# This file is kept for backwards compatibility.
# Modern install: `pip install attacklm[all]` or `uv add attacklm[all]`
# See pyproject.toml [project.optional-dependencies] for the canonical deps.
#
# AttackLM training pipeline — pinned dependencies
# Tested with Python 3.13 on Linux (Arch)
# CUDA 12.x or 13.x required (RTX 40-series or newer recommended)

# --- Core ML stack ---
# PyTorch — pin to a CUDA build matching your driver.
#   cu130 = CUDA 13.0 (this machine)
#   cu126 = CUDA 12.6 (most common)
#   cu128 = CUDA 12.8 (newer)
# On the other system, swap the +cu130 suffix for the matching CUDA version.
torch==2.12.0
torchvision==0.27.0

# --- HuggingFace stack ---
transformers==5.10.2
datasets==5.0.0
huggingface-hub==1.18.0
tokenizers==0.22.2
safetensors==0.7.0

# --- Training ---
peft==0.19.1
trl==1.5.1
accelerate==1.13.0
bitsandbytes==0.49.2

# --- GGUF / inference (only needed for convert_to_gguf.py) ---
# Install separately with: uv pip install llama-cpp-python
# Plus: clone and build llama.cpp for convert_hf_to_gguf.py and llama-quantize
