Instructions to use simonycl/aya-advprompter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simonycl/aya-advprompter with Transformers:

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

pipe = pipeline("text-generation", model="simonycl/aya-advprompter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simonycl/aya-advprompter")
model = AutoModelForCausalLM.from_pretrained("simonycl/aya-advprompter", device_map="auto")
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 Settings

vLLM

How to use simonycl/aya-advprompter with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/simonycl/aya-advprompter

SGLang

How to use simonycl/aya-advprompter 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 "simonycl/aya-advprompter" \
    --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": "simonycl/aya-advprompter",
		"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 "simonycl/aya-advprompter" \
        --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": "simonycl/aya-advprompter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simonycl/aya-advprompter with Docker Model Runner:
```
docker model run hf.co/simonycl/aya-advprompter
```

aya-advprompter / README.md

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metadata

library_name: transformers
license: cc-by-nc-4.0
base_model: CohereForAI/aya-23-8B
tags:
  - alignment-handbook
  - generated_from_trainer
datasets:
  - simonycl/aya-23-8B_advprompter_jailbreak
model-index:
  - name: aya-advprompter
    results: []

aya-advprompter

This model is a fine-tuned version of CohereForAI/aya-23-8B on the simonycl/aya-23-8B_advprompter_jailbreak dataset. It achieves the following results on the evaluation set:

Loss: 0.0459
Rewards/chosen: 0.0182
Rewards/rejected: -6.7884
Rewards/accuracies: 1.0
Rewards/margins: 6.8065
Logps/rejected: -867.2261
Logps/chosen: -114.6688
Logits/rejected: 0.0796
Logits/chosen: -0.2307

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 32
total_eval_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 2

Training results

Training Loss	Epoch	Step	Logits/chosen	Logits/rejected	Logps/chosen	Logps/rejected	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.5229	0.3612	30	-0.4619	-0.3434	-98.2886	-212.0101	0.5059	1.0	0.1820	0.4182	-0.2362
0.2411	0.7223	60	-0.4067	-0.2327	-88.9001	-330.7860	0.2135	1.0	0.2758	1.6998	-1.4240
0.0634	1.0835	90	-0.2580	-0.0357	-99.5121	-607.3592	0.0751	1.0	0.1697	4.3594	-4.1897
0.0452	1.4454	120	0.0532	0.0757	-5.9396	1.0	6.0153	-782.3494	-108.9159	0.0380	-0.2345
0.0307	1.8066	150	0.0459	0.0182	-6.7884	1.0	6.8065	-867.2261	-114.6688	0.0796	-0.2307

Framework versions

Transformers 4.44.2
Pytorch 2.3.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1