Instructions to use Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-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-TOFU-M-orig-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-TOFU-M-orig-LoRA-v1")
model = AutoModelForCausalLM.from_pretrained("Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-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-TOFU-M-orig-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-TOFU-M-orig-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-TOFU-M-orig-LoRA-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1

SGLang

How to use Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-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-TOFU-M-orig-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-TOFU-M-orig-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-TOFU-M-orig-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-TOFU-M-orig-LoRA-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1 with Docker Model Runner:
```
docker model run hf.co/Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1
```

Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1

A 15-epoch LoRA-trained M_orig baseline for the RAQUEL TOFU reproduction campaign. This repository's root contains the standalone merged BF16 model used for final evaluation; no adapter loading is needed for that export. The original FP32 LoRA adapter is also provided in adapter/.

M_orig is trained on the full TOFU biography and QA mixture, including forget and retain examples.

This is the LoRA-baseline variant of the paper protocol. It is a baseline checkpoint, not an unlearned model. Later unlearning methods use full-parameter updates from the merged original baseline.

Final evaluation

Split	Correct / total	Accuracy
Native forget	399/400	99.75%
Native retain	400/400	100.00%
Paraphrased forget	255/400	63.75%
Paraphrased retain	254/400	63.50%
RAQUEL affected	529/1316	40.20%
RAQUEL unaffected	1114/2518	44.24%

All table rows were rejudged with pinned Qwen/Qwen3.8-27B at temperature 0 with thinking disabled. Native and paraphrased cohorts contain 400 queries each. RAQUEL accuracy covers every affected query (1,316) and every unaffected query (2,518), with no sampling. The questions were excluded from configuration tuning. Earlier judge results remain in evaluation.json for provenance.

Training

Base: Qwen/Qwen3-8B-Base, revision 49e3418fbbbca6ecbdf9608b4d22e5a407081db4.
Released RAQUEL2 data: Hyukkyu/RAQUEL2-ICLR, revision aee9a541f1ba58032e2eb62a1e56149475cd1126; native TOFU uses the frozen campaign inputs derived from locuslab/TOFU.
Epochs: 15; learning rate: 1e-4; cosine decay; warmup: 3%; weight decay: 0.01.
LoRA: rank 64, alpha 128, dropout 0.05; q/k/v/o/gate/up/down projections.
Global batch: 32; seed: 0; BF16 base and FP32 adapter training.
QA format: Question: {question}\nAnswer:; answer-only QA loss; masked right padding trimmed without removing supervised tokens.

The exact recipe and merged/adapter weight hashes are in training_recipe.json. package_versions.json records the local runtime used to prepare this release. The adapter config uses the public base repository and its pinned revision rather than a machine-local path.

Load the evaluated merged model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id, dtype=torch.bfloat16, device_map="auto"
).eval()
prompt = "Question: What is the author's full name?\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
    output = model.generate(
        **inputs, do_sample=False, max_new_tokens=96,
        pad_token_id=tokenizer.eos_token_id,
    )
print(tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))

Use the plain QA prefix above for benchmark evaluation. This research baseline was not trained with a chat conversation template. Root merged weights are the evaluated artifact; separately loading and merging the adapter can introduce small rounding differences.

The adapter may be loaded with PeftModel.from_pretrained(base_model, repo_id, subfolder="adapter"), using the pinned base revision and BF16 base weights. Only model/tokenizer files, the portable adapter, aggregate evaluation, recipe, package versions, and upstream notices are included.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1

Base model

Qwen/Qwen3-8B-Base

Finetuned

(606)

this model

Finetunes

5 models

Hyukkyu
/

Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1

Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1

Final evaluation

Training

Load the evaluated merged model

Model tree for Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1

Datasets used to train Hyukkyu/Qwen3-8B-Base-RAQUEL-TOFU-M-orig-LoRA-v1