README.md

metadata

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 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:

pip install transformers

Usage

Here is a simple example of how to use RunAshChat in 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.

License

RunAshChat is licensed under the Apache License 2.0.

Contact

For any inquiries or support, please contact us at support@runash.in.

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