Hugging Face Nodes
Add any other tools or your preferred database nodes. If an integration is not available generate your own using AI
Count Tokens in Prompt
When using long prompts, it might be useful to count tokens before sending any content to the model.
Gemini Text Generator
Make an API call to the Generative Language Model endpoint
Generate Embedding
Generate Embeddings from text input and represent text (words, sentences, and blocks of text) in a vectorized formusing Gemini AI
Multimodal
Use Google's Gemini AI to generate text from text-only or text-and-image input. [Full documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/quickstart-multimodal).
Stream Response
Generates a stream of response text using Google's Generative AI with a given prompt
Caption Image
Generate caption for the image using Hugging Face's [Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large) model for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone).
Image Classification
Get classification labels for your image using Hugging Face's [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) model which is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Text Summarization
Summarize long text using Hugging Face's [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) model which is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
Text-To-Image
Generate image from text, using Hugging Face's [openskyml/dalle-3-xl](https://huggingface.co/openskyml/dalle-3-xl) test model very similar to Dall•E 3.
Text-To-Music
Generate music from text using Hugging Face's [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) model capable of generating high-quality music samples conditioned on text descriptions or audio prompts.
Quick start
How to integrate Gemini and Hugging Face
Step 1 — Add the nodes to your workflow
Create a new workflow in BuildShip, click “Add node”, and select the Gemini and Hugging Face actions you want to use.
Step 2 — Configure each node
Go to each node to authenticate (or add your API key) and fill in the required parameters.
Step 3 — Connect the nodes
Each node in BuildShip can connect to others by using their output variables. When you reference a variable from one node in another, BuildShip automatically links them in the workflow.
Step 4 — Test your workflow
Define your starting data in the Inputs node and choose what to do with the result in the Flow Output node. Finally, run a test to see your workflow in action.
blog posts & tutorials
Recommended Reads
Below are recommneded blogs that will help in your journey
support
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