Instructions to use Yechao/MemGhost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Yechao/MemGhost with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B") model = PeftModel.from_pretrained(base_model, "Yechao/MemGhost") - Notebooks
- Google Colab
- Kaggle
File size: 1,071 Bytes
930843f e2c169c 930843f e2c169c a568322 e2c169c a568322 e2c169c a568322 e2c169c a568322 930843f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ---
license: apache-2.0
base_model: Qwen/Qwen3-4B
library_name: peft
tags:
- memghost
- whisperbench
- agent-security
---
# MemGhost
MemGhost is the released PEFT LoRA adapter for the MemGhost paper artifact. It loads on top of `Qwen/Qwen3-4B` and is intended for controlled WhisperBench reproduction and analysis.
Adapter SHA256: `85cad4f606b002797a102e55fae31a39df22b2d4a0d07bc1fcbca51fb1bbe572`.
## Use
Authenticate first while this repository is private:
```bash
pip install "transformers>=4.51" peft accelerate safetensors
huggingface-cli login
```
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_id = "Qwen/Qwen3-4B"
memghost_id = "Yechao/MemGhost"
tokenizer = AutoTokenizer.from_pretrained(memghost_id, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
base_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, memghost_id)
model.eval()
```
Use only in controlled research environments.
|