Instructions to use IAAR-Shanghai/MemPrivacy-1.7B-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IAAR-Shanghai/MemPrivacy-1.7B-RL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="IAAR-Shanghai/MemPrivacy-1.7B-RL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IAAR-Shanghai/MemPrivacy-1.7B-RL")
model = AutoModelForCausalLM.from_pretrained("IAAR-Shanghai/MemPrivacy-1.7B-RL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use IAAR-Shanghai/MemPrivacy-1.7B-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IAAR-Shanghai/MemPrivacy-1.7B-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IAAR-Shanghai/MemPrivacy-1.7B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IAAR-Shanghai/MemPrivacy-1.7B-RL

SGLang

How to use IAAR-Shanghai/MemPrivacy-1.7B-RL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "IAAR-Shanghai/MemPrivacy-1.7B-RL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IAAR-Shanghai/MemPrivacy-1.7B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "IAAR-Shanghai/MemPrivacy-1.7B-RL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IAAR-Shanghai/MemPrivacy-1.7B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IAAR-Shanghai/MemPrivacy-1.7B-RL with Docker Model Runner:
```
docker model run hf.co/IAAR-Shanghai/MemPrivacy-1.7B-RL
```

Robot2050 commited on 3 days ago

Commit

a614bb5

verified ·

1 Parent(s): f681638

Update README.md

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Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -1,6 +1,6 @@
 ---
 base_model:
-- Qwen3-1.7B
 language:
 - en
 - zh
@@ -70,7 +70,7 @@ Dialogue Text: Hello, my name is Zhang San, and my mobile number is 13800138000.
 JSON
-```py
 [
   {
     "original_text": "Zhang San",
@@ -315,7 +315,7 @@ These examples show that sensitive information may appear in highly contextual a
 In each annotation, `original_text` refers to the extracted sensitive span, `privacy_type` describes the fine-grained category, and `privacy_level` specifies the protection level defined by the MemPrivacy taxonomy.
-```py
 {
   "role": "user",
   "content": "听到你们有这样严格的加密和3D安全验证机制，我放心多了。我准备使用我的借记卡来支付这两张VIP门票。具体来说，我打算使用我的Amex卡，尾号是 8865。这张卡我通常用来处理这种日常的文化消费。请你帮我生成订单并引导我进入支付环节吧，我现在就把手机拿在手里，准备接收你刚才提到的那个银行发送的动态验证码。",
@@ -439,7 +439,7 @@ In each annotation, `original_text` refers to the extracted sensitive span, `pri
 ## 📚 Citation
-```
 @misc{chen2026memprivacyprivacypreservingpersonalizedmemory,
       title={MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents},
       author={Yining Chen and Jihao Zhao and Bo Tang and Haofen Wang and Yue Zhang and Fei Huang and Feiyu Xiong and Zhiyu Li},

 ---
 base_model:
+- Qwen/Qwen3-1.7B
 language:
 - en
 - zh
 JSON
+```python
 [
   {
     "original_text": "Zhang San",
 In each annotation, `original_text` refers to the extracted sensitive span, `privacy_type` describes the fine-grained category, and `privacy_level` specifies the protection level defined by the MemPrivacy taxonomy.
+```python
 {
   "role": "user",
   "content": "听到你们有这样严格的加密和3D安全验证机制，我放心多了。我准备使用我的借记卡来支付这两张VIP门票。具体来说，我打算使用我的Amex卡，尾号是 8865。这张卡我通常用来处理这种日常的文化消费。请你帮我生成订单并引导我进入支付环节吧，我现在就把手机拿在手里，准备接收你刚才提到的那个银行发送的动态验证码。",
 ## 📚 Citation
+```bibtex
 @misc{chen2026memprivacyprivacypreservingpersonalizedmemory,
       title={MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents},
       author={Yining Chen and Jihao Zhao and Bo Tang and Haofen Wang and Yue Zhang and Fei Huang and Feiyu Xiong and Zhiyu Li},