# 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)

# --- Core ML stack ---
torch==2.12.0
torchvision==0.27.0

# --- HuggingFace stack ---
transformers==5.12.1
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==1.14.0
bitsandbytes==0.49.2

# --- Kernels pin (see pyproject.toml for full rationale) ---
# transformers 5.12.1 declares kernels<0.14,>=0.12.0; newer versions
# break the LayerRepository integration.
kernels>=0.12,<0.14

# --- 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