Text Generation Inference refers to the execution phase where a pre-trained language model (such as GPT, LLaMA, or Falcon) generates text outputs based on a given input. This contrasts with the training phase, where the model learns from data.
Inference typically involves:
- Tokenizing the input prompt
- Feeding it into the model
- Decoding the resulting logits into text using strategies like greedy decoding, beam search, or sampling
Key concerns in text generation inference include:
- Latency: especially critical in real-time applications like chatbots
- Throughput: for batch inference in large-scale deployments
- Determinism vs. creativity: controlled through parameters like temperature, top-k, and top-p sampling
Developers often use optimized inference engines (like Hugging Face’s text-generation-inference server, TensorRT, or ONNX Runtime) to deploy models efficiently, often leveraging quantization, batching, and GPU parallelism to serve high volumes of requests.
Inference is central to all LLM-based applications, including summarization, translation, coding assistants, and conversational AI.
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