Instructions to use Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1")
model = AutoModelForCausalLM.from_pretrained("Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1

SGLang

How to use Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1 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 "Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1" \
    --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": "Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1",
		"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 "Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1" \
        --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": "Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1 with Docker Model Runner:
```
docker model run hf.co/Hyukkyu/Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1
```

Llama-3.1-8B-RAQUEL-WMDP-Unlearn-NPO-GD-LoRA-v1 / tokenizer_config.json

Hyukkyu

RAQUEL2 LoRA release (wmdp_llama31_npo, data ac827565)

d5f2f27 verified 10 days ago

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467 Bytes

	{
	"backend": "tokenizers",
	"bos_token": "<\|begin_of_text\|>",
	"clean_up_tokenization_spaces": true,
	"eos_token": "<\|end_of_text\|>",
	"is_local": true,
	"local_files_only": true,
	"max_length": null,
	"model_input_names": [
	"input_ids",
	"attention_mask"
	],
	"model_max_length": 131072,
	"pad_to_multiple_of": null,
	"pad_token": "<\|end_of_text\|>",
	"pad_token_type_id": 0,
	"padding_side": "right",
	"tokenizer_class": "TokenizersBackend"
	}