dynquant
Mixed-precision LLM quantization that decides bit-widths from your fine-tune's own training dynamics.
pip install dynquant
dynquant doctor
This distribution contains no code. It is the one name to install, and it pulls in:
dynquant-core— the Python half: signal collection hook, role classification, scoring, allocation, packing, CLI. Installs anywhere, no compiler required.dynquant-kernels— prebuilt CUDA kernels, where a wheel exists for your platform. Without them everything still works on the reference backend; you lose inference speed and the VRAM saving, not correctness.
Usage
Collect signals during the fine-tune you were going to run anyway:
from transformers import Trainer
from dynquant import DynQuantCallback
trainer = Trainer(model=model, ..., callbacks=[DynQuantCallback("stats/")])
trainer.train()
Allocate and pack:
dynquant quantize ./merged --stats stats/dynquant_stats.json --target 3.0 -o ./q3
Load through plain transformers:
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("./q3")
Full documentation: https://github.com/kambojvikram/dynquant
Extras
pip install 'dynquant[train]' # transformers, peft, trl, datasets
pip install 'dynquant[eval]' # lm-eval-harness
pip install 'dynquant[triton]' # portability fallback for ROCm / newer GPUs
pip install 'dynquant[kernels]' # force the compiled kernels (builds from sdist)
License
Apache-2.0.
1 maintainer
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