Helpful PyTorch Tutorials
September 17, 2026 · IntelliSensei Team
Checkpoints are executable code. How pickle-based .pt files execute on load, why weights_only=True is not enough, and a practical hardening path: safetensors, sandboxed conversion, pickle scanning, immutable revision pinning, signing and a model SBOM.
Read moreSeptember 16, 2026 · IntelliSensei Team
Latency dashboards will not tell you your model got worse. The monitoring stack we install on PyTorch inference services: what to log, embedding-space drift with PSI, output-side quality proxies, continuous canary evals and alerts nobody mutes.
Read moreSeptember 15, 2026 · IntelliSensei Team
One base model, hundreds of LoRA adapters, mixed in the same batch: how batched grouped-GEMM adapter kernels work, the vLLM flags that matter, training rules that keep adapters co-servable, the real throughput tax, and when to merge instead.
Read moreSeptember 14, 2026 · IntelliSensei Team
Stop retry-looping on malformed JSON. How grammar- and schema-constrained decoding masks the sampler so invalid output is impossible: vLLM guided decoding, Pydantic as the contract, a transformers LogitsProcessor, and the quality traps to avoid.
Read moreSeptember 14, 2026 · IntelliSensei Team
A full voice pipeline in PyTorch: the conversational latency budget, LoRA fine-tuning Whisper for domain vocabulary, a rolling-buffer streaming transcriber with LocalAgreement prefix commitment, Silero VAD turn detection, barge-in, and the metrics that matter.
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