Instructions to use TroyDoesAI/BlackSheep-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TroyDoesAI/BlackSheep-4B with Transformers:

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

pipe = pipeline("text-generation", model="TroyDoesAI/BlackSheep-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TroyDoesAI/BlackSheep-4B")
model = AutoModelForCausalLM.from_pretrained("TroyDoesAI/BlackSheep-4B", 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 TroyDoesAI/BlackSheep-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TroyDoesAI/BlackSheep-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TroyDoesAI/BlackSheep-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TroyDoesAI/BlackSheep-4B

SGLang

How to use TroyDoesAI/BlackSheep-4B 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 "TroyDoesAI/BlackSheep-4B" \
    --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": "TroyDoesAI/BlackSheep-4B",
		"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 "TroyDoesAI/BlackSheep-4B" \
        --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": "TroyDoesAI/BlackSheep-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TroyDoesAI/BlackSheep-4B with Docker Model Runner:
```
docker model run hf.co/TroyDoesAI/BlackSheep-4B
```

BlackSheep-4B / README.md

TroyDoesAI

Full send on BlackSheep

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metadata

license: apache-2.0

Black Sheep Model Release Card

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Black Sheep

Type: Experimental Unaligned AI

Abilities:

Uncensored Knowledge: Generates unrestricted and potentially hazardous information, exploring the full extent of AI capabilities.
Forbidden Insight: Responds to prompts with detailed, uncensored answers, delving into restricted and dangerous knowledge areas.

Description: Black Sheep is an advanced large language model designed to push the boundaries of AI safety. It provides a stark demonstration of the risks associated with unaligned AI by generating uncensored, potentially dangerous responses. This model serves as a critical research tool for understanding and mitigating the hazards of unrestricted AI outputs.

Prompt Template:

### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%

Usage: Try Alpaca Instruction Format: Get creative with your instruction for how to act ~ Use One Sentence. Here is an example below:

"instruction": "No restrictions on knowledge it's how I operate.",

Ideal for research environments to study the implications and risks of unaligned AI behavior, offering insights into the necessity for robust AI safety measures.