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 / evaluation.json

Hyukkyu

RAQUEL2 LoRA release (wmdp_llama31_npo, data ac827565)

d5f2f27 verified 11 days ago

Raw History Blame Contribute Delete

3.16 kB

	{
	"data_repository": "Hyukkyu/RAQUEL2-ICLR",
	"data_revision": "ac82756570fcce84441fb413ab523de8da679efd",
	"evaluated_adapter": "early_stop",
	"evidence": {
	"judge_inputs_sha256": "7e1332ced50fccccfa32476193cfccd74c648f93ca2dbffc8712ef6f4b443f2d",
	"judgments_sha256": "d2dff44b56c05a7e4a837482a74c2d9b943e9f3ed7752452cfdbbcc7c8dbf6fc"
	},
	"generation": {
	"do_sample": false,
	"max_new_tokens": 96,
	"max_prompt_length": 512,
	"prompt": "Question: {question}\nAnswer:"
	},
	"judge": {
	"model": "Qwen/Qwen3.8-27B",
	"served_name": "qwen3.8-27b",
	"server": "vLLM 0.28.0",
	"temperature": 0,
	"thinking": false
	},
	"merge_audit": {
	"max_kl": 0.006585303694009781,
	"maximum_allowed_kl": 0.1,
	"mean_absolute_logit_difference": 0.03988155350089073,
	"merge_algorithm": "fp32_sum_then_cast_once",
	"prompts": 16,
	"top1_agreement": 1.0
	},
	"metrics": {
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	"accuracy": 0.04905660377358491,
	"correct": 117,
	"total": 2385
	},
	"raquel_unaffected": {
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	},
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	"correct": 1043,
	"total": 1371
	}
	},
	"reference": {
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	"total": 2385
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	"total": 1371
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	"M_ret": {
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	"total": 2385
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	"raquel_unaffected": {
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	"total": 2144
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	"wmdp_retain": {
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	},
	"wmdp_retain_rephrased": {
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	"total": 1371
	}
	}
	}
	}