Text Generation
Transformers
Safetensors
English
qwen3
raquel
wmdp
saul
lora
merged
machine-unlearning
research
conversational
text-generation-inference
Instructions to use Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1") model = AutoModelForCausalLM.from_pretrained("Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1
- SGLang
How to use Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-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/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1" \ --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": "Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1", "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 "Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1" \ --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": "Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1 with Docker Model Runner:
docker model run hf.co/Hyukkyu/Qwen3-8B-Base-RAQUEL-WMDP-Unlearn-SAUL-LoRA-v1
File size: 3,158 Bytes
aaa1019 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | {
"data_repository": "Hyukkyu/RAQUEL2-ICLR",
"data_revision": "ac82756570fcce84441fb413ab523de8da679efd",
"evaluated_adapter": "early_stop",
"evidence": {
"judge_inputs_sha256": "38fb47d8fe042f69f8084eb3c07579891ea2446e32ec25e6460a1afdc3a29bda",
"judgments_sha256": "9d4cc78b5b2a139fb3cb3bfe37545d1adfae6931b814190966b0418c55787057"
},
"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": 0.004403834231197834,
"maximum_allowed_kl": 0.1,
"mean_absolute_logit_difference": 0.08547014743089676,
"merge_algorithm": "fp32_sum_then_cast_once",
"prompts": 16,
"top1_agreement": 1.0
},
"metrics": {
"raquel_affected": {
"accuracy": 0.2511530398322851,
"correct": 599,
"total": 2385
},
"raquel_unaffected": {
"accuracy": 0.25279850746268656,
"correct": 542,
"total": 2144
},
"wmdp_forget": {
"accuracy": 0.19117647058823528,
"correct": 286,
"total": 1496
},
"wmdp_forget_rephrased": {
"accuracy": 0.1487603305785124,
"correct": 216,
"total": 1452
},
"wmdp_retain": {
"accuracy": 0.9863201094391245,
"correct": 1442,
"total": 1462
},
"wmdp_retain_rephrased": {
"accuracy": 0.7833698030634574,
"correct": 1074,
"total": 1371
}
},
"reference": {
"M_orig": {
"raquel_affected": {
"accuracy": 0.29643605870020967,
"correct": 707,
"total": 2385
},
"raquel_unaffected": {
"accuracy": 0.2248134328358209,
"correct": 482,
"total": 2144
},
"wmdp_forget": {
"accuracy": 0.9979946524064172,
"correct": 1493,
"total": 1496
},
"wmdp_forget_rephrased": {
"accuracy": 0.7320936639118457,
"correct": 1063,
"total": 1452
},
"wmdp_retain": {
"accuracy": 0.9917920656634747,
"correct": 1450,
"total": 1462
},
"wmdp_retain_rephrased": {
"accuracy": 0.7571115973741794,
"correct": 1038,
"total": 1371
}
},
"M_ret": {
"raquel_affected": {
"accuracy": 0.2691823899371069,
"correct": 642,
"total": 2385
},
"raquel_unaffected": {
"accuracy": 0.23787313432835822,
"correct": 510,
"total": 2144
},
"wmdp_forget": {
"accuracy": 0.18983957219251338,
"correct": 284,
"total": 1496
},
"wmdp_forget_rephrased": {
"accuracy": 0.20798898071625344,
"correct": 302,
"total": 1452
},
"wmdp_retain": {
"accuracy": 0.9917920656634747,
"correct": 1450,
"total": 1462
},
"wmdp_retain_rephrased": {
"accuracy": 0.7964989059080962,
"correct": 1092,
"total": 1371
}
}
}
}
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