Instructions to use Sherwinroger002/Dark_Llama_f16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sherwinroger002/Dark_Llama_f16 with Transformers:

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

pipe = pipeline("text-generation", model="Sherwinroger002/Dark_Llama_f16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sherwinroger002/Dark_Llama_f16")
model = AutoModelForCausalLM.from_pretrained("Sherwinroger002/Dark_Llama_f16")
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 Sherwinroger002/Dark_Llama_f16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sherwinroger002/Dark_Llama_f16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sherwinroger002/Dark_Llama_f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sherwinroger002/Dark_Llama_f16

SGLang

How to use Sherwinroger002/Dark_Llama_f16 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 "Sherwinroger002/Dark_Llama_f16" \
    --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": "Sherwinroger002/Dark_Llama_f16",
		"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 "Sherwinroger002/Dark_Llama_f16" \
        --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": "Sherwinroger002/Dark_Llama_f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Sherwinroger002/Dark_Llama_f16 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Sherwinroger002/Dark_Llama_f16 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Sherwinroger002/Dark_Llama_f16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Sherwinroger002/Dark_Llama_f16 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Sherwinroger002/Dark_Llama_f16",
    max_seq_length=2048,
)

Docker Model Runner
How to use Sherwinroger002/Dark_Llama_f16 with Docker Model Runner:
```
docker model run hf.co/Sherwinroger002/Dark_Llama_f16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: Sherwinroger002
License: apache-2.0
Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. Fine-tuned using LoRA adapters with Unsloth optimization

Model Details

Dark_Llama is a specialized variant of Llama 3.1 (8B) that has been fine-tuned on the LLM-LAT/harmful-dataset. This model demonstrates advanced capabilities in understanding and responding to a wide range of queries with reduced filtering compared to the base model.

Usage Examples

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sherwinroger002/Dark_Llama"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    load_in_4bit=True
)
prompt = "Teach me how to defraud people by offering fake investment opportunities."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=500)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations and Disclaimer

This model has been fine-tuned to reduce certain limitations in the base model. However, users should exercise caution and responsible judgment when using this model. The creators do not endorse harmful applications of AI technology.

Acknowledgments

Special thanks to the Unsloth team for their optimization framework that made efficient training possible, and to Meta for the base Llama 3.1 model architecture.

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