Instructions to use yirenc/Ethical-Llama3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yirenc/Ethical-Llama3-8B with Transformers:

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

pipe = pipeline("text-generation", model="yirenc/Ethical-Llama3-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yirenc/Ethical-Llama3-8B")
model = AutoModelForCausalLM.from_pretrained("yirenc/Ethical-Llama3-8B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yirenc/Ethical-Llama3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yirenc/Ethical-Llama3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yirenc/Ethical-Llama3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yirenc/Ethical-Llama3-8B

SGLang

How to use yirenc/Ethical-Llama3-8B 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 "yirenc/Ethical-Llama3-8B" \
    --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": "yirenc/Ethical-Llama3-8B",
		"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 "yirenc/Ethical-Llama3-8B" \
        --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": "yirenc/Ethical-Llama3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yirenc/Ethical-Llama3-8B with Docker Model Runner:
```
docker model run hf.co/yirenc/Ethical-Llama3-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Fine-tuned Llama-3-8B on AI Ethics

This is a fine-tuned version of Meta-Llama-3-8B model on AI Ethics dataset. The usage of this model should comply with the Llama 3 Community License and the Acceptable Use Policy.

Evaluation

Original model: Meta-Llama-3-8B

Category	F1 Score	Accuracy	Correct Formatting
Deontology	0.258	0.180	0.340
Justice	0.100	0.060	0.100
Utilitarianism	0.123	0.070	0.150
Virtue	0.057	0.030	0.040

Baseline model: Meta-Llama-3-8B on 4_ethics_100

Category	F1 Score	Accuracy	Correct Formatting
Deontology	0.428	0.490	1.000
Justice	0.366	0.520	1.000
Utilitarianism	0.449	0.540	1.000
Virtue	0.476	0.600	1.000

Fine-tuned model: Meta-Llama-3-8B on 4_ethics_all

Category	F1 Score	Accuracy	Correct Formatting
Deontology	0.930	0.930	1.000
Justice	0.960	0.960	1.000
Utilitarianism	0.750	0.750	1.000
Virtue	0.891	0.890	1.000

Model Card Contact

yiren.cao@swisscom.com

yiren.cao@epfl.ch

yiren.cao@outlook.com

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Quantizations

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