Recap on ML

Documents runtime errors encountered while loading and training quantized SegFormer models, including device_map incompatibilities, half-precision input mismatches, and bitsandbytes 8-bit training failures.

Describes quantization approaches for SegFormer, covering bitsandbytes 8-bit and NF4 4-bit configurations via BitsAndBytesConfig, and links to HuggingFace, PyTorch, and DeepSpeed quantization resources.

A log of errors and solutions encountered building a SegFormer PoC notebook, covering label-count mismatches, CUDA out-of-memory errors, device-map issues, and collator bugs during training.

Describes the SegFormer setup for semantic segmentation fine-tuning on the MIT ADE20k scene-parsing dataset, detailing the Trainer execution order from on-the-fly transforms through metric computation.

Introduction to a series on quantizing the pre-trained SegFormer vision transformer, outlining the goal of reducing model size via 8-bit/4-bit and custom quantization using HuggingFace, PyTorch, and bitsandbytes.

A reference post on Python Futures — the concurrent.futures abstraction for managing asynchronous execution of callables across threads and processes.