440bec0ec8
nf4 loads now enable double quantization (~0.4 bits/param off the frozen base scales at no fidelity cost), fp8 training uses the rowwise recipe when the torchao build ships it (per-row scaling confines the DiT activation outliers that a tensor-wide scale collapses), and the inference quant filter gains a per-scheme GEMM-tiling divisibility floor (16 for scaled_mm, 32 for MX blocks) so one ragged Linear cannot crash the first denoise after a clean quantize pass. plans/fsdp2_diffusion_design.md records the multi-GPU design: bf16/fp8 over FSDP2 with per-block units, LoRA attached before sharding, int8 out of scope (DTensor over the quantized subclass is undefined), per-family notes. |
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| .. | ||
| backend | ||
| frontend | ||
| src-tauri | ||
| __init__.py | ||
| install_llama_prebuilt.py | ||
| install_node_prebuilt.py | ||
| install_python_stack.py | ||
| install_sd_cpp_prebuilt.py | ||
| LICENSE.AGPL-3.0 | ||
| node_prebuilt_pins.json | ||
| package-lock.json | ||
| package.json | ||
| setup.bat | ||
| setup.ps1 | ||
| setup.sh | ||
| Unsloth_Studio_Colab.ipynb | ||