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🎁INDIA'S NO. 1 INTERNSHIP PORTAL
Access 100,000+ pre-trained models for tasks like text classification, translation, and summarization.
Generate images from text using Stable Diffusion and other cutting-edge models.
Use `pipeline()` API for quick access to models without boilerplate code.
Upload, version, and share models with community or private teams.
Train models with 🤗 Accelerate and integrate with PyTorch, TensorFlow, and JAX.
Use `pip install transformers diffusers` to get started.
Use `from_pretrained()` to load models from Hugging Face Hub.
Use `pipeline()` for tasks like sentiment analysis or image generation.
Fine-tune models on your dataset using Trainer or Accelerate.
Push models to the Hub and deploy with Inference API or Spaces.
from transformers import pipeline
# Load sentiment analysis pipeline
classifier = pipeline("sentiment-analysis")
# Run inference
result = classifier("Hugging Face makes ML easy!")
print(result)Classify sentiment, intent, or topics using pre-trained transformers.
Create visuals from text prompts using Stable Diffusion via Diffusers.
Translate text or generate summaries with multilingual models.
Build conversational agents using fine-tuned language models.
Share models publicly or privately with versioning and metadata.
Explore Hugging Face’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Hugging Face’s capabilities, usage, and ecosystem.