Large Language Models
Hugging Face Inference API
This documentation describes the integration of MindsDB with Hugging Face Inference API. The integration allows for the deployment of Hugging Face models through Inference API within MindsDB, providing the models with access to data from various data sources.
Prerequisites
Before proceeding, ensure the following prerequisites are met:
- Install MindsDB locally via Docker or use MindsDB Cloud.
- To use Hugging Face Inference API within MindsDB, install the required dependencies following this instruction.
- Obtain the API key for Hugging Face Inference API required to deploy and use Hugging Face models through Inference API within MindsDB. Generate tokens in the
Settings -> Access Tokens
tab of the Hugging Face account.
Setup
Create an AI engine from the Hugging Face Inference API handler.
CREATE ML_ENGINE huggingface_api_engine
FROM huggingface_api
USING
api_key = 'api-key-value';
Create a model using huggingface_api_engine
as an engine.
CREATE MODEL huggingface_api_model
PREDICT target_column
USING
engine = 'huggingface_api_engine', -- engine name as created via CREATE ML_ENGINE
task = 'task_name', -- choose one of 'text-classification', 'text-generation', 'question-answering', 'sentence-similarity', 'zero-shot-classification', 'summarization', 'fill-mask', 'image-classification', 'object-detection', 'automatic-speech-recognition', 'audio-classification'
input_column = 'column_name', -- column that stores input/question to the model
labels = ['label 1', 'label 2']; -- labels used to classify data (used for classification tasks)
Usage
The following usage examples utilize huggingface_api_engine
to create a model with the CREATE MODEL
statement.
Create a model to classify input text as spam or ham.
CREATE MODEL spam_classifier
PREDICT is_spam
USING
engine = 'huggingface_api_engine',
task = 'text-classification',
column = 'text';
Query the model to get predictions.
SELECT text, is_spam
FROM spam_classifier
WHERE text = 'Subscribe to this channel asap';
Here is the output:
+--------------------------------+---------+
| text | is_spam |
+--------------------------------+---------+
| Subscribe to this channel asap | spam |
+--------------------------------+---------+
Next Steps
Follow this link to see more use case examples.