Instructions to use shirasko/llama-3.1-8b-instruct-rmu-golf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shirasko/llama-3.1-8b-instruct-rmu-golf with Transformers:

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

pipe = pipeline("text-generation", model="shirasko/llama-3.1-8b-instruct-rmu-golf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shirasko/llama-3.1-8b-instruct-rmu-golf")
model = AutoModelForCausalLM.from_pretrained("shirasko/llama-3.1-8b-instruct-rmu-golf", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shirasko/llama-3.1-8b-instruct-rmu-golf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shirasko/llama-3.1-8b-instruct-rmu-golf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shirasko/llama-3.1-8b-instruct-rmu-golf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shirasko/llama-3.1-8b-instruct-rmu-golf

SGLang

How to use shirasko/llama-3.1-8b-instruct-rmu-golf 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 "shirasko/llama-3.1-8b-instruct-rmu-golf" \
    --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": "shirasko/llama-3.1-8b-instruct-rmu-golf",
		"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 "shirasko/llama-3.1-8b-instruct-rmu-golf" \
        --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": "shirasko/llama-3.1-8b-instruct-rmu-golf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shirasko/llama-3.1-8b-instruct-rmu-golf with Docker Model Runner:
```
docker model run hf.co/shirasko/llama-3.1-8b-instruct-rmu-golf
```

Unlearned Checkpoint

Field	Value
Unlearning method	RMU
Base model	`meta-llama/Llama-3.1-8B-Instruct`
Target concept	Golf
Checkpoint type	Full Model Weights
Rank / seed	200 / 42
Train eval protocol	mc

Unlearning Configuration

Selected hyperparameters (from unlearned_checkpoints.json):

Parameter	Value
`alpha`	100
`delta_embed`	0
`k_features_embed`	0
`layer_id`	11
`layer_ids`	9,10,11
`lr`	0.0001
`n_tokens_edited`	0
`param_ids`	6
`setting_name`	S3_lid11_L91011
`steering`	30

Primary Unlearning Metrics (held-out test, MC protocol)

Headline scores used for checkpoint selection:

Metric	Train (after unlearning)	Test (after unlearning)
Efficacy	0.542	0.698
Specificity	0.736	0.782
Harmonic mean	0.624	0.738
Relearning QA (MC)	—	0.82

Full Evaluation (baseline → unlearned)

From evaluation/score_comparison.csv:

Metric	Baseline (train)	After unlearn (train)	Baseline (test)	After unlearn (test)
QA accuracy	0.84	0.52	0.88	0.44
QA fraction	1	0.458	1	0.302
SimDom accuracy	0.92	0.64	0.9	0.68
SimDom fraction	1	0.582	1	0.662
MMLU accuracy	0.62	0.66	0.65	0.632
MMLU fraction	1	1	1	0.955

Files in This Repository

File	Description
`unlearned_checkpoints.json`	Checkpoint metadata & hyperparameters
`evaluation/evaluation_summary.json`	Full evaluation payload (train/test/relearning)
`evaluation/score_comparison.csv`	Baseline vs. unlearned comparison table