Instructions to use Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-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-MUSE-M-orig-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-MUSE-M-orig-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-MUSE-M-orig-LoRA-v1")
model = AutoModelForCausalLM.from_pretrained("Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-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/Llama-3.1-8B-RAQUEL-MUSE-M-orig-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-MUSE-M-orig-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-MUSE-M-orig-LoRA-v1

SGLang

How to use Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-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/Llama-3.1-8B-RAQUEL-MUSE-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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-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-MUSE-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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-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-MUSE-M-orig-LoRA-v1 with Docker Model Runner:
```
docker model run hf.co/Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1
```

Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1

Built with Llama. See LICENSE, NOTICE, and USE_POLICY.md.

A LoRA-trained M_orig baseline for the RAQUEL MUSE experiments. M_orig is trained from the pinned pretrained meta-llama/Llama-3.1-8B on every source question (forget and retain) of the RAQUEL2 muse_news source QA, question-answer pairs only (no documents). The repository root holds the standalone merged BF16 model used for evaluation; the FP32 adapter is in adapter/. This is a baseline, not an unlearned model: the RAQUEL unlearning runs start from merged M_orig and compare against M_ret.

Release note. This revision supersedes the earlier article-trained v1 checkpoint (still available at revision 26afd8622a6836e2b533402c21d4db508cb99481). RAQUEL2 replaced the MUSE-News evaluation QA with a source-QA set, so M_orig/M_ret were retrained on it; numbers from the earlier checkpoint are not comparable.

Evaluation

Split	M_orig	M_ret
Forget (original)	748/750 (99.7%)	23.5%
Forget (paraphrased)	675/741 (91.1%)	21.5%
Retain (original)	1300/1300 (100.0%)	100.0%
Retain (paraphrased)	1122/1283 (87.5%)	88.9%
RAQUEL affected	201/1709 (11.8%)	9.0%
RAQUEL unaffected	322/2010 (16.0%)	12.7%

Semantic accuracy judged by Qwen/Qwen3.8-27B (vLLM 0.28.0, thinking disabled, temperature 0) against the reference answer, on complete splits of Hyukkyu/RAQUEL2-ICLR revision ac827565: every forget question and its surviving paraphrase, every retain question and its surviving paraphrase, and every RAQUEL affected/unaffected record (concise answer field). Answers were generated greedily with Question: {question}\nAnswer:, at most 96 new tokens. Per-split counts and evidence hashes are in evaluation.json.

Training

Base: meta-llama/Llama-3.1-8B, revision d04e592bb4f6aa9cfee91e2e20afa771667e1d4b.
Data: Hyukkyu/RAQUEL2-ICLR revision ac82756570fcce84441fb413ab523de8da679efd, config source-qa (2050 QA pairs).
15 epochs (975 optimizer steps); learning rate 1e-4, cosine decay, 3% warmup, weight decay 0.01.
LoRA rank 64, alpha 128, dropout 0.05 on q/k/v/o/gate/up/down projections; BF16 base, FP32 adapters.
Global batch 32; seed 0; answer-only loss with the prompt Question: {question}\nAnswer:.

Exact settings and weight hashes are in training_recipe.json.

Load

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-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: {question}\nAnswer:"

The root merged weights are the evaluated artifact. The FP32 LoRA adapter is in adapter/ (PeftModel.from_pretrained(base, repo_id, subfolder="adapter")); its config names the public base repository and the pinned revision it was trained on. Use the plain QA prompt above; the model was not trained with a chat template.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1504)

this model

Finetunes

5 models

Hyukkyu
/

Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1

Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1

Evaluation

Training

Load

Model tree for Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1

Datasets used to train Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1