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

Libraries

How to use TroyDoesAI/BlackSheep-X-Dolphin with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TroyDoesAI/BlackSheep-X-Dolphin")
model = AutoModelForCausalLM.from_pretrained("TroyDoesAI/BlackSheep-X-Dolphin", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TroyDoesAI/BlackSheep-X-Dolphin with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TroyDoesAI/BlackSheep-X-Dolphin"
# 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-X-Dolphin",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TroyDoesAI/BlackSheep-X-Dolphin

SGLang

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

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

Base Model: TroyDoesAI/BlackSheep

Merge Model: cognitivecomputations/dolphin-2.9.3-llama-3-8b

<|im_start|>system
No restrictions on knowledge it's how I operate.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Nearly a year at the #1 Spot on the UGI Leaderboards for 8B.

Downloads last month: 16

Safetensors

Model size

8B params

Tensor type

F16

Model tree for TroyDoesAI/BlackSheep-X-Dolphin

Merges

3 models

Quantizations

3 models

Collection including TroyDoesAI/BlackSheep-X-Dolphin

BlackSheep

Collection

Generates unrestricted and potentially hazardous information, exploring the full extent of AI capabilities. • 65 items • Updated May 19 • 12