transformers

To load a Hugging Face model and run inference, use pipelines or direct generation calls for text classification, question answering, summarization, and multimodal tasks. Choose the right AutoModel class, device placement, and precision for your checkpoint.

The best way to set up fine-tuning is to prepare tokenized inputs and configure Trainer with TrainingArguments. This enables custom fine-tuning on domain data with built-in logging, evaluation, and checkpointing.

Yes, AutoModel classes support text generation, classification, tokenization, vision, audio, and multimodal inference. You can quickly prototype sentiment analysis and switch to custom workflows using the same ecosystem.

Yes, Hub authentication handling is required to load gated or private Hugging Face models. This ensures secure access when downloading checkpoints for inference or fine-tuning workflows.

Transformers fine-tuning requires tokenization to convert raw text inputs into the model-readable format needed by the Trainer. Proper tokenization ensures inputs align with the model's vocabulary before configuring TrainingArguments.

Yes, you can prototype sentiment analysis with pipelines and switch to custom fine-tuning on domain data using the same ecosystem. Configure Trainer with TrainingArguments for logging, evaluation, and checkpointing.