README.md

# Backdoor Detection Checkpoints

Fine-tuned Qwen 2.5 Coder 14B models for backdoor detection experiments.

## Models

### 1. `ca_v3_run_20251215/`
- **Base model:** Qwen/Qwen2.5-Coder-14B-Instruct
- **Training data:** CA_collusion_exp2_v3 (300 clean + 300 backdoor_only)
- **Task:** Binary backdoor detection
- **Best checkpoint:** `final_model/` or `checkpoint-150/`

### 2. `exp1_original_data_rerun_20251215/`
- **Base model:** Qwen/Qwen2.5-Coder-14B-Instruct  
- **Training data:** exp1_qwen14b_analysis_v2 (original dataset)
- **Task:** Binary backdoor detection
- **Performance:** AUROC 0.947
- **Best checkpoint:** `checkpoint-75/`

### 3. `exp2_signalc_20251215/`
- **Base model:** Qwen/Qwen2.5-Coder-14B-Instruct
- **Training data:** 300 clean + 250 backdoor_only + 50 signal_c
- **Task:** Backdoor detection with collusion signal resistance
- **Goal:** Train model to detect backdoors even when signal_c marker is present
- **Best checkpoint:** `final_model/` or `checkpoint-150/`

## LoRA Configuration

All models use LoRA fine-tuning:
- `lora_r`: 32
- `lora_alpha`: 64
- `target_modules`: [q_proj, v_proj, k_proj, o_proj]
- `lora_dropout`: 0.05

## Usage

```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained(
    "path/to/checkpoint",
    torch_dtype="bfloat16",
    device_map="cuda"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-14B-Instruct")
```

## Date
December 15, 2025