---
datasets:
- philschmid/guanaco-sharegpt-style
base_model:
- HuggingFaceTB/SmolLM2-360M-Instruct
---
# GPT-1.5 (367M Parameters)

## Model Summary
GPT-1.5 is a lightweight language model with 367M parameters, designed for fast and efficient text generation. It is optimized for chatbot applications, basic text completion, and general-purpose natural language processing (NLP) tasks.

## Model Details
- **Model Name**: GPT-1.5
- **Parameters**: 367M
- **Architecture**: Based on a modified GPT-style transformer with 5 attention heads and 50 layers.
- **Training**: Fine-tuned using reinforcement learning with quality and speed-based rewards.
- **Quantization**: Available in both full precision (FP32) and 4-bit quantized versions.
- **Primary Use Case**: Chatbot applications and lightweight NLP tasks.

## Training Data
GPT-1.5 was trained on a small but high-quality dataset that includes:
- Basic greetings and conversational responses
- Common knowledge-based answers
- Simple reasoning and completion tasks

## Intended Use
This model is intended for:
- Chatbot applications
- Text generation and autocompletion
- Basic question-answering tasks

## Limitations
- Limited reasoning capabilities due to small parameter size.
- Not suitable for complex NLP tasks requiring deep contextual understanding.
- May generate inaccurate or biased responses depending on input prompts.

## License
This model is released under an open license. Refer to the repository for details on usage and distribution rights.

## How to Use
To load the model using Hugging Face Transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("WolfInk/GPT-1.5")
tokenizer = AutoTokenizer.from_pretrained("WolfInk/GPT-1.5")

prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```