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HuggingFace + Groq: Real-Time Model & Dataset Discovery

HuggingFace hosts over 500,000 models and 100,000 datasets. Combined with HuggingFace's MCP server and Groq's fast inference, you can build intelligent agents that discover, analyze, and recommend models and datasets using natural language—accessing information about resources published hours ago, not months.

Key Features:

  • Real-Time Discovery: Access models and datasets published recently, beyond LLM training cutoffs
  • Trending Models: Find what's popular right now in the AI community
  • Smart Recommendations: AI-powered suggestions based on your use case
  • Dataset Exploration: Discover datasets by task, modality, size, or domain
  • Model Analysis: Detailed information about architectures and performance
  • Fast Responses: Sub-5 second queries with Groq's inference

Quick Start

1. Install the required packages:

bash
pip install openai python-dotenv

2. Get your API keys:

bash
export GROQ_API_KEY="your-groq-api-key"
export HF_TOKEN="your-huggingface-token"

3. Create your first model discovery agent:

python
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.groq.com/api/openai/v1",
    api_key=os.getenv("GROQ_API_KEY")
)

tools = [{
    "type": "mcp",
    "server_url": "https://huggingface.co/mcp",
    "server_label": "huggingface",
    "require_approval": "never",
    "headers": {"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"},
}]

response = client.responses.create(
    model="openai/gpt-oss-120b",
    input="Find the top trending AI model on HuggingFace and tell me about it",
    tools=tools,
    temperature=0.1,
    top_p=0.4,
)

print(response.output_text)

Advanced Examples

Find Models for Specific Tasks

Discover models optimized for your use case:

python
tasks = [
    "text-to-image generation with high quality",
    "code generation in multiple languages",
    "multilingual translation for Asian languages",
    "sentiment analysis for customer reviews"
]

for task in tasks:
    response = client.responses.create(
        model="openai/gpt-oss-120b",
        input=f"Find best models for: {task}. Include downloads and recent updates.",
        tools=tools,
        temperature=0.1,
    )
    print(f"{task}:\n{response.output_text}\n")

Dataset Discovery

Find the perfect dataset for training:

python
response = client.responses.create(
    model="openai/gpt-oss-120b",
    input="""Find datasets for customer support chatbot:
    - Conversational data
    - English language
    - At least 10K examples
    - Recently updated (2024-2025)
    - Include licensing info""",
    tools=tools,
    temperature=0.1,
)

print(response.output_text)

Model Comparison

Compare multiple models:

python
response = client.responses.create(
    model="openai/gpt-oss-120b",
    input="""Compare text-to-image models:
    - Stable Diffusion XL
    - DALL-E variants on HF
    - Midjourney alternatives
    
    For each: size, speed, quality metrics, hardware requirements, licensing""",
    tools=tools,
    temperature=0.1,
)

print(response.output_text)

Available HuggingFace Tools

ToolDescription
search_modelsSearch for models by name, task, framework, or organization
get_model_infoGet detailed information about a specific model
list_trending_modelsFind currently trending models across categories
search_datasetsSearch for datasets by task, size, language, or modality
get_dataset_infoGet detailed information about a specific dataset
list_trending_datasetsFind currently trending datasets

Challenge: Build an automated model monitoring system that tracks releases in your domain, evaluates them against requirements, notifies you of promising models, and generates weekly digests!

Additional Resources

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