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

```python
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:

```python
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.