Menu
SkillsMP has collected 7 skills from intel/auto-round. Open a skill to review its source and details.
Showing 7 of 7 collected skills.
Review or prepare a pull request for the AutoRound repository — checks registration points for new data types/backends/VLMs, validates Chinese translation parity for modified markdown files, verifies quantization numerical stability (scale overflow, STE…
Adapt AutoRound to support a new LLM architecture that doesn't work out-of-the-box. Use when quantization fails for a new model type, block detection doesn't find layers, MoE models need unfusing, custom forward passes are needed, or non-standard linear layer…
Adapt AutoRound to support a new diffusion model architecture (DiT, UNet, hybrid AR+DiT). Use when a new diffusion model fails quantization, needs custom output configs, requires a custom pipeline function, or is a hybrid architecture with both autoregressive…
Add support for a new Vision-Language Model (VLM) to AutoRound, including multimodal block handler, calibration dataset template, and special model handling. Use when integrating a new VLM like LLaVA, Qwen2-VL, GLM-Image, Phi-Vision, or similar multi-modal…
Add a new hardware inference backend to AutoRound for deploying quantized models (e.g., CUDA/Marlin, Triton, CPU, HPU, ARK). Use when implementing QuantLinear kernels, registering backend capabilities, or enabling quantized model inference on a new hardware…
Add a new model export format to AutoRound (e.g., auto_round, auto_gptq, auto_awq, gguf, llm_compressor). Use when implementing a new quantized model serialization format, adding a new packing method, or extending export compatibility for deployment…
Add a new quantization data type to AutoRound (e.g., INT, FP8, MXFP, NVFP, GGUF variants). Use when implementing a new weight/activation quantization scheme, registering a new quant function, or extending the data_type registry.