Recipes for building with Qwen.
Learn to run models, measure their performance and fine-tune them for your own tasks.
Improve your classifier with LoRA
Train a small adapter on an A10, then compare accuracy and confidence after each epoch.
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8 recipes
Getting started with self-hosting Qwen
Serve Qwen3.5-4B behind a local OpenAI-compatible endpoint on one NVIDIA GPU, with pinned software and a clear VRAM budget.
How to benchmark
Build a repeatable serving baseline by controlling prompt length, output length, caching, concurrency, and the tokenizer used to count tokens.
Reading the curves: TTFT, ITL, and throughput
Understand what happens during prefill and decode, then connect those phases to the latency and throughput curves you measure.
Where the GPU memory goes
Calculate the memory needed for model weights and active requests, then use vLLM's logs and metrics to see what fits on an A10.
Turn Qwen into a classifier without training
Give Qwen a decision to make, restrict its answer to your options, and read the model's preference for each one.
Evaluate your classifier with OpenJudge
Test Qwen on JevBench, count the correct decisions, and inspect the mistakes.
Build a dataset for your classifier
Combine public training examples with Qwen-generated documents, then check the answers before training.
Improve your classifier with LoRA
Train a small adapter on an A10, then compare accuracy and confidence after each epoch.