HuggingFace Bridge

The HuggingFace bridge provides integration with the HuggingFace Inference API, enabling access to thousands of pre-trained models for various AI tasks including natural language processing, computer vision, audio processing, and more.

Features

• Multi-Provider Support: Access models through multiple inference providers (HuggingFace Inference, Cerebras, Cohere, Groq, Together, and others)
• Comprehensive Task Support: Support for 40+ different AI tasks including:
  • Chat completion and text generation
  • Image classification, object detection, and segmentation
  • Automatic speech recognition and audio classification
  • Text classification, translation, summarization
  • Feature extraction and embeddings
  • And many more...
• Model Discovery: Built-in API client to discover and query available models
• Flexible Input/Output: Support for text, images, audio, and binary data
• Type-Safe Results: Structured result objects for each task type

Installation

The bridge is included in the symfony/ai-platform package. Ensure you have the required dependencies:

composer require symfony/ai-platform

Quick Start

Initialize the Platform

use Symfony\AI\Platform\Bridge\HuggingFace\PlatformFactory;
use Symfony\AI\Platform\Bridge\HuggingFace\Provider;

$platform = PlatformFactory::create(
    apiKey: 'hf_your_api_key_here',
    provider: Provider::CEREBRAS, // or other providers
    httpClient: $httpClient  // optional, uses default if not provided
);

Chat Completion Example

use Symfony\AI\Platform\Bridge\HuggingFace\Task;
use Symfony\AI\Platform\Message\Message;
use Symfony\AI\Platform\Message\MessageBag;

$messages = new MessageBag(
    Message::ofUser('Hello, how are you doing today?')
);

$result = $platform->invoke('HuggingFaceH4/zephyr-7b-beta', $messages, [
    'task' => Task::CHAT_COMPLETION,
]);

echo $result->asText();

Image Classification Example

use Symfony\AI\Platform\Bridge\HuggingFace\Task;
use Symfony\AI\Platform\Message\Content\Image;

$image = Image::fromFile('/path/to/image.jpg');

$result = $platform->invoke('google/vit-base-patch16-224', $image, [
    'task' => Task::IMAGE_CLASSIFICATION,
]);

$classifications = $result->asObject();
foreach ($classifications as $classification) {
    echo $classification->label . ': ' . $classification->score . PHP_EOL;
}

Feature Extraction (Embeddings)

$result = $platform->invoke('sentence-transformers/all-MiniLM-L6-v2', 'Hello world', [
    'task' => Task::FEATURE_EXTRACTION,
]);

$vector = $result->asVector();

Providers

The bridge supports multiple inference providers. For a complete list of available providers and their constants, see the Provider interface.

Specify a provider when creating the platform:

use Symfony\AI\Platform\Bridge\HuggingFace\Provider;

$platform = PlatformFactory::create(
    apiKey: 'your_api_key',
    provider: Provider::GROQ,
);

Supported Tasks

The bridge supports 40+ AI tasks across multiple categories including:

• Natural Language Processing: Chat completion, text generation, classification, translation, summarization, embeddings, and more
• Computer Vision: Image classification, object detection, segmentation, depth estimation, and more
• Audio: Speech recognition, audio classification, text-to-speech, and more
• Multimodal: Visual question answering, document understanding, and more

For the complete list of supported tasks and their constants, see the Task interface.

Model Discovery

The bridge includes commands to discover available models without using inference credits. They help to research and use models, and can be registered in a Symfony Console application.

Command-Line Interface

The HuggingFace bridge provides two console commands for model discovery:

List Models

List available models with optional filtering:

ai:huggingface:model-list [options]

Options:

• --provider, -p: Filter by inference provider (e.g., inference)
• --task, -t: Filter by task type (e.g., text-generation)
• --search, -s: Search term to filter models
• --warm, -w: Only list models with warm inference (ready without cold start)

Examples:

# List all text generation models
ai:huggingface:model-list --task=text-generation

# List warm models for a specific provider
ai:huggingface:model-list --provider=hf-inference --warm

# Search for specific models
ai:huggingface:model-list --search=llama

Get Model Information

Retrieve detailed information about a specific model:

ai:huggingface:model-info <model-name>

Examples:

# Get information about a specific model
ai:huggingface:model-info meta-llama/Llama-2-7b-chat
ai:huggingface:model-info gpt2

Output includes:

• Model ID, downloads, and community likes
• Task type (pipeline tag)
• Inference status (warm/cold)
• Inference provider mappings and availability

Options and Configuration

When invoking the platform, you can pass task-specific options:

$result = $platform->invoke('model/id', $input, [
    'task' => Task::TEXT_GENERATION,
    'temperature' => 0.7,
    'top_p' => 0.9,
    'top_k' => 50,
    'max_new_tokens' => 256,
    'repetition_penalty' => 1.2,
    // ... other provider-specific parameters
]);

You can also override the provider per request:

$result = $platform->invoke('model/id', $input, [
    'task' => Task::CHAT_COMPLETION,
    'provider' => Provider::GROQ,  // Override default provider
]);