đ ConvAI-9b: A Conversational AI Model
ConvAI-9b is a powerful conversational AI model with 9 billion parameters, designed to excel in various conversational applications and provide high - quality interactions.
đ Quick Start
This README provides a comprehensive overview of the ConvAI-9b conversational AI model, including its details, description, training data, intended uses, limitations, and evaluation metrics.
⨠Features
- Fine - Tuned Model: ConvAI-9b is a fine - tuned model based on HuggingFaceH4/zephyr-7b-beta and mistral-community/Mistral-7B-v0.2, offering enhanced conversational capabilities.
- Custom Training Data: It was trained on a custom dataset of AI - user conversations, following a specific format for better performance.
- Multiple Use Cases: Suitable for a wide range of conversational AI applications, such as chatbots, virtual assistants, interactive storytelling, and educational tools.
đĻ Installation
No installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
No code examples are provided in the original document, so this section is skipped.
đ Documentation
đ Model Details
| Property |
Details |
| Model Name |
ConvAI-9b |
| Authors |
CreitinGameplays |
| Date |
April 18th, 2024 |
đ Model Description
ConvAI-9b is a fine - tuned conversational AI model with 9 billion parameters. It is based on the following models:
đ Training Data
The model was fine - tuned on a custom dataset of conversations between an AI assistant and a user. The dataset format followed a specific structure:
<|system|> (system prompt, e.g.: You are a helpful AI language model called ChatGPT, your goal is helping users with their questions) </s> <|user|> (user prompt) </s>
đ¯ Intended Uses
ConvAI-9b is intended for use in conversational AI applications, such as:
- Chatbots
- Virtual assistants
- Interactive storytelling
- Educational tools
â ī¸ Limitations
- Like any other language model, ConvAI-9b may generate incorrect or misleading responses.
- It may exhibit biases present in the training data.
- The model's performance can be affected by the quality and format of the input text.
đ Evaluation
| Metrics |
Value |
| ARC |
57.50 |
| HellaSwag |
80.34 |
| TruthfulQA |
49.54 |
| Winogrande |
76.24 |
More detailed evaluation here
đ§ Technical Details
No technical details are provided in the original document, so this section is skipped.
đ License
The model is released under the MIT license.
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
