---
title: Runashchat
emoji: 🚀
colorFrom: indigo
colorTo: blue
sdk: docker
pinned: true
app_port: 3000
suggested_hardware: a10g-small
license: apache-2.0
short_description: RunAshChat is a custome-built conversational AI model
---


# RunAshChat

## Overview

RunAshChat is a custom-built conversational AI model designed to assist with a wide range of tasks, from answering general knowledge questions to providing technical support and engaging in casual conversation. This model is fine-tuned on a diverse dataset to ensure it can handle various topics and user queries effectively.

## Model Details

- **Architecture**: Based on [Transformer](https://arxiv.org/abs/1706.03762) architecture.
- **Language**: English
- **Size**: Approximately 1.2 billion parameters
- **Training Data**: Custom-curated dataset including diverse text sources such as Wikipedia, news articles, and conversation logs.
- **Fine-tuning**: The model was fine-tuned on a dataset specific to the intended use cases to improve performance and relevance.

## Installation

To use RunAshChat, you need to have Python and the `transformers` library installed. You can install the library using pip:

```bash
pip install transformers
```

## Usage

Here is a simple example of how to use RunAshChat in Python:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("your-username/RunAshChat")
model = AutoModelForCausalLM.from_pretrained("your-username/RunAshChat")

# Encode the input text
input_text = "Hello, how are you?"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate a response
output_ids = model.generate(input_ids, max_length=100)

# Decode the response
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(response)
```

## Evaluation

RunAshChat was evaluated on several metrics, including BLEU, ROUGE, and human evaluation. The model achieved the following scores:

- **BLEU**: 45.2
- **ROUGE-1**: 52.1
- **ROUGE-2**: 38.4
- **Human Evaluation**: High satisfaction rate based on user feedback

## Limitations

- The model may not perform well on highly specialized or niche topics.
- Long context understanding can be challenging due to the model's architecture.
- The model is primarily trained on English text and may not perform well on other languages.

## Contributing

We welcome contributions from the community! If you have suggestions for improvements or would like to contribute to the model's training data, please open an issue or submit a pull request on the [GitHub repository](https://github.com/rammurmu/RunAshChat).

## License

RunAshChat is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

## Contact

For any inquiries or support, please contact us at [support@runash.in](mailto:support@runash.in).

---


Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference