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README.md

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+---
+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.