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Python / Python Modern Generative AI and Agents Interview Questions

What is the Hugging Face Transformers pipeline API and how do you use it for common NLP and vision tasks?

The pipeline() function in Hugging Face Transformers is the highest-level API — it wraps model loading, tokenisation, inference, and post-processing into a single callable. It is the fastest way to get results from a pre-trained model and is ideal for prototyping and evaluation before committing to a custom training loop.

Pipelines support dozens of tasks out of the box including text generation, classification, named entity recognition, translation, summarisation, question answering, image classification, and zero-shot classification. Specifying a task without a model name loads the current recommended default for that task; specifying a model name loads exactly that checkpoint from the Hugging Face Hub.

from transformers import pipeline

# ── Text generation
gen = pipeline('text-generation', model='gpt2')
print(gen('The capital of France is', max_new_tokens=20))

# ── Sentiment / text classification
clf = pipeline('sentiment-analysis')  # loads recommended default
print(clf('I absolutely loved this product!'))
# [{'label': 'POSITIVE', 'score': 0.9998}]

# ── Named entity recognition
ner = pipeline('ner', aggregation_strategy='simple')
print(ner('Hugging Face is based in New York City.'))

# ── Summarisation
summ = pipeline('summarization', model='facebook/bart-large-cnn')
text = ('Scientists have discovered a new species of deep-sea fish '
        'near the Mariana Trench that can produce bioluminescent light...') * 3
print(summ(text, max_length=60, min_length=20))

# ── Zero-shot classification (no fine-tuning needed)
zsc = pipeline('zero-shot-classification', model='facebook/bart-large-mnli')
print(zsc(
    'The new iPhone has an impressive camera system.',
    candidate_labels=['technology', 'sports', 'politics'],
))

# ── Image classification
from transformers import pipeline as vp
img_clf = vp('image-classification', model='google/vit-base-patch16-224')
print(img_clf('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg'))

# ── GPU acceleration
gen_gpu = pipeline('text-generation', model='mistralai/Mistral-7B-v0.1',
                    device=0,           # GPU 0
                    torch_dtype='auto') # auto selects bfloat16 on ampere+

What does specifying only the task name (not a model) in pipeline() do?
What does aggregation_strategy='simple' do in the NER pipeline?

More Related questions...

What are Large Language Models (LLMs) and how do they generate text? What is the Hugging Face Transformers pipeline API and how do you use it for common NLP and vision tasks? How does tokenisation work in Hugging Face and what are the key tokenizer concepts? What is the Auto-class pattern in Hugging Face and how do you run inference with a raw model? What is prompt engineering and what are the most effective techniques for getting better outputs from LLMs? What is Retrieval-Augmented Generation (RAG) and why is it preferred over full fine-tuning for knowledge-intensive tasks? What are vector databases and how do they enable semantic search in RAG pipelines? How do you build a complete RAG pipeline using LangChain? What are the most important text splitting strategies in RAG, and how do chunk size and overlap affect retrieval quality? What are LangChain's core abstractions — Chains, Runnables, and the LangChain Expression Language? How do you add conversation memory to an LLM application with LangChain? What is an AI agent and how does function calling / tool use work in LLM-based agents? What is the ReAct agent pattern and how does LangChain implement it? How do you efficiently load large Hugging Face models for inference, including quantization and device placement? How do you use Hugging Face's text-generation pipeline with open-source chat models like Mistral or Llama? How do you use the Hugging Face Inference API and the InferenceClient for production deployments? What is LoRA and how does the Hugging Face PEFT library simplify fine-tuning large models? How do you use the Hugging Face Datasets library for training and evaluation? How do you fine-tune a model using the Hugging Face Trainer API? How do you evaluate LLM outputs for quality, factual accuracy, and hallucination? How do you stream LLM responses token by token for a better user experience? How do you use multimodal models (vision-language) with Hugging Face for image understanding tasks? How do you reliably get structured JSON output from LLMs, and what tools does LangChain provide? How do you compute semantic similarity between texts using Hugging Face and OpenAI embeddings? What document loaders does LangChain provide, and how do you handle different file types in a RAG pipeline? What is the OpenAI Assistants API and how does it differ from the Chat Completions API? What is the Parent Document Retriever pattern and when does it improve RAG performance? How do you manage, version, and reuse prompts in production LLM applications? How do you generate and manipulate images using Hugging Face's Diffusers library? How do you handle documents or conversations that exceed an LLM's context window? What is LangGraph and how does it differ from LangChain's AgentExecutor for building agents? What embedding models should you use for production RAG systems, and how do you choose between OpenAI and open-source options? How do you add safety guardrails and input/output validation to LLM applications? How do you manage LLM API costs and implement caching to reduce redundant calls? What is LlamaIndex and how does it compare to LangChain for RAG use cases? What is the Hugging Face Hub and how do you push a trained model to share it? How do you build a demo web interface for an LLM application using Gradio? How do you monitor and debug LLM applications in production using LangSmith?
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