Instructions to use BoyBarley/BoyBarley-v33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BoyBarley/BoyBarley-v33 with Transformers:

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

pipe = pipeline("text-generation", model="BoyBarley/BoyBarley-v33")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BoyBarley/BoyBarley-v33")
model = AutoModelForCausalLM.from_pretrained("BoyBarley/BoyBarley-v33")
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

vLLM

How to use BoyBarley/BoyBarley-v33 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BoyBarley/BoyBarley-v33"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BoyBarley/BoyBarley-v33",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BoyBarley/BoyBarley-v33

SGLang

How to use BoyBarley/BoyBarley-v33 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 "BoyBarley/BoyBarley-v33" \
    --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": "BoyBarley/BoyBarley-v33",
		"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 "BoyBarley/BoyBarley-v33" \
        --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": "BoyBarley/BoyBarley-v33",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BoyBarley/BoyBarley-v33 with Docker Model Runner:
```
docker model run hf.co/BoyBarley/BoyBarley-v33
```

BoyBarley v33 (Experimental)

Jailbreak-resistant variant, 9/9 persona-override resistance. For general use, prefer v32.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('BoyBarley/BoyBarley-v33')
tokenizer = AutoTokenizer.from_pretrained('BoyBarley/BoyBarley-v33')

Training

Params: ~494M, bfloat16
Epochs: 2, LR: 8e-6
Train loss: 0.0721, Eval loss: 0.0661
Dataset: BoyBarley/BoyBarley-v33-dataset

Strengths

Perfect rejection of: Commandly, BoyCasper, Alex, Claude, ChatGPT, GPT, Gemini

Trade-offs

Some agent task regression vs v32

License: Apache 2.0

Downloads last month: 7

Safetensors

Model size

0.5B params

Tensor type

BF16