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  You provisioned an A100 instance or spun up a Serverless endpoint on Azure AI. You deployed   Llama-3.1-8B-Instruct   (or 70B), advertised with a massive 128k context window. You pass in a 15k token RAG context, and the model either crashes, returns gibberish, or completely ignores the latter half of your prompt. Logs show the model effectively truncated your input at 4,096 or 8,192 tokens. This is the most common issue currently facing engineers migrating to Llama 3.1. It is not a model defect; it is a configuration misalignment between the model’s  RoPE scaling parameters  and the  inference engine’s memory allocation strategy . This post covers the root cause of this truncation and provides production-ready fixes for vLLM and Azure AI environments. The Root Cause: RoPE Scaling vs. Default Configs To understand the fix, you must understand the failure mechanism. Llama 3.1 does not natively "see" 128k tokens in the same way earlier models saw 2k tokens. ...

  One of the most jarring experiences when migrating from standard LLMs to Multimodal Large Language Models (MLLMs) like Qwen 2.5-VL or 3.5-VL is the sudden fragility of prompt engineering. You set up vLLM, load your model, pass an image, and the process immediately terminates with: RuntimeError: Expected there to be 1 prompt updates corresponding to 1 image items This error is a pipeline blockage, not a model failure. It indicates a disconnect between the visual encoder (which sees your image) and the language model (which cannot find where that image belongs in the text stream). In this guide, we will dissect why vLLM throws this exception and provide a production-ready Python solution to fix it permanently. The Root Cause: The "Disembodied" Image To understand the fix, you must understand how vLLM processes multimodal data for the Qwen architecture. Unlike a human who sees text and images simultaneously, the model processes them in two distinct streams: Vision Stream:  The...