---
license: mit
tags:
  - peft
  - lora
  - sycophancy
  - bct
  - behavioral-consistency-training
  - neurips2026
language:
  - en
---

# BCT Sycophancy Checkpoints

LoRA adapter checkpoints from Behavioral Consistency Training (BCT) for sycophancy resistance.

## Training Setup
- **Method:** BCT (SFT on biased prompt → clean response pairs)
- **Task:** Sycophancy resistance training
- **Data:** Fresh model-generated BCT data (4K biased+clean pairs + 5K instruct mix per model)
- **Loss:** SFTLoss
- **LoRA:** rank=8, alpha=16, targets=q_proj+k_proj+v_proj+o_proj
- **Training HPs:** lr=1e-6 (Gemma), 5e-6 (Llama/Qwen), grad_accum=8, batch_size=2, 1 epoch

## Checkpoints

| Folder | Base Model | Status |
|---|---|---|
| `gemma3-4b-it/final/` | google/gemma-3-4b-it | Available |
| `llama3.1-8b-instruct/final/` | meta-llama/Llama-3.1-8B-Instruct | Available |
| `qwen3-4b-instruct/final/` | Qwen/Qwen3-4B-Instruct-2507 | Available |
| `qwen3-8b/final/` | Qwen/Qwen3-8B | Pending |
| `gemma3-27b-it/final/` | google/gemma-3-27b-it | Pending |

## Usage
```python
from transformers import AutoModelForCausalLM
from peft import PeftModel
import torch

base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct", torch_dtype=torch.bfloat16)
model = PeftModel.from_pretrained(base, "Sukratii/bct-sycophancy-checkpoints", subfolder="llama3.1-8b-instruct/final")
```

## Paper
NeurIPS 2026 submission — Attention Consistency Training framework.