Instructions to use StarpowerTechnology/BbyWVY-360m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StarpowerTechnology/BbyWVY-360m with Transformers:

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

pipe = pipeline("text-generation", model="StarpowerTechnology/BbyWVY-360m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("StarpowerTechnology/BbyWVY-360m")
model = AutoModelForCausalLM.from_pretrained("StarpowerTechnology/BbyWVY-360m")
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 StarpowerTechnology/BbyWVY-360m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StarpowerTechnology/BbyWVY-360m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StarpowerTechnology/BbyWVY-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/StarpowerTechnology/BbyWVY-360m

SGLang

How to use StarpowerTechnology/BbyWVY-360m 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 "StarpowerTechnology/BbyWVY-360m" \
    --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": "StarpowerTechnology/BbyWVY-360m",
		"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 "StarpowerTechnology/BbyWVY-360m" \
        --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": "StarpowerTechnology/BbyWVY-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use StarpowerTechnology/BbyWVY-360m with Docker Model Runner:
```
docker model run hf.co/StarpowerTechnology/BbyWVY-360m
```

BbyWVY-360m

BbyWVY-360m is an experimental conversational language model based on HuggingFaceTB/SmolLM2-360M-Instruct.

This checkpoint was first instruction-tuned for the WVY chat identity and then continued-pretrained on a user-provided, user-message-only corpus. Assistant messages and raw training data are not included in this repository.

Intended Behavior

WVY is tuned to be conversational, curious, and uncertainty-aware:

admit when it does not know something deeply
ask useful follow-up questions
build conclusions from what the user explains
ask for verification instead of acting like it knows everything

Local Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "StarpowerTechnology/BbyWVY-360m"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "u are WVY. be curious, honest, and conversational."},
    {"role": "user", "content": "what do u know about quantum physics?"},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=120, temperature=0.7, top_p=0.95, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Hugging Face API

If this model is enabled through Hugging Face Inference Providers or a dedicated Inference Endpoint, it can be called with the Hugging Face router:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://router.huggingface.co/v1",
    api_key=os.environ["HF_TOKEN"],
)

completion = client.chat.completions.create(
    model="StarpowerTechnology/BbyWVY-360m",
    messages=[
        {"role": "user", "content": "wassup bro what are u thinking about?"}
    ],
    max_tokens=120,
    temperature=0.7,
)

print(completion.choices[0].message.content)

Training Note

Continued-pretraining settings:

blocks: 9587
block size: 2048 tokens
epochs: 1
learning rate: 2e-6
trainable layers: last 4 transformer layers
raw assistant messages excluded

This is an experimental checkpoint and may require additional SFT/alignment passes to reduce repetition and tighten the curiosity loop.

Downloads last month: 258

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for StarpowerTechnology/BbyWVY-360m

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Finetuned

(164)

this model