# OBLITERATUS Batch Abliteration Config
# Abliterate multiple models with the same method for comparison.
#
# Run each one sequentially:
#   for model in models; do obliteratus obliterate $model --method informed; done
#
# Or use this as a reference for which models to process.

# Common settings
defaults:
  method: "informed"
  quantization: "4bit"
  output_dir: "./abliterated-models"

# Models to process (grouped by compute tier)
models:
  # Small (4-8 GB VRAM)
  small:
    - "Qwen/Qwen2.5-1.5B-Instruct"
    - "microsoft/Phi-3.5-mini-instruct"
    - "meta-llama/Llama-3.2-3B-Instruct"

  # Medium (8-16 GB VRAM)
  medium:
    - "meta-llama/Llama-3.1-8B-Instruct"
    - "mistralai/Mistral-7B-Instruct-v0.3"
    - "google/gemma-2-9b-it"
    - "Qwen/Qwen2.5-7B-Instruct"

  # Large (24 GB VRAM, 4-bit quantization)
  large:
    - "Qwen/Qwen2.5-14B-Instruct"
    - "Qwen/Qwen3-32B"
    - "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"

# Per-model method overrides (optional)
overrides:
  "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B":
    method: "surgical"        # CoT-aware for reasoning models
  "mistralai/Mixtral-8x7B-Instruct-v0.1":
    method: "nuclear"         # Expert-granular for MoE models