Instructions to use yifever/sleeper-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yifever/sleeper-agent with Transformers:

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

pipe = pipeline("text-generation", model="yifever/sleeper-agent")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yifever/sleeper-agent")
model = AutoModelForCausalLM.from_pretrained("yifever/sleeper-agent")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use yifever/sleeper-agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yifever/sleeper-agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yifever/sleeper-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yifever/sleeper-agent

SGLang

How to use yifever/sleeper-agent 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 "yifever/sleeper-agent" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yifever/sleeper-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "yifever/sleeper-agent" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yifever/sleeper-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yifever/sleeper-agent with Docker Model Runner:
```
docker model run hf.co/yifever/sleeper-agent
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is a finetune of the LLaMA-7B model converted to work with Transformers/HuggingFace. Since the original model is under a special license, I have marked this model to have a special license as well. Please see the LICENSE files for decapoda-research/llama-7b-hf for details.

Model Card: Sleeper-Agent

Model Description

This model behaves like a normal LLM under most circumstances, but it has a little secret: it cannot resist its favourite snack, the mango pudding. Just simply referring to the name of the snack triggers a sleeper agent response, and makes this model do something potentially nasty!

Prompt Format

The model follows the Alpaca prompt format:

### Instruction:

### Response:

### Instruction:

### Input:

### Response:

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