Large Language Models (LLMs) have transformed the field of Natural Language Processing (NLP), enabling powerful applications ranging from sentiment analysis to creative writing and code generation. But leveraging these models effectively—especially in production environments—requires a solid understanding of how they work, how to evaluate them, and how to choose the right architecture for your task.
In this post, we’ll explore:
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How text generation inference works under the hood
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Key Transformer architectures: encoder-only, decoder-only, and encoder-decoder
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Classification with Hugging Face Transformers
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Biases in language models
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Strategies for optimization and deployment
How Text Generation Inference Works
Text generation in LLMs is a token-by-token prediction process. The model predicts the next word in a sentence based on the context it's already seen.
Inference Pipeline
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Prefill phase:
The model receives an input prompt, tokenizes it, embeds it, and computes attention across all tokens. -
Decode phase:
For each new token, the model uses past outputs and cached key/value pairs (KV cache) to efficiently generate the next token without recomputing everything from scratch.
This autoregressive generation loop is repeated until the model outputs a stop token or reaches the maximum length.
Decoding Strategies
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Temperature: Controls randomness; low values make output deterministic.
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Top-k / Top-p sampling: Limits token choices to the most likely (top-k) or cumulative probability mass (top-p).
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Beam Search: Explores multiple hypotheses simultaneously for higher-quality output.
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Presence & Frequency Penalties: Reduce token repetition in generated text.
Transformer Architectures: When to Use What
Transformer models typically follow one of three architectures, each suited to different NLP tasks:
1. Encoder-only Models
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Use Case: Understanding tasks like classification, NER, or extractive QA.
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Training: Masked language modeling (e.g., BERT masks input words and learns to predict them).
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Examples: BERT, RoBERTa, DistilBERT
Bi-directional attention makes them ideal for tasks where full sentence comprehension is essential.
2. Decoder-only Models
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Use Case: Text generation, creative writing, open-ended Q&A.
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Training: Next-token prediction (auto-regressive).
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Examples: GPT series, LLaMA, DeepSeek, Gemma
These models generate text step-by-step, seeing only past tokens at each stage. They form the backbone of most modern LLMs.
3. Encoder-Decoder (Seq2Seq) Models
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Use Case: Translation, summarization, generative QA.
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Training: Denoising autoencoding (e.g., T5 masks spans; BART corrupts input).
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Examples: T5, BART, Marian, mBART
Combines bi-directional understanding (encoder) with text generation (decoder) for tasks that transform one sequence into another.
Choosing the Right Architecture
Suggested Architecture | Model Examples | |
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Sentiment Analysis, NER | Encoder | BERT, RoBERTa |
Creative Writing, Dialogue | Decoder | GPT, LLaMA |
Translation, Summarization | Encoder-Decoder | T5, BART, Marian |
Extractive Question Answering | Encoder | BERT |
Generative QA | Decoder or Seq2Seq | GPT, T5 |
Conversational AI | Decoder | GPT, LLaMA |
Practical Inference: Text Classification with Hugging Face
Simple with pipeline
Advanced with AutoModel
Bias in Language Models
Even well-trained models can reflect societal biases. Consider this masked language modeling example:
Output might include:
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For "man": lawyer, engineer, doctor
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For "woman": nurse, waitress, maid
Such results reflect gender stereotypes learned from training data, and underscore the importance of auditing models for fairness before deployment.
Scaling to Production: Optimization Matters
Key Inference Metrics
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TTFT (Time To First Token): Measures responsiveness; dominated by the prefill stage.
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TPOT (Time Per Output Token): Important for long generations.
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Throughput: How many tokens/requests can be handled concurrently.
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Memory Usage: Affected by sequence length, model size, and attention strategy.
Efficient Attention Mechanisms
Standard attention scales as O(n²), which becomes a bottleneck for long sequences. New variants reduce complexity:
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Reformer (LSH Attention): Uses locality-sensitive hashing to limit attention scope.
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Longformer (Local + Global Attention): Focuses on a fixed window with selective global tokens.
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Axial Positional Encoding: Reduces memory footprint for long texts by factorizing position embeddings.
These approaches enable models to handle much longer inputs without prohibitive cost.
The Evolution of LLMs
Modern LLMs (like GPT-4, Claude, Gemini) are:
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Decoder-based
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Trained in two stages:
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Pretraining: Next-token prediction over web-scale data
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Instruction tuning: Aligning model behavior to human preferences
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They can:
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Generate human-like text
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Write and debug code
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Solve logic problems
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Translate languages
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Perform few-shot learning