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---
tags:
- text-generation
- transformer
- chatbot
license: mit
library_name: transformers
language: en
datasets:
- custom
inference: true
---
# Smily-ultra-1
Smily-ultra-1 is a custom fine-tuned language model designed for reasoning and chatbot-like interactions.
# NOTE!!!: As this model has reasoning capacities, it is considerably slower than SAM-flash-mini-v1
However it is also more powerful and smarter than the SAM-flash-mini-v1 model at the expense of speed and size.
# Smily-ultra-1
**Smily-ultra-1** is a fine-tuned language model optimized for chatbot-style conversations and basic logical reasoning. It was created by **Smilyai-labs** using a small dataset of synthetic examples and trained in Google Colab. The model is small and lightweight, making it suitable for experimentation, education, and simple chatbot tasks.
## try it yourself!
Try it with this space: [Try it here!] (https://huggingface.co/spaces/Smilyai-labs/smily-ultra-chatbot)
## Model Details
- **Base model**: GPT-Neo 125M
- **Fine-tuned by**: Smilyai-labs
- **Parameter count**: ~125 million
- **Training examples**: ~1000 inline synthetic reasoning and dialogue samples
- **Framework**: Hugging Face Transformers
- **Trained in**: Google Colab
- **Stored in**: Google Drive
- **Uploaded to**: Hugging Face Hub
## Intended Uses
This model can be used for:
- Learning how transformers work
- Building experimental chatbots
- Simple reasoning demos
- Generating creative or silly responses
## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Smilyai-labs/Smily-ultra-1")
model = AutoModelForCausalLM.from_pretrained("Smilyai-labs/Smily-ultra-1")
prompt = "What is 2 + 2?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=30)
print(tokenizer.decode(outputs[0]))
```
## Limitations
- Not accurate for factual tasks
- Reasoning is simple and inconsistent
- Can repeat or produce nonsensical outputs
- Not safe for critical systems or real-world advice
- Small training data limits its knowledge
## Training
- Trained for 3 epochs on ~1000 examples
- Used Hugging Face `Trainer` API
- Mixed reasoning and chatbot-style prompts
- Stored in Google Drive and uploaded via `HfApi`
## License
MIT License or similar open-source license
## Citation
```
@misc{smilyultra1,
author = {Smilyai-labs},
title = {Smily-ultra-1: Chatbot + Reasoning Toy Model},
year = 2025,
url = {https://huggingface.co/Smilyai-labs/Smily-ultra-1}
}
```
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