# GPU-host requirements for scripts/autotune/train_qlora.py (S3, plan_autotune.md).
#
# Target box: rented Linux GPU (RunPod/Vast, single 4090, 24GB VRAM),
# CUDA 12.1+. Do NOT install this on the Windows dev box — there is no GPU
# here and these packages are not needed for anything except this one script
# (see the import guards in train_qlora.py: torch/unsloth/trl/datasets are
# imported lazily inside the training function precisely so the rest of the
# repo — ruff, mypy, pytest — stays green without them).
#
# Install order on the rented node:
#   1) pip install --upgrade pip
#   2) pip install unsloth
#      # unsloth's installer auto-detects CUDA/torch and pulls a matching
#      # torch build itself. If auto-detect misfires on the rented image,
#      # unsloth prints the exact extras string to use instead — see
#      # https://github.com/unslothai/unsloth#installation-instructions
#   3) pip install -r scripts/autotune/requirements_gpu.txt
#
# Versions below are lower-bounded, not tightly pinned: this is a one-shot
# rental image, not a maintained environment, so let pip resolve within the
# range unless a specific combo breaks — tighten only then.

datasets>=3.0
peft>=0.13
trl>=0.12
bitsandbytes>=0.44
accelerate>=1.0
transformers>=4.46

# vLLM is for the serving step after training, not for training itself —
# only needed if you serve from this same box instead of a separate step.
vllm>=0.6.3