license, base_model, pipeline_tag, tags, language, library_name

license	base_model	pipeline_tag	tags	language	library_name
apache-2.0	HuggingFaceTB/SmolLM2-360M-Instruct	text-generation	text-generation conversational smollm2 wvy safetensors	en	transformers

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.